Peter Vanroose

Identification of Everyday Objects on the Basis of Silhouette and Outline Versions

Line drawings of everyday objects were modified into silhouettes by filling-in the complete area enclosed by boundary contours, and outline versions were created by extracting the contours from the silhouettes. A large number of participants was asked to try to identify these silhouette and outline versions in experiment 1. Identifiability ranged from 0% to 100% correct responses with a large range in-between. Several kinds of errors and several reasons for difficulties with identification emerged in our data set. In experiment 2, we compared the original identification rates to those of inverted silhouettes (white figures on a black background), and in experiment 3 we compared the original identification rates of objects with filled-in holes or background parts to those of versions without filling-in. These stimuli and identification norms are useful for additional research on priming and context effects of object identification, neuropsychological deficits of object identification, and all kinds of studies with silhouettes where the role of top down knowledge could be of interest.

Code construction for the noiseless binary switching multiple-access channel

The noiseless coding problem is considered for a discrete memoryless multiple-access channel that is a counterpart to the well-known binary adder channel. Upper and lower bounds on the number of code words in a uniquely decodable code pair are given, from which the zero-error capacity region of the channel is derived. This region coincides with the classical capacity region of this channel. The proof uses the notion of second-order distance of a code. For several values of n and k, good code pairs of block length n are constructed, with the first code being (n,k)-linear. Some of these are found to be optimal. Some convolutional codes are investigated that yield additional good rate pairs. >

How an Erdos-Renyi-type search approach gives an explicit code construction of rate 1 for random access with multiplicity feedback

Pippenger (1981) showed in a probabilistic way that the capacity of a collision channel with multiplicity feedback is one. In this correspondence, using an Erdos-Renyi type search strategy, we settle a long-standing open problem by giving a constructive proof of this result. Moreover, we prove that two different capacity definitions are equivalent, thereby solving a problem posed by Tsybakov (1985).

Uniquely decodable codes for deterministic relay channels

The deterministic relay channel is analyzed and explicit code constructions for all binary and all ternary/binary channels are given. An explicit set of equivalence conditions is used to make a classification of all such relay channels, for which also the capacity is evaluated. The coding problem is then reduced to finding all possible output sequences of a certain finite-state channel determined by the relay coding strategy. The channel states correspond to the possible relay memory contents. For some relay channels capacity is reached by using simple uniquely decodable codes, thus establishing the zero-error capacity of those channels with finite-memory relay strategies. For other relay channels the relay memory must be arbitrarily large to achieve zero-error rates arbitrarily close to capacity. One such code construction is given. It is not known whether there exist relay channels for which the zero-error capacity is strictly smaller than the average-error capacity. The code construction problem for the semideterministic relay channel and for the nonsynchronized relay channel is briefly considered. >

The Capacity of a Relay Channel, Both With and Without Delay

In this correspondence, we point out the incorrectness of an upper bound in van der Meulen (Advances in Applied Probability, vol. 3, no. 1, pp. 120-154, 1971) and report the correct relay capacity for one of the examples given there. We also show that, when viewed as a relay-without-delay channel, the capacity of this channel is strictly larger than its classical relay capacity.

On the construction of Cartesian authentication codes over symplectic spaces

Various constructions of authentication codes using spaces related to the general linear group have been proposed and analyzed. In the paper the authors describe two new constructions of Cartesian authentication codes using symplectic spaces. This illustrates the feasibility of codes from spaces based on geometries of the other classical groups. >

Using shape to correct for observed nonuniform color in automated egg grading

We report on algorithmic aspects for the automated visual quality control for grading of brown eggs. Using RGB color images of four different views of every egg enabled to analyze the entire eggshell. The scene was illuminated using a set of white fluorescent tubes placed in a rectangular grid. After detection and approximation of the egg contour (ellipse fitted), the color was corrected to compensate for the elliptical shape of the eggs. A second order polynomial was fitted through points taken from subsequent horizontal lines inside the egg. Iteration was used to reject outliers (most likely points with visual defects). The shape- corrected intensity was calculated as the signed difference between polynomial and measured value, increased with the average egg intensity. Based on the corrected color, dirt regions like yolk, manure, blood, and red mite spots were segmented from the egg-background. Features based on color and shapes were calculated for every segmented region as the combined space and color moments of zeroth, first and second order. A classifier identified most of the defective eggs. Elimination of false rejects due to mirror reflection of the light tubes on some eggs (segmented because of the different color) is currently under investigation.

Ordering in Sequence Spaces: An Overview

“Creating order” is maybe one of the most important human activities. In its simplest form, ordering is just “sorting”, which is a mathematically well understood problem. However, in real life we are often facing practical limitations which inhibit complete sorting. These limitations can be either knowledge (information) restrictions —we don’t know the future, we forget the past— or manipulation restrictions —we don’t want to carry objects too far—.

Non-Linear Diffusion as a Neuron-Like Paradigm for Low-Level Vision

Remote sensing puts high demands on image processing. It calls for state-of-the-art algorithms, e.g. neural networks. However, neural nets usually work on preprocessed data and the preprocessing steps themselves have proved difficult to implement with NNs. Here a NN-like paradigm for low-level image processing is presented, that is based on the evolution of coupled, non-linear diffusion equations. The illustrations are focussed on feature preserving noise reduction, but the framework is more general.