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Eric Saund

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5 papers
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5

NeurIPS Conference 2005 Conference Paper

Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours

  • Eric Saund

This paper presents representation and logic for labeling contrast edges and ridges in visual scenes in terms of both surface occlusion (border ownership) and thinline objects. In natural scenes, thinline objects in- clude sticks and wires, while in human graphical communication thin- lines include connectors, dividers, and other abstract devices. Our analy- sis is directed at both natural and graphical domains. The basic problem is to formulate the logic of the interactions among local image events, specifically contrast edges, ridges, junctions, and alignment relations, such as to encode the natural constraints among these events in visual scenes. In a sparse heterogeneous Markov Random Field framework, we define a set of interpretation nodes and energy/potential functions among them. The minimum energy configuration found by Loopy Belief Prop- agation is shown to correspond to preferred human interpretation across a wide range of prototypical examples including important illusory con- tour figures such as the Kanizsa Triangle, as well as more difficult ex- amples. In practical terms, the approach delivers correct interpretations of inherently ambiguous hand-drawn box-and-connector diagrams at low computational cost.

NeurIPS Conference 1993 Conference Paper

Unsupervised Learning of Mixtures of Multiple Causes in Binary Data

  • Eric Saund

This paper presents a formulation for unsupervised learning of clus(cid: 173) ters reflecting multiple causal structure in binary data. Unlike the standard mixture model, a multiple cause model accounts for ob(cid: 173) served data by combining assertions from many hidden causes, each of which can pertain to varying degree to any subset of the observ(cid: 173) able dimensions. A crucial issue is the mixing-function for combin(cid: 173) ing beliefs from different cluster-centers in order to generate data reconstructions whose errors are minimized both during recognition and learning. We demonstrate a weakness inherent to the popular weighted sum followed by sigmoid squashing, and offer an alterna(cid: 173) tive form of the nonlinearity. Results are presented demonstrating the algorithm's ability successfully to discover coherent multiple causal representat. ions of noisy test data and in images of printed characters.

AAAI Conference 1986 Conference Paper

Abstraction and Representation of Continuous Variables in Connectionist Networks

  • Eric Saund

A method is presented for using connectionist networks of simple computing elements to discover a particular type of constraint in multidimensional data. Suppose that some data source provides samples consisting of n-dimensional feature-vectors, but that this data all happens to lie on an m-dimensional surface embedded in the n-dimensional feature space. Then occurrences of data can be more concisely described by specifying an m-dimensional location on the embedded surface than by reciting all n components of the feature vector. The recoding of data in such a way is a form of abstraction. This paper describes a method for performing this type of abstraction in connectionist networks of simple computing elements. We present a scheme for representing the values of continuous (scalar) variables in subsets of units. The backpropagation weight updating method for training connectionist networks is extended by the use of auxiliary pressure in order to coax hidden units into the prescribed representation for scalar-valued variables.