#
Past HCI Colloquia

26.02.2013 17:15

**From Particle Stereo to Scene Stereo**

Carsten Rother, Microsoft Cambridge

*Abstract*.

In this talk I will present two lines of research which are both
applied to the problem of stereo matching. The first line of research
tries to make progress on the very traditional problem of stereo
matching. In BMVC 11 we presented the PatchmatchStereo work which achieves surprisingly good results with a simple energy function consisting of unary terms only. As optimization
engine we used the PatchMatch method, which was designed for image
editing purposes. In BMVC 12 we extended this work by adding to the
energy function the standard pairwise smoothness terms. The main
contribution of this work is the optimization technique, which we call
PatchMatch-BeliefPropagation (PMBP). It is a special case of
max-product Particle Belief Propagation, with a new sampling schema
motivated by Patchmatch. The method may be suitable for many energy
minimization problems in computer vision, which have a non-convex,
continuous and potentially high-dimensional label space.
The second line of research combines the problem of stereo matching
with the problem of object extracting in the scene. We show that both
tasks can be solved jointly and boost the performance of each individual task.
In particular, stereo matching improves since objects have to obey
physical properties, e.g. they are not allowed to fly in the air.
Object extracting improves, as expected, since we have additional
information about depth in the scene.

17.07.2012 17:15

**Machines Reading the Data**

Kristian Kersting, Fraunhofer IAIS, Bonn, Germany

*Abstract*.

The time is ripe for the AI community to set its sights on machines reading
data, the combination of information and feature extraction,

learning, and reasoning to draw conclusions about implicitly given knowledge.
This "understanding of data" --- the formation of a coherent setof
beliefs based on data and a declarative background theory --- is a long-standing
goal of AI since it holds the promise of revolutionizing web search, robotics,
computational sustainability

and other fields. Although much has been achieved already, yet much remains
to be done if we are to reach this grand goal. This talk will

examine some of what we have recently understood, as a means of identifying
what might be understood next.

Consider e.g. a subject setting a table, and the height of the subject's right
hand is tracked over time. Can machines understand the process

of setting the table? That is, can we make use of declarative world knowledge
within continuous, non-linear regression tasks? I will

demonstrate that this is indeed the case if we gate a non-parametric Bayesian
regression model for the hand's height with a relational

world model encoding our knowledge about the locations of objects and rules
describing actions to set the table. So, reading machines

handle the complexity and uncertainty of the real world using probabilistic
relational models. Ideally, probabilistic inference

within such models should be lifted as in first-order logic, handling whole
sets of indistinguishable objects together. On

several important AI tasks such as probabilistic inference, SAT, and linear
programming, I will illustrate how to lift corresponding

solvers and that significant efficiency gains are obtainable, often by orders
of magnitude. Both showcases together put

a 'Big Picture' view on AI in reach that should be understood next, namely "Statistical
Relational AI".

The talk is mainly based on joint works with Babak Ahmadi, Martin Mladenov,
Sriraam Natarajan, Marion Neumann,

Scott Sanner, and Martin Schiegg.

M. Schiegg, M. Neumann, K. Kersting.

Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression
Data.

In Proceedings of the 15th International Conference on Artificial Intelligence

and Statistics (AISTATS 2012), La Palma, Canary Islands, Spain, April 21-23,

2012. Volume 22 of JMLR: W&CP 22.

M. Mladenov, B. Ahmadi, K. Kersting.

Lifted Linear Programming.

In Proceedings of the 15th International Conference on Artificial Intelligence

and Statistics (AISTATS 2012), La Palma, Canary Islands, Spain, April 21-23,

2012. Volume 22 of JMLR: W&CP 22.

K. Kersting, B. Ahmadi, S. Natarajan.

Counting Belief Propagation.

In Proceedings of the 25th Conference on Uncertainty in Artificial

Intelligence (UAI 2009), Montreal, Canada, June 18-21 2009.

02.07.2012

**Hardware Acceleration and Image Processing - Architectures and Design
Methods**

Prof. Dr.-Ing. Sven Simon, Institut für Parallele und Verteilte Systeme, Universität Stuttgart

*Abstract*

This presentation consists of two parts. In the first, part hardware acceleration based on field programmable gate arrays (FPGAs) is presented. It is shown that architecture friendly algorithms can outperform CPUs with GHz-clock-rates by one or two orders of magnitude in the image processing domain. Basic architectural considerations, case studies and design methods for field programmable gate arrays are discussed.

In the second part, a design method for field programmable gate arrays on the physical board level is presented. The method is based on image processing of 3D Computed Tomography (CT) data to determine electrical parameters of high-speed interconnects. X-ray inspection has been used for printed circuit boards (PCB) for many years. As X-ray CT-technology has been developed further over the years, it has become possible to generate accurate geometric 3D-models from CT data even comprising of manufacturing tolerances. It is shown that from these geometric 3D-models of passive structures an electrical characterization in the GHz range can be accomplished by applying standard EM field solvers. Compared to electrical measurements this method has several advantages like the contactless electrical characterization.

29.05.2012

V.Franc (presenter) , A.Zien, B.Schoelkopf

**Support Vector Machines as Probabilistic Models**

*Abstract:*

We show how the SVM can be viewed as a maximum likelihood estimate of

a class of probabilistic models. This model class can be viewed as a

reparametrization of the SVM in a similar vein to the $\nu$-SVM

reparametrizing the classical ($C$-)SVM.

It is not discriminative, but has a non-uniform marginal. We illustrate

the benefits of this new view by re-deriving and re-investigating two

established SVM-related algorithms.

08.05.2012

Oliver Zendel, Austria Institute of Technology

**Coverage-oriented test data generation for preparing the certification**

of CV-algorithms

of CV-algorithms

*Abstract:*

Computer vision applications are steadily increasing over the last years

but their use in critical situations is still limited due to the absence

of suitable certification procedures. At the Austrian Institute of

Technology (AIT) we are currently working on filling this important gap.

Essential for the certification of algorithms are meaningful test data

sets. It is our goal to generate thorough test data sets automatically

from meta-model descriptions. On the one hand, the test data set should

cover relevant scene elements as well as a broad range of difficult

image effects. On the other hand it should not introduce rendering

artifacts into the test data that will result in tests to produce

misleading results because of insufficient realism.

My talk will give some insight into our ongoing research activities as

well as outlooks into the future.

24.04.2012 17:00

Harlyn Baker, HP, Paolo Alto

**Multi-imager camera arrays for panoramic, multi-viewpoint, and 3D capture**

*Abstract:*

Advances in building high-performance camera arrays have opened the opportunity
– and challenge – of using these devices for synchronized 3D and
multi-viewpoint capture. In this vein, I will discuss a high-bandwidth multi-imager
camera system supporting 72 wide-VGA imagers in simultaneous synchronized 30
and 60 Hz operation uncompressed to a single PC. A 6-imager 1080P60 system is
in house, preparing for upgrade from PCI-X to PCIe, where multiple dozens are
expected to be supported in sustained video delivery. Such a source of massive
synchronized video capture presents new opportunities in imaging, including
geometry recovery, immersive experiences, entertainment, surveillance, autonomous
navigation, and others. A requirement of using camera arrays for quantitative
work is that their relative poses be known, so calibration of these sensing
elements is a prerequisite of their use. I argue for use of structured arrays
– where imagers' intrinsics and relative positions do not change, and
it is feasible to perform this calibration once, before any use. I will present
progress in developing a variety of calibration approaches that capitalize on
high quality homographies (non metric) and related camera placement constraints
in developing globally optimal solutions, including rectifying homographies,
fundamental matrices, epipoles, and epipolar rectification parameters for the
entire system. The methods build on what we identify as the Rank-One-Perturbation-of-Identity
(ROPI) structure of homologies in posing a unified SVD estimator for the parameters.
I will summarize the theory, and present both qualitative depictions and quantitative
assessments of our results.

Our use of these camera systems include composing panoramic mosaics for videoconferencing
and sports capture, linear baseline multiview capture for automultiscopic display,
and geometry recovery using Epipolar Plane structurings. I hope to bring a multi-view
camera system with me and demonstrate its capabilities on a laptop.

Much of this work has been done in collaboration with Zeyu Li, studying at UC
Berkeley under Ruzena Bajcsy.

08.03.2012 14:15

**"End-to-end" machine learning of image segmentation
(for neural circuit reconstruction)**

Srini Turaga, Gatsby Computational Neuroscience Unit, UCL

*Abstract:*

Supervised machine learning is a powerful tool for creating image segmentation algorithms that are well adapted to our datasets. Such algorithms have three basic components: 1) a parametrized function for producing segmentations from images, 2) an objective function that quantifies the performance of a segmentation algorithm relative to ground truth, and 3) a means of searching the parameter space of the segmentation algorithms for an optimum of the objective function.

In this talk, I will present new work in each of these areas: 1) a segmentation algorithm based on convolutional networks as boundary detectors, 2) the Rand index as a measure of segmentation quality, and 3) the MALIS algorithm for training boundary detectors to optimize the Rand index segmentation measure. Taken together, these three pieces constitute the first system for truly "end-to-end" learning of image segmentation, where all parameters in the algorithm are adjusted to directly minimize segmentation error.

07.12.2011 15:00

**Multi-People Tracking through Global Optimization **

Pascal Fua, EPFL, Lausanne, Switzerland

*Abstract:*

Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively detect and track people, even when the only available data comes from the binary output of a simple blob detector and the number of present individuals is a priori unknown.

We start from occupancy probability estimates in a top view and rely on a generative model to yield probability images to be compared with the actual input images. We then refine the estimates so that the probability images match the binary input images as well as possible. Finally, having performed this computation independently at each time step, we compute trajectories over tive by solving a convex constrained flow problem, which allows us accurately follow individuals across thousands of frames. Our algorithm yields metrically accurate trajectories for each one of them, in spite of very significant occlusions.

In short, we combine a mathematically well-founded generative model that works in each frame individually with a simple approach to global optimization. This yields excellent performance using very simple models that could be further improved.

28.10.2011 15:00

**An Iteratively Reweighted Algorithm for Image Reconstruction**

Prof. Ming-Jun Lai, Dept. Mathematics, University of Georgia, U.S.A

*Abstract:*

In this talk, I will discuss how to recover a low-rank matrix from a small number
of its linear measurements, e.g.,

a subset of its entries. Such a problem share many common features with the
recent study of recovering sparse

vectors. I will extend an iteratively reweighted algorithm from recovering sparse
vectors to recovering low-rank

matrices, e.g. image reconstruction from its partial pixel values.

Mainly I will present a convergence analysis of an unconstrained $\ell_q$ minimization
algorithm

to compute the sparse solution and extend the analysis for matrix completion
problem.

Finally, I shall present some numerical results for recovering images from their
ramdon sampling

entries with noises.

18.10.2011 17:15

**Fusion komplementärer Sensoriken zur Nahbereichsumfelderfassung
als Basis für Fahrassistenzsysteme im Niedriggeschwindigkeitsbereich**

Leo Vepa, Robert Bosch GmbH, Leonberg

*Kurzfassung*:

Neuartige Fahrassistenzsysteme für Park- und Rangierfunktionen benötigen präzise Informationen über das Fahrzeugumfeld. Für die Planung, Kontrolle und Durchführung von solchen Manövern im Niedriggeschwindigkeitsbereich ist eine Modellierung der gesamten unmittelbaren Fahrzeugumgebung notwendig. Problematisch ist hierbei der große Winkelbereich, der von den Umfeldsensoren des Fahrzeuges abgedeckt werden muss. Hierfür eigenen sich besonders serienerprobte Sensoren mit großen Öffnungswinkeln, wie z.B. Kamera-, Ultraschall- und Radarsensoren.

