NOTICE
DATE CHANGE .
The Vision
day will be held on
Computer
Vision Day
The Interdisciplinary
Center – Herzliya
Time 
Speaker and
Collaborators 
Affiliation 
Title 

Gathering 


Daphna Weinshall, Aharon BarHillel, Tomer Hertz, Noam Shental 
HUJI 


Nir Sochen, Chen
Sagiv, Daniel Cremers, Christoph Schnoerr 
TAU 
Segmentation:
From Descartes to Kant 

Nachum Kiryati, Leah
Bar, Nir Sochen 
TAU 
Variational Pairing of Image Segmentation and Blind Restoration 

Coffee Break 


Yael Moses, Shai Avidan and Yoram Moses 
IDC 


Michal Irani and
Bernard Sarel 
Weizmann 
Separating Transparent Layers through Layer Information Exchange 

Lunch break 


Micha Lindenbaum, E.
Engbers and A. Smeulders 
Technion 


Amnon Shashua and Lior Wolf 
HUJI 
A New Paradigm for Feature Selection with some surprising results 

Ronen Basri, 
Weizmann 
Shape Representation and Classification Using the Poisson Equation 

Coffee Break 


Yoav Schechner Shree Nayar and Peter Belhumeur 
Technion 


Eero Simoncelli 
NYU 
General: It has been several years since the termination of the annual Vision meetings held at TAU.
We would like to reinstate this tradition, hopefully on an annual basis. This year IDC is happy to host the first Israeli Computer Vision Day. We hope it will be an academically fruitful and pleasant conference.
Location and Directions: The Vision Day will take place at the Interdisciplinary Center (IDC), Herzliya, in the Ivtzer Auditorium. For driving instructions see map.
A convenient option to arrive is by train, see time schedule here. Get off at the Herzliya Station,
and order a taxi ride by phone. There are two taxi stations that provide this
service: Moniyot AvYam (09 9501263 or 09 9563111), and Moniyot Pituach (09
9582288 or 09 9588001). The fair for a taxi ride from the railway station to
IDC is around 20.
Abstracts
Waveletdomain Gaussian Scale Mixture Models for Images
Eero P. Simoncelli 
I'll describe some of our recent work in modeling the joint
statistics of images when represented in
a multiscale oriented basis. Local neighborhoods of coefficients,
associated with basis functions
at nearby positions, scales and orientations, may be described as arising from
the product of a
Gaussian random vector and a hidden scalar variable. This model captures
a number of nonGaussian behaviors
that are typical of natural images, and it also proves to be quite tractable in
applications such as denoising.
Hagit HelOr, Ulrich Barnhoefer, Brian Wandell –
Images acquired by digital cameras are affected by
sensor noise and by color deviations. It can be shown that there is a tradeoff
between these two image artifacts which is dependent on the sensor
sensitivities and the ensuing transformations applied to the sensor outputs.
We study sensors associated with a special class of
cameras, namely, Colorimetric Cameras. These sensors are capable of capturing
colors which span the human visual subspace, thus are able to produce zero DE error, which in turn imply no color
deviations in the captured image. We find the optimal sensors in this set with
respect to minimizing sensor noise.
It was found that two of the three optimal sensors are
surprisingly similar to the CIEX and CIEZ color matching functions. We show
that this is not accidental. In fact, in choosing the XYZ primaries, the CIE
unknowingly chose those whose matching functions (when considered as sensors)
minimize sensor noise. We show that the criteria set by the CIE in choosing the
XYZ primaries, imply minimization of sensor noise within our camera model.
Nir Sochen, Chen Sagiv, Daniel Cremers, Christoph
Schnoer – Tel
The partition of the image domain to significant
regions is a long standing problem in computer vision. I will review several
approaches which parallel various philosophical schools. This will lead to a
formalism that put the low and high level vision on the same footing. In
particular, segmentation with shape prior(s) and dynamic labeling will be
demonstrated.
Variational
Pairing of Image Segmentation and
Segmentation and blind restoration are
both classical problems, that are
known to be difficult and have attracted major research efforts.
This paper shows that these problems are tightly coupled and can
be successfully solved together.
Multiview correspondence in a distributed setting
Yael Moses, Shai Avidan, and Yoram Moses
– The Interdisciplinary Center
We present a probabilistic algorithm for
finding correspondences across
multiple images. The algorithm runs in a distributed setting, where each camera
is attached to a separate computing unit, and the cameras communicate over a
network. No central computer is involved in the computation. The
algorithm runs with low computational and communication cost. Our
distributed algorithm assumes access to a standard pairwise widebaseline
stereo matching algorithm (WBS) and our goal is to minimize the number of
images transmitted over the network, as well as the number of times the WBS is
computed. We employ the theory of random graphs to provide an efficient
probabilistic algorithm that performs WBS on a small number of image pairs,
followed by a correspondence propagation phase. The heart of the work is
a theoretical analysis of the number of times WBS must be performed to ensure
that an overwhelming portion of the correspondence information is
extracted. The analysis is extended to show how to combat computer and
communication failures, which are expected to occur in such settings, as well
as correspondence misses. This analysis yields an efficient distributed
algorithm, but it can also be used to improve the performance of centralized
algorithms for correspondence.
Separating
Transparent Layers through Layer Information Exchange
Michal Irani and Bernard Sarel –
Weizmann Inst.
We present an approach for separating two
transparent layers in images and video sequences. Given two initial unknown
physical mixtures (I1 and I2) of real scene layers, (L1 and L2) we seek a layer
separation which minimizes the structural correlations across the two layers at
every image point. Such a separation is achieved by transferring local
structure from one image to the other wherever it is highly correlated with the
underlying local structure in the other image, and vice versa. This
bidirectional transfer operation, which we call the ``layer
information exchange", is performed on diminishing window sizes, from
global image windows (i.e., the entire image), down to local image windows,
thus detecting similar structures at varying scales across pixel positions.
We show the applicability of this approach to various realworld scenarios,
including image and video transparency separation. In particular, we show that
this approach can be used for separating transparent layers in images obtained
under different polarizations, as well as for separating complex {\em
nonrigid} transparent motions in video sequences. These can be done without
prior knowledge of the layer mixing model (simple additive, alphamated
composition with an unknown alphamap, or other), and under unknown complex
temporal changes (e.g., unknown varying lighting conditions).
An
Informationbased Measure for Grouping Quality
Micha Lindenbaum, E.
Engbers and A. Smeulders  Technion
Grouping is an essential process of computer vision. However, evaluation of
grouping results is not straightforward and is often heuristic. We
propose a method for measuring grouping quality, based on the following
observation: a better grouping result provides more information about the true,
unknown grouping.
The amount of information is evaluated by the number of queries required to
specify the true grouping. An automatic procedure, relying on the given
hypothesized grouping, generates (homogeneity) queries about the true grouping
and answers them using an oracle. The process terminates once the queries
suffice to specify the true grouping. The number of queries is a measure of the
hypothesis noninformativeness.
A related measure of informativeness is the uncertainty of the true grouping,
characterized using a probabilistic model and common information theory terms
such as surprise and entropy. This relation between the measures is established
and experimentally supported. The proposed method suggests two main innovations
and advantages relative to existing approaches:
Generality and fairness  Most previous, similaritybased
measures, involve unavoidably arbitrary choices. The proposed informationbased
quality measure is free from such arbitrary choices, treats different types of
grouping errors in a uniform way and does not favor any algorithm.
Nonheuristic justification  Unlike previous approaches, the
number of queries may be interpreted as a surprise in an information
theory context. The query count was found to be approximately monotonic
in the entropy, independent of the grouping error type, indicating that this
interpretation is valid and that the query count is an adequate unbiased means for
comparing grouping results.
Moreover, we found that the query count measure approximates human judgment
better than other methods and as such, gives better results when used to
optimize a segmentation algorithm, as demonstrated in our experiments.
A
New Paradigm for Feature Selection with some surprising results
Amnon Shashua and Lior
Wolf –
The problem of selecting a subset of
relevant features in a potentially overwhelming quantity of data is classic and
found in many branches of science. Examples in computer vision, text processing
