Showing papers by "Tony Lindeberg published in 1990"

Scale-space for discrete signals

Abstract: A basic and extensive treatment of discrete aspects of the scale-space theory is presented. A genuinely discrete scale-space theory is developed and its connection to the continuous scale-space theory is explained. Special attention is given to discretization effects, which occur when results from the continuous scale-space theory are to be implemented computationally. The 1D problem is solved completely in an axiomatic manner. For the 2D problem, the author discusses how the 2D discrete scale space should be constructed. The main results are as follows: the proper way to apply the scale-space theory to discrete signals and discrete images is by discretization of the diffusion equation, not the convolution integral; the discrete scale space obtained in this way can be described by convolution with the kernel, which is the discrete analog of the Gaussian kernel, a scale-space implementation based on the sampled Gaussian kernel might lead to undesirable effects and computational problems, especially at fine levels of scale; the 1D discrete smoothing transformations can be characterized exactly and a complete catalogue is given; all finite support 1D discrete smoothing transformations arise from repeated averaging over two adjacent elements (the limit case of such an averaging process is described); and the symmetric 1D discrete smoothing kernels are nonnegative and unimodal, in both the spatial and the frequency domain. >

Scale detection and region extraction from a scale-space primal sketch

Abstract: The authors present: (1) a multi-scale representation of gray-level shape, called a scale-space primal sketch, which makes explicit both features in scale-space and the relations between features at different levels of scales; (2) a theory for extraction of significant image structure from this representation; and (3) applications to edge detection, histogram analysis and junction classification demonstrating how the proposed method can be used for guiding later stage processing. The representation gives a qualitative description of the image structure that allows for detection of stable scales and regions of interest in a solely bottom-up data-driven way. In other words, it generates coarse segmentation cues and can be hence seen as preceding further processing, which can then be properly tuned. The authors argue that once such information is available many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives perceptually intuitive results. >

Construction of a Scale-Space Primal Sketch

Abstract: We present a multi-scale representation of grey-level shape, called scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure --- stable scales and regions of interest --- in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives perceptually intuitive results.

On scale and resolution in the analysis of local image structure

Abstract: Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans.