Showing papers by "Peter Meer published in 1990"
TL;DR: A blind noise variance algorithm that recovers the variance of noise in two steps is proposed and application of the algorithm to differently sized images is also discussed.
Abstract: A blind noise variance algorithm that recovers the variance of noise in two steps is proposed. The sample variances are computed for square cells tessellating the noise image. Several tessellations are applied with the size of the cells increasing fourfold for consecutive tessellations. The four smallest sample variance values are retained for each tessellation and combined through an outlier analysis into one estimate. The different tessellations thus yield a variance estimate sequence. The value of the noise variance is determined from this variance estimate sequence. The blind noise variance algorithm is applied to 500 noisy 256*256 images. In 98% of the cases, the relative estimation error was less than 0.2 with an average error of 0.06. Application of the algorithm to differently sized images is also discussed. >
127 citations
TL;DR: A novel hierarchical approach toward fast parallel processing of chain-codable contours is presented, which makes possible fast, O(log(image/sub -/size), computation of contour representation in discrete scale-space.
Abstract: A novel hierarchical approach toward fast parallel processing of chain-codable contours is presented. The environment, called the chain pyramid, is similar to a regular nonoverlapping image pyramid structure. The artifacts of contour processing on pyramids are eliminated by a probabilistic allocation algorithm. Building of the chain pyramid is modular, and for different applications new algorithms can be incorporated. Two applications are described: smoothing of multiscale curves and gap bridging in fragmented data. The latter is also employed for the treatment of branch points in the input contours. A preprocessing module allowing the application of the chain pyramid to raw edge data is also described. The chain pyramid makes possible fast, O(log(image/sub -/size)), computation of contour representation in discrete scale-space. >
64 citations
01 Apr 1990
TL;DR: An image analysis technique in which a separate hierarchy is built over every compact object of the input, made possible by a stochastic decimation algorithm which adapts the structure of the hierarchy to the analyzed image.
Abstract: In this paper we have presented an image analysis technique in which a separate hierarchy is built over every compact object of the input. The approach is made possible by a stochastic decimation algorithm which adapts the structure of the hierarchy to the analyzed image. For labeled images the final description is unique. For gray level images the classes are defined by converging local processes and slight differences may appear. At the apex every root can recover information about the represented object in logirhtmic number of processing steps, and thus the adjacency graph can become the foundation for a reulational model of the scene.
61 citations
16 Jun 1990
TL;DR: A systematic approach to least square approximation of images and of their derivatives is presented and it is shown that if orthonormal polynomial bases are employed the filters have closed-form solutions.
Abstract: A systematic approach to least square approximation of images and of their derivatives is presented. Derivatives of any order can be obtained by convolving the image with a priori known filters. It is shown that if orthonormal polynomial bases are employed the filters have closed-form solutions. The same filter is obtained when the fitted polynomial functions have one consecutive degree. Moment-preserving properties, sparse structure for some of the filters, and the relationship to the Marr-Hildreth and Canny edge detectors are proven. >
27 citations
TL;DR: The employment of multiple roots defined on the smoothed representation of the input contributes to the robustness of the method at very low signal-to-noise ratios.
7 citations