L.J. Spreeuwers

Evaluation of edge detectors using average risk

A new method for evaluation of edge detectors, based on the average risk of a decision, is discussed. The average risk is a performance measure well-known in Bayesian decision theory. Since edge detection can be regarded as a compound decision making process, the performance of an edge detector is context dependent. Therefore, the application of average risk to edge detection is non-trivial. The paper describes a method to estimate the probabilities on a number of different types of (context dependent) errors. A weighted sum of these estimated probabilities represents the average risk. The weight coefficients define the cost function. The method is suitable, not only for the comparison of edge operators, but also for the determination of the weaknesses and strengths of a certain edge operator. This is demonstrated with some well-known edge operators.<<ETX>>

Numerical optimisation in spot detector design

Spots are image details resulting from objects, the projections of which are so small that the inner structure of these objects cannot be resolved from their image. Spot detectors are image operators aiming at the detection and localisation of spots in the image. Most spot detectors can be tuned with parameters. This paper addresses the problem of how to select the parameters. We propose to use carefully designed test images, a performance measure, and numerical optimisation techniques to solve this problem. Several optimisation methods are compared, and their adequacy for spot detector design is tested.

Particle smoothing for solving ambiquity problems in one-shot structured light systems

One-shot structured light systems for 3D depth reconstruction often use a periodic illumination pattern. Finding corresponding points in the image and projector plane, needed for a triangulation, boils down to phase estimation. The 2πN ambiguities in the phase cause ambiguities in the reconstructed depth. This paper solves these ambiguities by constraining the solution space to scenes that only contain objects with flat surfaces, i.e. polyhedrons. We develop a new particle filter that estimates the depth and solves the ambiguity problem. A state model is proposed for piecewise continuous signals. This state model is worked out to find the optimal proposal density of the particle filter. The approach is validated with a demonstration.