Um die Leistungsfähigkeit der Umfelderfassung zu steigern wird eine Fusion
der Messdaten der verschiedenen Umfeldsensoren angestrebt. Aufgrund der unterschiedlichen
physikalischen Messprinzipien der Sensoren und der zeitlich steigenden Zahl
der Messungen ist es möglich durch eine Informationsfusion Messunsicherheiten
zu minimieren und präzisere Daten zu akquirieren. Somit ist es möglich
eine genauere und vollständigere Beschreibung des Fahrzeugumfeldes zu erhalten.
Als Basis für eine Datenfusion wird eine Fusionsarchitektur benötig,
welche sicherstellt, dass die unterschiedlichen Sensordaten korrekt in das übergeordnete
Umfeldmodell integriert werden. Dieses Umfeldmodell kann anschließend
als Datenbasis für unterschiedliche Fahrassistenzfunktionen verwendet werden.

11.10.2011 17:15

**Global optimale und lokale nichtlineare Verfahren zur szenenbasierten
Fixed-Pattern-Noise Korrektur**

Marc Geese, Robert Bosch GmbH, Leonberg

*Kurzfassung*:

Bei der Herstellung von Bildsensoren treten produktionsbedingt räumliche
Inhomogenitäten in der Sensorcharakteristik auf. Diese Inhomogenitäten
erzeugen ein sogenanntes Fixed-Pattern-Noise (FPN), welches die Bildqualität
insbesondere bei CMOS-Bildsensoren verschlechtert. Die meisten Bildsensoren
lassen ich

durch ein lineares Sensormodell beschreiben wie es z.B. im EMVA1288 Standard
beschrieben ist. In diesem linearen Fall besteht das FPN aus den Komponenten
DSNU (dark signal non-uniformity) und PRNU (photo response non-uniformity).

Im Allgemeinen sind die FPN Komponenten thermisch und zeitlich nur meta-stabil,
was einen Wartungsaufwand erzeugt und Nachkalibrierungen erforderlich macht.
Daher existieren neben Anstrengungen in verbesserter Pixelhardware und einer
labor-photometrischen Kalibrierung auch zunehmend szenenbasierte Verfahren zur
Kalibrierung der FPN-Parameter. Eine Nachkalibrierung der Kamera während
langjähriger Einsätze oder bei starken Temperaturschwankungen ist
somit möglich (z.B. für Fahrerassistenzsysteme im automotive Bereich).

In dem Vortrag werden neue global optimale sowie neue lokale Methoden zur Schätzung
der FPN Parameter vorgestellt werden und gegen literaturbekannte Methoden verglichen.
Dabei werden Annahmen an das physikalische Lichtsignal gemacht, welche dann
in Methoden umgesetzt werden denen das Sensormodell des

EMVA1288 Standards zugrunde liegt. Die so geschätzten FPN Parameter werden
gegen die photometrische Laborkalibrierung gemäß EMVA1288 verglichen,
und die

Ergebnisse diskutiert.

29.09.2011 11:00

**CRFs in Action: Intrinsic Images and Decision Tree Fields**

Carsten Rother, Microsoft Research, Cambridge

*Abstract*

In this talk I will present two upcoming papers (NIPS '11 and ICCV '11),

which both utilize Conditional Random Fields (CRFs) but are otherwise

uncorrelated.

The task of recovering intrinsic images is to separate a given input

image into its material-dependent properties, known as reflectance or

albedo, and its light-dependent properties, such as shading and shadows.

We develop a new CRF model which achieves state-of the art results. The

key novel ingredient is a sparseness prior on reflectance, which encodes

the property that a scene is often composed of a few different materials.

Decision Tree Fields (DTFs) are a new model that combines and

generalizes random forests and conditional random fields (CRF). The key

idea is to have a very large number of potential functions which are all

based on non-parametric decision trees. We show that learning and

inference is still tractable for models with millions of parameters. We

demonstrate excellent performance for various tasks such as in-painting

and person detection in depth images.

21.06.2011

**Bildfusion kombinierter Stereo-, Fokus- und Spektralserien am Beispiel
des Kamera-Arrays des Fraunhofer IOSB**

Dr. Michael Heizmann, Fraunhofer IOSB Karlsruhe

*Abstract*

Zur bildbasierten Erfassung räumlicher Szeneneigenschaften gibt es zahlreiche Ansätze. Darunter befindet sich der Multi-Stereo-Ansatz, bei dem mehrere Kameras mit unterschiedlichen Positionen simultan Bilder aufnehmen und der dort vorhandene Stereo-Effekt zur räumlichen Rekonstruktion der Szene verwendet wird. Bei solchen Kamera-Arrays können darüber hinaus noch weitere Aufnahmeparameter variiert werden, so dass mehr Information über die Szene gewonnen werden kann: Werden die Fokuseinstellungen der Kameras variiert, entsteht durch den Fokuseffekt Zusatzinformation über die räumliche Gestalt der Szene, die bei der Rekonstruktion verwendet werden kann. Werden vor die (Grauwert-)Kameras Spektralfilter platziert, kann die zusätzliche spektrale Information z.B. zur Materialklassifikation genutzt werden. Allerdings weisen die entstehenden kombinierten Bildserien den Nachteil auf, dass sie mit Standard-Stereo-Algorithmen nicht mehr ausgewertet werden können. Im Vortrag werden Methoden vorgestellt, die zur Auswertung von solchen kombinierten Bildserien geeignet sind. Dabei kommt eine regionenbasierte Formulierung des stark gekoppelten Fusionsproblems zum Einsatz, das mittels Methoden der Energieminimierung gelöst wird.

03.05.2011

**Image Processing for Dynamic Contrast Enhanced Magnetic Resonance Image
Sequences**

*Abstract*

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a

diagnostic approach in which the distribution of injected contrast agent is

imaged with high temporal resolution in order to identify potentially

pathological transport properties. Major software challenges for this

technique include:

- determining tissue transport properties from intensity distributions
- reconstructing images from rapidly and incompletely sampled data, and
- removing physiological motion in order to track tissue points.

Successful approaches for the first two tasks have been demonstrated while
the

current research front is focused on the third task. Patient motion is to be

eliminated by registering each image of a DCE-MRI sequence to an appropriate

target. However, defining an image similarity measure is complicated by the

following aspects:

- Intensity changes due to motion cannot be separated easily from those

due to the distribution of contrast agent. - Higher contrast in one moment creates new structures not present in

the previous moment, meaning the edges may not match appropriately. - If segmentation is to be performed simultaneously in order to match

segments, first order regularization models are typically not suitable

because of many gradual intensity variations.