and more recently bioinformatics are abundant. In text classification tasks,
for example, it is not uncommon to have 104 to 107 features of the size of the
vocabulary containing word frequency counts, with the expectation that only a
small fraction of them are relevant. Typical examples include the automatic
sorting of URLs into a web directory and the detection of spam email.
In this work we present a definition of
"relevancy" based on spectral properties of the Laplacian of the
features' measurement matrix. The feature selection process is then based on a
continuous ranking of the features defined by a leastsquares optimization
process. A remarkable property of the feature relevance function is that sparse
solutions for the ranking values naturally emerge as a result of a ``biased
nonnegativity'' of a key matrix in the process. As a result, a simple
leastsquares optimization process converges onto a sparse solution, i.e., a
selection of a subset of features which form a local maxima over the relevance
function. The feature selection algorithm can be embedded in both unsupervised
and supervised inference problems and empirical evidence show that the feature
selections typically achieve high accuracy even when only a small fraction of
the features are relevant.
Shape Representation and Classification Using the
Poisson Equation
Ronen Basri, Lena Gorelick, Meirav Galun, Eitan Sharon,
Achi Brandt – Weizmann Inst.
Silhouettes contain rich information about the
shape of objects that can be used for recognition and classification. We
present a novel approach that allows us to reliably compute many useful
properties of a silhouette. Our approach assigns for every internal point of
the silhouette a value reflecting the mean time required for a random walk
beginning at the point to hit the boundaries. This function can be computed by
solving Poisson's equation, with the silhouette contours providing boundary
conditions. We show how we can use this function to reliably extract various
shape properties including part structure and rough skeleton, local orientation
and aspect ratio of different parts, and convex and concave sections of the
boundaries. In addition to this we discuss properties of the solution and show
how to efficiently compute this solution using multigrid algorithms. We
demonstrate the utility of the extracted properties by using them for shape
classification and show how these properties can be incorporated in a
hierarchical segmentation scheme to enforce smooth continuation of segment
boundaries.
Codes for Multiplexing Images and Lighting
Yoav Schechner, Shree Nayar and
Peter Belhumeur  Technion
Imaging of objects under variable
lighting directions is an important and frequent practice in computer vision
and imagebased rendering. We introduce an approach that significantly improves
the quality of such images, practically at no cost.
Traditional methods for acquiring images under variable illumination directions
use only a single light source per acquired image. In contrast, our approach is
based on a multiplexing principle, in which multiple light sources illuminate
the object simultaneously from different directions. Thus, the object
irradiance is much higher. The acquired images are then computationally
demultiplexed.
We give the optimal code by which the illumination should be multiplexed to
obtain the highest quality output. We then demonstrate its utility in
experiments using high directional resolution lighting. The mathematical
principle behind this approach is useful in other domains of imaging, unrelated
to the regime of illumination.
Learning
Distance Functions for Image Retrieval by Query
Daphna Weinshall, Aharon BarHillel, Tomer Hertz, Noam
Shental –
Image retrieval critically relies on the
distance function used to compare the query image and the images in the
database. We suggest to learn such distance functions
by training binary classifiers with margins, where the classifiers are defined
over the product space of pairs of images. The classifiers are trained to
distinguish whether two points come from the same class, and their signed
margin is used as a distance function. We explored several variants of this
idea, based on using SVM and Boosting algorithms as product space classifiers.
Our main contribution is a distance learning method which combines boosting
hypotheses over the product space with a weak learner based on partitioning the
original feature space. I will show comparative results of image retrieval in a
distributed learning paradigm, using two databases: a large database of facial
images (YaleB), and a database of natural images taken from a commercial CD. In
both cases our combined boosting method outperforms all other methods, and its
generalization to unseen classes is superior.