Controllable Multi-dimensional Filters and Models in Low-Level Computer Vision

Abstract: This thesis concerns robust estimation of low-level features for use in computer vision systems. The presentation consists of two parts.The first part deals with controllable filters and models. A basis filter set is introduced which supports a computationally efficient synthesis of filters in arbitrary orientations. In contrast to many earlier methods, this approach allows the use of more complex models at an early stage of the processing. A new algorithm for robust estimation of orientation is presented. The algorithm is based on synthesized quadrature responses and supports the simultaneous representation and individual averaging of multiple events. These models are then extended to include estimation and representation of more complex image primitives such as as line ends, T-junctions, crossing lines and curvature. The proposed models are based on symmetry properties in the Fourier domain as well as in the spatial plane and the feature extraction is performed by applying the original basis filters directly on the grey-level image. The basis filters and interpolation scheme are finally generalized to allow synthesis of 3-D filters. The performance of the proposed models and algorithms is demonstrated using test images of both synthetic and real world data.The second part of the thesis concerns an image feature representation adapted for a robust analogue implementation. A possible use for this approach is in analogue VLSI or corresponding analogue hardware adapted for neural networks. The methods are based on projections of quadrature filter responses and mutual inhibition of magnitude signals.