Since the force driving registration is stronger for intensity as opposed to

edge based similarity measures, the approach considered here is to adapt

intensities in local segments of the target image to better match intensities

of local segments in the transformed image. The segmentation is based upon a

higher order model which is more suitable for piecewise smooth as opposed to

piecewise constant images. The methods implemented are seen as an

approximation to a higher order Mumford-Shah registration approach, about

which continuing research will be reported. Finally, an approach for

eliminating motion will be discussed which involves to match the entire

sequence all at once to a derived sequence.

19.04.2011:17:15

**Instrumentation and Mathematical Concepts for Multimodal Optical Imaging
inSmall Animals**

Dr. Jörg Peter, German Cancer Research Center (DKFZ), Div. Biophysics& Medical Radiation Physics

*Topics:*

In vivo molecular imaging modalities

Nuclear vs. optical imaging

Reasoning for combining imaging modalities

State-of-the-art multimodal instrumentation (small animals)

SPECT-CT-OT - The first trimodal imager (conventional approach)

Microlens-based optical detector for in vivo imaging

Mathematical concepts for image formation

Tomographic realizations for multimodal applications (OI-MRI, OI-PET)

15.03.2011 14:45

**A new kind of image processing journal **

Jean-Michel Morel, Centre de Mathématiques et de Leurs Applications, Cachan, France

*Abstract*

The new journal Image Processing on Line http://www.ipol.im/

publishes image analysis algorithms. Each journal article has four parts:

a) a careful text description of the algorithm on the main web page;

b) an on line demo running the algorithm in real time;

c) a non-moderated archive of all experiments performed by users;

d) a commented code in C or C++.

According to the recent statistics of the first published articles, this

format permits a quick and strong diffusion.

The publication criterion is not the novelty, but the interest to the

scientific community of certifying and diffusing the algorithm. Each

submission is carefully evaluated to ensure "reproducible research".
The

scientific editors request referees to check whether a), b), c) and d)

fit perfectly or not. Indeed, the main goal is to publish certified

reference versions of algorithms.

It is hoped that this new format will foster experiment sharing, on line

benchmarks, collaborative projects and in general accelerate research by

providing certified algorithms.

This journal is in the starting phase, but some fifteen algorithms are

in course of publication and twenty more submitted. A publication

on line is different from --and complementary to-- a journal

publication. I'll describe briefly several on line algorithms, discuss

the technical and organization challenges of such publications, and take

all suggestions.

04.02.2011 14:15

**The Quadratic-Chi Histogram Distance Family **

Michael Werman, The Institute
of Computer Science, The Hebrew University of Jerusalem

Jerusalem 91904, Israel

*Abstract:*

We present a new histogram distance family, the Quadratic-Chi (QC).

QC members are Quadratic-Form distances with a cross-bin 2-like normalization.

The cross-bin 2-like normalization reduces the effect of large bins having

undo influence. Normalization was shown to be helpful in many cases, where the

2 histogram distance outperformed the L2 norm. We show that the new

QC members outperform state of the art distances for these tasks, while having
a

short running time. The experimental results show that both the

cross-bin property

and the normalization are important.

If there is time i will show some reults on earth mover distance

and some pretty pictures (computational photography)

27.10.2010 17:15

Filip Korc, Institut für Geodäsie und Geoinformation, Univ. of Bonn

**On Markov Random Field Estimation for 3D Segmentation of MRI Knee Data**

*Abstract*

We present an example of employing a global statistical model in the context
of 3d semantic

segmentation of magnetic resonance images of the knee. We formulate a single
model that

allows to jointly segment all classes and describe possible approaches to estimating

the model automatically from labeled data, while pointing to the key computational

challenges. We show results of an approach, where an involved learning problem
is reduced

to a simple histogram based nonparametric density estimation.

04.10.2010 4:00 pm

**Computational reconstruction of zebrafish early embryogenesis
by nonlinear PDE methods of image processing**

Prof. Karol Mikula, Department
of Mathematics, Slovak University of Technology, Bratislava, Slovakia

Abstract:

In the talk we present mathematical models and numerical

methods which lead to early embryogenesis reconstruction and

extraction of the cell lineage tree from the large-scale 4D image

sequences. Robust and efficient finite volume schemes for solving

nonlinear PDEs related to filtering, object detection and segmentation

of 3D images were designed to that goal and studied mathematically.

They were parallelized for massively parallel computer clusters and

applied to the mentioned problems in developmental biology. The

presented results were obtained in cooperation of groups at Slovak

University of Technology, Bratislava, CNRS, Paris and University of

Bologna.

27.07.2010

**LEARNING 3-D MODELS OF OBJECT STRUCTURE FROM IMAGES**

Joseph Schlecht, Computer Vision, Univ. of Heidelberg

*Abstract*:

Recognizing objects in images presents a difficult challenge attributable

to large variations in object appearance, shape, and pose. The problem
is

further compounded by ambiguity from projecting 3D objects into a 2D
image.

I will present an approach to resolve these issues by modeling object

structure with a collection of connected 3D geometric primitives and
a

separate

model for the camera. From sets of images we simultaneously learn a

generative,

statistical model for the object representation and parameters of the

imaging

system. We explore our approach in the context of microscopic images
of

biological structure and single view images of man-made objects composed
of

block-like parts, such as furniture. We express detected features from
both

domains as statistically generated by an image likelihood conditioned
on

models

for the object structure and imaging system. Our results demonstrate

that we can infer both 3D object and camera parameters simultaneously
from

images, and that doing so improves understanding of structure in images.

20.07.2010

**Benchmarking Stereo Vision and Optical Flow Algorithms**

Daniel Scharstein, Middlebury College

*Abstract:*

Stereo vision and optical flow methods attempt to measure scene depth

and motion by matching and tracking pixels across images. To evaluate

the performance of such methods, we need "ground truth" -
the true

depth or true object motion. In this talk I will describe different

techniques for creating image datasets with ground truth, including

structured lighting, laser and CT scanners, and hidden fluorescent

texture. The Middlebury datasets are now well-established benchmarks

in computer vision, and I will discuss both benefits and potential

pitfalls of such benchmarks. I will also briefly touch on how data

with ground truth can aid in developing new algorithms.

