Abstract — We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully-automated phenotyping system. The system contains three modules (1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; (2) an Energy-Based Model which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; (3) A set of elastic models of the embryo at various stages of development that are matched to the label images. Index Terms — image segmentation, convolutional networks, nonlinear filter, energy-based model A. Automatic Phenotyping I.

Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies -- to the maximum possible degree -- a set of given constraints. This pat)er scribes some experiments performed applying it to POS tagging, and the results obtained. it also ponders the possibility of applying it, to Word Sense Disambiguation.

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. Object recognition systems involve parameters such as thresholds, bounds and weights. These parameters have to be tuned before the system can perform successfully. A common practice is to choose such parameters manually on an ad hoc basis, which is a disadvantage. This paper presents a novel theory of parameter estimation for optimization-based object recognition where the optimal solution is defined as the global minimum of an energy function. The theory is based on supervised learning from examples. Correctness and instability are established as criteria for evaluating the estimated parameters. A correct estimate enables the labeling implied in each exemplary configuration to be encoded in a unique global energy minimum. The instability is the ease with which the minimum is replaced by a non-exemplary configuration after a perturbation. The optimal estimate minimizes the instability. Algorithms are presented for computing correct and minimal-instability estimates. The theory is a...

We present a new approach to the supervised learning of lateral interactions for the competitive layer model (CLM) dynamic feature binding architecture. The method is based on consistency conditions, which were recently shown to characterize the attractor states of this linear threshold recurrent network. For a given set of training examples the learning problem is formulated as a convex quadratic optimization problem in the lateral interaction weights. An efficient dimension reduction of the learning problem can be achieved by using a linear superposition of basis interactions.

We address the problem of training relaxation labeling processes, a popular class of parallel iterative procedures widely employed in pattern recognition and computer vision. The approach discussed here is based on a theory of consistency developed by Hummel and Zucker, and contrasts with a recently introduced learning strategy which can be regarded as heteroassociative, i.e., what is actually learned is the association between patterns rather than the patterns themselves. The proposed learning model is instead autoassociative and involves making a set of training patterns consistent, in the sense rigorously defined by Hummel and Zucker; this implies that they become local attractors of the relaxation labeling dynamical system. The learning problem is formulated in terms of solving a system of linear inequalities, and a straightforward iterative algorithm is presented to accomplish this. The learning model described here allows one to view the relaxation labeling process as a kind of ...

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Abstract — We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM) dynamic feature binding model which is applicable on a wide range of perceptual grouping and segmentation problems. A predefined target segmentation can be achieved as attractor states of this linear threshold recurrent network, if the lateral weights are chosen by Hebbian learning. The weight matrix is given by the correlation matrix of special pattern vectors with a structure dependent on the target labeling. Generalization is achieved by applying vector quantization on pairwise feature relations, like proximity and similarity, defined by external knowledge. We show the successful application of the method to a number of artifical test examples and a medical image segmentation problem of fluorescence microscope cell images.

Deterministic annealing and relaxation labeling algorithms for classification and matching are presented and discussed. A new approach---self annealing---is introduced to bring deterministic annealing and relaxation labeling into accord. Self annealing results in an emergent linear schedule for winner-take-all and linear assignment problems. Self annihilation, a generalization of self annealing is capable of performing the useful function of symmetry breaking. The original relaxation labeling algorithm is then shown to arise from an approximation to either the self annealing energy function or the corresponding dynamical system. With this relationship in place, self annihilation can be introduced into the relaxation labeling framework. Experimental results on synthetic matching and labeling problems clearly demonstrate the three-way relationship between deterministic annealing, relaxation labeling and self annealing. Keywords: Deterministic annealing, relaxation labeling, self anneal...

. Deterministic annealing and relaxation labeling algorithms for classification and matching are presented and discussed. A new approach ---self annealing---is introduced to bring deterministic annealing and relaxation labeling into accord. Self annealing results in an emergent linear schedule for winner-take-all and assignment problems. Also, the relaxation labeling algorithm can be seen as an approximation to the self annealing algorithm for matching and labeling problems. 1 Introduction Labeling and matching problems abound in computer vision and pattern recognition (CVPR). It is not an exaggeration to state that some form or the other of the basic problems of template matching or data clustering has remained central to the CVPR and neural networks communities for about three decades. Due to the somewhat disparate natures of these communities, different frameworks for formulating and solving these two problems have emerged and it is not immediately obvious how to go about reconcili...