15.06.2010

**The Lazy Flipper: A Minimal Exhaustive Search Algorithm for
Graphical
Models with Higher Order Operands**

Bjoern Andres, HCI, Univ. of Heidelberg

*Abstract:*

The optimization of functions of binary variables that decompose

according to a graphical model is NP-hard in the general case. Good

bounds on the global optimum are essential in computer vision. A search

algorithm (The Lazy Flipper) is introduced that starts from an initial

assignment of zeros and ones to the variables and converges to a global

optimum. Along the way, it passes through a series of monotonously

improving local minima; some of these are guaranteed to be the best

configurations within a given and increasing Hamming distance. For a

submodular Ising model, the algorithm finds surprisingly good upper

bounds on the minimum energy within limited search depth. For a

difficult non-submodular image segmentation problem with higher order

potentials, it finds 22% lower bounds than min-sum belief propagation
in

1/50 of the runtime.

01.06.2010

**Graph-cut Based Image Segmentation with Connectivity Priors**

Sara Vicente, Dept. of Computer Science, University College London, United Kingdom

*Abstract:*

Graph cut is a popular technique for interactive image segmentation.

However, it has certain shortcomings. In particular, graph cut has problems

with segmenting thin elongated objects due to the ``shrinking bias''.

In this talk I'll describe how imposing an additional connectivity prior
can

help overcoming this problem.

We have formulated several versions of the connectivity constraint
and

showed that the corresponding optimization problems are all NP-hard.

For some of these versions we proposed two optimization algorithms:
(i) a

practical heuristic technique which we call DijkstraGC, and (ii) a slow

method based on problem decomposition which provides a lower bound on
the

problem.

Enriching MRF based models with higher-order constraints, such as

connectivity, follows a recent trend of research in image segmentation
and

computer vision.

25.05.2010

**Structured Prediction with Global Interactions: Connectivity-Constrained
Segmentation**

Dr. Sebastian Nowozin, Microsoft Research, Cambridge UK

*Abstract:*

"Structured prediction" refers to prediction functions with
an output domain

that contains dependencies, relations and constraints among multiple

variables. In the last decade, random field models (MRF, CRF) have taken
a

prominent place and are popularly applied to solve structured prediction
tasks

in computer vision.

However, in order to be computationally tractable they typically incorporate

only local interactions and do not model global properties, such as

connectedness, a potentially useful high-level prior for object segmentation.

Recently, similar high-arity potential functions of specialized type
have

received attention by the research community. In this work, we show
how a

NP-hard potential function ensuring connectedness of the output labeling
can

be approximated in the framework of recent MAP-MRF linear programming

relaxations. Using techniques from polyhedral combinatorics, we show
that an

approximation to the MAP solution of the resulting MRF can still be
found

efficiently by solving a sequence of max-flow problems.

The contribution is a part of a larger trend of deriving richer predictive

models customized to the problem structure of computer vision problems.

02.02.2010

**Verbesserung der tomographischen Rekonstruktion von
Partikelvolumina durch nichtlineare Verzerrung des Suchraums**

Sebastian Gesemann, DLR (German Aerospace Center), Goettingen

*Abstract:*

In diesem Vortrag geht es um ein Teilproblem der optischen

Messtechnik "TomoPIV" (Tomographic Particle Image Velocimetry).

TomoPIV liefert dreidimensionale Felder von Geschwindigkeitsvektoren

und ist für die Untersuchung von Strömungen interessant. Um
diese

Felder berechnen zu können, ist eine schnelle und gute

dreidimensionale Rekonstruktion der Partikelvolumina erforderlich. Im

Vortrag werden werden verschiedene Rekonstruktionsverfahren

vorgestellt und bewertet, darunter auch ein neuer, vielversprechender

Ansatz. Verschiedene Testfälle wurden für eine Bewertung der
Verfahren

simuliert.

10.11.2009

**Learning from Labeled and Unlabeled Data, Global vs. Multiscale
Approaches. **

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science

*Abstract:*

In recent years there is increasing interest in

learning from both labeled and unlabeled data

(a.k.a. semi-supervised learning, or SSL).

The key assumption in SSL, under which an abundance

of unlabeled data may help, is that there is some

relation between the unknown response function

to be learned and the marginal density of the

predictor variables.

In the first part of this talk I'll present a statistical analysis

of two popular graph based SSL algorithms: Laplacian

regularization method and Laplacian eigenmaps.

In the second part I'll present a novel multiscale approach

for SSL as well as supporting theory. Some

intimate connections to harmonic analysis on abstract

data sets will be discussed.

Joint work with Nati Srebro (TTI), Xueyuan Zhou (Chicago),

Matan Gavish (WIS/Stanford) and Ronald Coifman (Yale).

27.10.2009

**Interactive Learning and Segmentation Tool Kit - Development
Snapshot**

*Abstract:*

For biomedical and industrial applications, segmentation and

classification techniques are of high relevance. We develop a supervised

classification framework which shall enable the user to interactively

train a segmentation system without any need for custom programming.
It

is able to tackle multi-object segmentation and multi-class object

classification in settings where long-range context is not required.

Three-dimensional and spectrally resolved input will be supported. We

want to demonstrate the current development of the graphical use

interface written in Python/Qt and give an outlook on future

development, including parallelization.

13.10.2009

**3D-Internet, Intelligente Simulierte Realität und mehr:
Forschungsthemen am neuen "Intel Visual Computing Institut"
in Saarbrücken**

Prof. Philipp Slusallek,
Intel Visual Computing Institute,

Universität des Saarlandes, Saarbrücken

*Abstract*:

"Visual Computing" mit seinen enormen Leistungs- und Echtzeit- Anforderungen definiert heute weitgehend die Prozessor-Architekturen der Chip-Hersteller -- vom kleinen Smartphone bis zum Supercomputer. In diesem Kontext hat sich Intel nach langer Suche Mitte diesen Jahres entschieden, in Saarbrücken das erste "Intel Visual Computing Institut" in Zusammenarbeit mit der Universität des Saarlandes, dem Deutschen Forschungsinstitut für Künstliche Intelligenz (DFKI) und den Max-Planck-Instituten für Informatik und für Softwaresysteme einzurichten.

An dem auf Grundlagenforschung ausgerichteten Uni-Institut laufen seit kurzem die ersten Forschungsprojekte. In meinem Vortrag werde ich kurz die Struktur und Ausrichtung des neue Institut erläutern sowie einigen ausgewählte Forschungsthemen näher diskutieren.

21.07.2009

**Mapping the space of cellular phenotypes using genome-wide
RNAi,
automated image analysis and multiparametric cellular descriptors**

Wolfgang Huber, EMBL

*Abstract:*

Phenotyping of cellular model systems through high content screening

(automated microscopy and image analysis) is a powerful approach to

associate genes with biological processes. It also opens the

possibility to systematically assay genetic and chemical perturbations

and their interactions. Statistical data analysis and computing

infrastructures are necessary to address this task. I will describe
an

approach to the complete workflow of: image segmentation and feature

extraction, screen quality assessment, feature selection and distance

metric learning, data presentation and integrative biological analyses.

The methods are provided as freely available R/Bioconductor packages.

I will describe biological insights from applying this approach, and

the computational challenges of experiments ahead.

14.07.2009

**Pose invariant shape prior segmentation using continuous graph
cuts and gradient descent on Lie groups **

Prof. Anders Heyden, Malmoe University and Lund University

*Abstract:*

In this talk I will propose a novel formulation of the Chan-Vese

model for pose invariant shape prior segmentation as a continuous graph

cut problem. The model is based on the classic L2 shape dissimilarity

measure and with pose invariance under the full (Lie-) group of similarity

transforms in the plane. To overcome the common numerical problems

associated with step size control for translation, rotation and scaling
in

the discretization of the pose model, a new gradient descent procedure

for the pose estimation is introduced. This procedure is based on the

construction of a Riemannian structure on the group of transformations

and a derivation of the corresponding pose energy gradient. Numerically

this amounts to an adaptive step size selection in the discretization
of

the gradient descent equations. Together with efficient numerics for
TV-

minimization we get a fast and reliable implementation of the model.

Moreover, the theory introduced is generic and reliable enough for ap-

plication to more general segmentation- and shape models.

30.06.2009

**1. Global Segmentation and Curvature Analysis of Volumetric
Data Sets Using Trivariate B-spline Functions.
2. Coupled Non-Parametric Shape and Moment-Based Inter-Shape Pose Priors
for Multiple Basal Ganglia Structure Segmentation.
**Octavian
Soldea, PhD

Computer Science Department

Technion, Haifa

Each part will take 20-25 minutes.

**Global Segmentation and Curvature Analysis of Volumetric Data
Sets Using Trivariate B-spline Functions**

*Abstract:*

This work presents a method to globally segment volumetric images into

regions that contain convex or concave (elliptic) iso-surfaces, planar
or

cylindrical (parabolic) iso-surfaces, and volumetric regions with saddlelike

(hyperbolic) iso-surfaces, regardless of the value of the iso-surface

level. The proposed scheme relies on a novel approach to globally compute,

bound, and analyze the Gaussian and mean curvatures of an entire volumetric

data set, using a trivariate B-spline volumetric representation. This

scheme derives a new differential scalar field for a given volumetric

scalar field, which could easily be adapted to other differential properties.

Moreover, this scheme can set the basis for more precise and accurate

segmentation of data sets targeting the identification of primitive
parts.

Since the proposed scheme employs piecewise continuous functions, it
is

precise and insensitive to aliasing.

**Coupled Non-Parametric Shape and Moment-Based Inter-Shape Pose
Priors for Multiple Basal Ganglia Structure Segmentation**

*Abstract:*

This work presents a new active contour-based, statistical method for

simultaneous volumetric segmentation of multiple subcortical structures
in

the brain. In biological tissues, such as the human brain, neighboring

structures exhibit co-dependencies which can aid in segmentation,

if properly analyzed and modeled. Motivated by

this observation, we formulate the segmentation problem

as a maximum a posteriori estimation problem, in which

we incorporate statistical prior models on the shapes and

inter-shape (relative) poses of the structures of interest.

This provides a principled mechanism to bring high level

information about the shapes and the relationships of

anatomical structures into the segmentation problem. For

learning the prior densities based on training data, we

use a nonparametric multivariate kernel density estimation

framework. We combine these priors with data in a variational framework

and develop an active contour-based iterative segmentation algorithm.
We

test our method on the problem of volumetric segmentation of basal ganglia

structures in magnetic resonance (MR) images. We present

a set of 2D and 3D experiments as well as a quantitative performance

analysis. In addition, we perform a comparison

to several existent segmentation methods and demonstrate

the improvements provided by our approach in terms of

segmentation accuracy.

16.06.2009

**The GeoMap: A Unified Representation (not only) for Image Segmentation
**

Dr. rer. nat. Hans Meine, Computer Science Department, University of Hamburg

*Abstract:*

Image analysis is dealing with a wealth of algorithms; for instance,

there is a large variety of region- and boundary-based segmentation

methods, each with different strengths and weaknesses.

Unfortunately, the comparison or combination of such methods is made

difficult by the fact that virtually every approach uses its own,

customized representation.

This talk describes the "GeoMap", a unified formalism for
the

representation of segmentation states, i.e. plane partitions. The

main characteristic is that it includes both topological *and*

geometrical aspects of a segmentation result. It also comes with

corresponding modification operations which guarantee to preserve

consistency of the GeoMap.

Although it was originally developed as a means to combine the

strengths of different segmentation algorithms in a common framework,

its extension to a sub-pixel precise (polygonal) geometry has opened

it up for other applications. Therefore, in this talk we will not

only show automatic and (semi-)interactive segmentation approaches,

but also skeletonization and a new boundary reconstruction method

based on alpha-shapes.

26.05.2009

**An Invitation to Network Analysis **

Informatik & Informationswissenschaft,

Universität Konstanz

*Abstract:*

As a methodology, network analysis is currently diffusing into an

incredibly wide range of applications in the social, life, and other

sciences. In general, the objects of interest are attributed graphs

which are most often analyzed using approaches based on algorithmic
graph

theory, linear algebra, or statistics. I will discuss three exemplary

methods for indexing (vertex centrality), grouping (structural similarity)

and modelling (event networks) in the context of various applications.

21.04.2009

**Discriminative learning of max-sum structural classifiers**

International Research and Training Centre for Information

Technologies and Systems, Kiev, Ukraine

Dept. Image Processing and Recognition

*Abstract:*

The talk is devoted to learning of max-sum structural classifiers. An

output of such a classifier is a solution of the best labeling

problem, often cited also as energy minimization problem.

We briefly survey existing methods for discriminative learning of the

classifiers. Most of the methods have identical calculation scheme:

they are iterative and they require to calculate the output of the

max-sum classifier on each iteration. However such a calculation is

often a hard computational problem itself.

We will show that it is possible to learn a wide class of max-sum

classifiers without iterative calculation of their output. Moreover,

it is possible to learn without calculation of classifier's output at

all. The scheme we will propose can also be used to approximate

solution of learning problems for max-sum classifiers.

At the end we will present ideas of further research.

31.03.2009

**Extended and Constrained Diagonal Weighting Algorithm for Image
Reconstruction**

Dr. Aurelian Nicola, Prof. Dr. Constantin Popa,

Ovidius University, Constanta, Romania

*Abstract*:

In 2001 Y. Censor, D. Gordon and R. Gordon introduced a new iterative

parallel technique suitable for large and sparse unstructured systems
of linear

equations - the Diagonal Weighting algorithm (DW) - as a generalization
of

the classical Cimmino's re

ections method. It oers an approximation of the

least squares solutions of ||Ax - b|| = min! in the consistent case,
whereas

in the inconsistent one, it only approximates the least squares solutions
of a

weighted problem ||jAx - b||M = min!, where M is a symmetric and positive

denite matrix.

In our talk we present developments of the above DW algorithm in the

following two directions:

- an extension to the inconsistent case of ||jAx- b|| = min!, which
pro-

duces sequences of approximations which always converge to a least

squares solution

- a constraining strategy, for both DW algorithm and its extension

Numerical experiments are performed on two "phantom" images
generated

with the SNARK'93 software package for image reconstruction in Comput-

erized Tomography.

19.03.2009 10:00

**A Multilayered Latent Aspect Model for Multimodal Image Collections**

IMT, Lucca, Italy

*Abstract:*

Bridging the gap between the low level representation of the visual

content and the underlying high-level semantics is a major research

issue of current interest. This talk introduces a novel latent aspect

model addressing visual content understanding through a multi-level

approach that exploits a layered representation of both the visual and

semantic information. On the visual level, it is provided a

multi-resolution representation of the pictorial data, by exploiting
a

computational model of a cortical memory mechanism to pool together

local visual patches, organizing them into perceptually meaningful

intermediate structures. Such a representation is paired with a

hierarchical organization of the latent space (i.e. the semantic part
of

the model), allowing the discovery and organization of the visual topics

into a hierarchy of aspects. The proposed model is shown to effectively

address the unsupervised discovery of relevant visual classes from

pictorial collections, segmenting out the image regions containing the

discovered classes. Further, it is show how this model can be extended

to process and represent multi-modal collections comprising textual
and

visual data.

12.02.2009 15:15

**Statistical Learning Approaches for Computational Pathology**

Thomas
Fuchs,

ETH Zuerich

*Abstract:*

The histological assessment of human tissue has emerged as the key challenge for detectionand treatment of cancer. We employ ensemble learning techniques and survival statistics to automate and objectify two of the most crucial tasks in modern pathology

(i) A framework to quantify biomarkers in tissue microarrays (TMA) is
developed for biomedical research. Due to the absence of ground truth
we utilize the information gained from extensive labeling experiments
with domain experts. Based on this gold standard we assess the inter
and intra variability of pathologists and train various models for nuclei
detection, cancer classification and survival estimation.

(ii) Micrometastases are detected in sentinel lymph nodes for clinical
therapy decisions.

I will conclude with an overview of biomedical research projects in
the ETH Machine learning group.

27.01.2009

Bayesian Optimization of Magnetic Resonance Imaging Sequences

Matthias
Seeger

Max Planck Institute for Informatics

Saarland University, Saarbruecken, Germany

*Abstract*:

We show how sampling trajectories of magnetic resonance imaging sequences

can be optimized by Bayesian computations. Combining

approximate Bayesian inference and natural image statistics

with high-performance numerical computation, we propose the

first Bayesian experimental design framework for this problem of

high relevance to clinical and brain research. Our solution requires

large-scale approximate inference for dense, non-Gaussian models.

We propose a novel variational inference algorithm, which is scaled
up to

full high-resolution images through primitives of numerical mathematics
and

signal processing. Our approach is evaluated on raw data from a Siemens
3T

scanner.

Joint work with Hannes Nickisch, Rolf Pohmann, Bernhard Schoelkopf,

MPI for Biological Cybernetics, Tuebingen.

13.01.2009

**Estimating uncertainty in the presence of multiple labels.**

Dr. Nikolaos Gianniotis,

HCI,

University of Heidelberg

*Abstract*

It is usual that in a classification task, the dataset is created by

enquiring a

a group of experts, thus collecting multiple labels per data item.

However, experts are rarely unanimous on their evaluations, and

the collected labels are often conflicting. Such ambiguity in the labels,

introduces an additional source of noise in the classification task.

Furthermore, without absolute confidence in the labels it becomes

difficult to evaluate the performance of the learnt classifiers.

This talk presents some results from a preliminary search into the above

issues.

02.12.2008

**Learning with Few Examples**

Erik Rodner, Prof. Dr.-Ing. Joachim Denzler,

Friedrich-Schiller-Universität, Jena

*Abstract:*

Current machine learning approaches often need a huge number of training

examples to learn from. This requirement is contrary to the abilities
of

the human visual system, which is able to recognize many object

categories from just few views. There is a common belief that this

ability is based on generalization from previously learned similar

object categories.

The talk will present a classifier extension which explicitly

incorporates information from a set of similar classes to learn

a new category model given just few examples. The method is based on

maximum-a-posteriori estimation of decision tree parameters. Shared

knowledge is represented using a special prior distribution which

enables the regularization of the ill-posed parameter estimation.

Applications can be found in character recognition and image

categorization and will be presented in some experiments.

25.11.2008 16:30

**Boosted Projections for Classification**

Thomas Hörnlein,

Robert Bosch GmbH, Hildesheim

*Abstract:*

Numerous sophisticated Boosting algorithms can be found in the literature. Selection of appropriate weak learners has not yet received the same level of attention - though the performance and complexity of the weak learners will greatly affect performance of the overall system.

I propose to use a weak learning scheme inspired by the well known Projection Pursuit for data regression. Advantages are cheap computation, simple regularization and flexibility. The scheme is compared to state-of-the-art classification algorithms on a selection of UCI data sets and shows compatible performance.

In a second part, extensions to Boosted Projections are proposed. The extensions are mainly designed to improve performance on high-dimensional data, e.g. images or time-series. The first extension tries to find low-dimensional informative subspaces during the course of boosting training. The second extensions adds a shift-invariant feature generation layer to deal with local image deformations. Some preliminary results are shown.

28.10.2008

*Note: This colloquium will exceptionally take place in
room 041 of the BIOQUANT (Im Neuenheimer Feld 267) at 5 pm*.

**Molecular histology of cells and tissue: Label free imaging
of biomolecular distributions**

*FOM-Institute for Atomic and Molecular Physics, *

*Kruislaan 407,*

* 1098 SJ Amsterdam, The Netherlands*

*Abstract*:

Histopathology using H&E stained tissue sections is one of the established methods used to reveal molecular anomalies at the cellular and tissue level. It is commonly understood that a better fundamental understanding of the molecular basis of disease is rapidly changing health care. Diagnosis of diseases, classification of the stage of a disease, and the investigation of the efficacy of a treatment still rely on established and validated methods. Physics is contributing to this multidisciplinary research by the development of new tools for health care. This lecture will focus on one of these developments: molecular histology with high performance mass spectrometry.

Imaging mass spectrometry (IMS) is a powerful technique that enables researchers to identify and localize biological compounds directly on tissue without the need for radioactive or fluorescence labels or immunochemical reagents. It opens up a new way for molecular histological research. The advantages of using a mass spectrometer for molecular imaging in the discovery phase of any biomedical experiment are large. It eliminates the need for labeling as the molecular mass is used as an endogenous label. This leaves the biomolecules of interest functionally unmodified. In this way it removes the interference of potential fluorescent labels with the biological function. Imaging mass spectrometry also allows the detection of post-translational modifications (PTM) as these generally involve mass changes. Often it is not possible to generated antibodies that allow the (labeled) visualization of the PTM distribution directly in tissue. With a mass spectrometer it is ‘easy’ to see the location of the mass change. An additional advantage is that mass spectrometry provides multiplexed information from a surface as for each peak in the mass spectrum an image can be generated. The spatial resolution of this technique ranges from 200 nm to 100 micrometer depending on which class of molecules is imaged with which IMS technology.

Recently it has been recognized as a tool in proteomics for in situ spatial analysis of biomolecules. We developed a multidimensional molecular imaging protocol for direct high resolution tissue analysis. It has been employed for biomedical studies to identify small molecules, peptides and proteins and the positions where these disease related molecules are present. In this lecture we will illustrate the recent advances in molecular histology using a mass spectrometer as a microscope.

22.10.2008

**Local Invariant Features for 3D Image Analysis**

*Chair of Pattern Recognition and Image Processing
Institute for Computer Science
Albert-Ludwigs-University, Freiburg i.Br., Germany*

21.10.2008

Seeing the Objects Behind the Parts: Compositional Scene Understanding

*UC Berkeley, Dept. of EECS,*

*University of California, Berkeley*

*Abstract:*

The compositional nature of visual objects significantly limits their

representation complexity and renders learning of structured object

models tractable. Adopting this modeling strategy we both (i)

automatically decompose objects into a hierarchy of relevant

compositions and we (ii) learn such a compositional representation for

each category without supervision. The compositional structure supports

feature sharing already on the lowest level of small image patches.

Compositions are represented as probability distributions over their

constituent parts and the relations between them. The global shape of

objects is captured by a graphical model which combines all

compositions. Inference based on the underlying statistical model is

then employed to obtain a category level object recognition system.
The

generative nature of the presented model provides direct insights into

the learned compositional structure of objects.

Finally, the approach has been successfully extended to near real-time

analysis of videos where category-level object recognition,

segmentation, and tracking of multiple objects are jointly handled.
This

investigation shows how the key concept of compositionality can actually

be exploited for both, making learning feasible and rendering

recognition computationally tractable.

14.10.2008

**On the Role of Exponential Functions in Image Interpolation.**

*Department of Electrical Engineering, *

*Technion– Israel Institute of Technology*

* Haifa, Israel*

*Abstract:*

A reproducing-kernel Hilbert space approach to image interpolation is

introduced. In particular, the reproducing kernels of Sobolev spaces

are shown to be exponential functions. These functions, in turn, give

rise to interpolation kernels that outperform presently available

methods. Both theoretical and experimental results are presented. A

tight l_2 upper-bound on the interpolation error is then derived,

indicating that the proposed exponential functions are optimal in this

regard. Furthermore, a unified approach to image interpolation by

ideal and non-ideal sampling procedures is derived and demonstrated,

suggesting that the proposed exponential kernels may have a

significant role in image modeling as well. Our conclusion is that the

proposed Sobolev-based approach could be instrumental and a preferred

alternative in many interpolation tasks.

Last update: 06.06.2013, 14:11 |