Wimmelbild which means “teeming figure picture” is a popular genre of visual puzzles. Abundant masses of small
figures are brought together in complex arrangements to make one scene in a Wimmelbild. It is picture hunt game. We
discuss what type of computations/processes could possibly underlie the solution of the discovery of figures that are
hidden due to a distractive influence of the context. One thing for sure is that the processes are unlikely to be purely
bottom-up. One possibility is to re-arrange parts and see what happens. As this idea is linked to creativity, there are
abundant examples of unconventional part re-organization in modern art. A second possibility is to define what to look
for. That is to formulate the search as a top-down process. We address top-down visual search in Wimmelbild with the
help of diffuse distance and curvature coding fields.
Human motion analysis is one of the active research areas in computer vision. The trend shifts from computing motion
fields to determining actions. We present an action coding scheme based on a trajectory of features defined with respect
to a part based coordinate system. The method does not require prior human model or special motion capture hardware.
The features are extracted from images segmented in the form of silhouettes. The feature extraction step ignores 3D
effects such as self occlusions or motion perpendicular to the viewing plane. These effects are later revealed in the
trajectory analysis. We demonstrate preliminary experiments.
Symmetric axis based representations have been widely employed to enhance visualization and to enable quantitative analysis, classification, and registration of medical images. Although the basic idea of shape representation via local symmetries is very old, recently, various new techniques for extracting local symmetries are proposed. Despite seemingly different tools, the main - if not only - difference among these new methods is how the computation is carried out. Recently, by Tari and Shah, a new method for computing symmetries are proposed, and the comparison of the method to the related works is provided. The method constructs a nested symmetry set of an increasing degree of symmetry and decreasing dimension. This is achieved by examining the local geometry of a new distance function. Because the method doesn't suppress any of the symmetry based representations. In this paper, a computational implementation for assigning perceptual meaning and significance to the points in the symmetry set is provided. The coloring scheme allows recovery of the features of interest such as the shape skeletons from the complicated symmetry representation. The method is applicable to arbitrary data including color and multi-modality imags. On the computational side, for a 256 X 256 binary image, two minutes on a low-end Pentium machine is sufficient to compute both the distance function and the colored nested symmetries at four scales.
Availability of different imaging modalities requires techniques to process and combine information form different images of the same phenomena. We present a symmetry based approach for combining information from multiple images. Fusion is performed at data level. Actual object boundaries and shape descriptors are recovered directly from raw sensor output(s). Method is applicable to arbitrary number of images in arbitrary dimension.
Requirements for a good shape representation lead to descriptors that are object centered and that have the notion of scale. These representations usually take the form of shape skeletons at multiple detail levels. Classical tool for skeleton extraction is the grassfire equation, in which the process is lossless and the equation can be run backwards in order to obtain shape boundary from the shape skeleton. Many complicated strategies have been devised to assign significance to skeletal points in order to arrive at the skeleton scale space. A recent alternative approach is to introduce regularization directly to the skeleton extraction process, by combining diffusion with grassfire. Very recently, techniques, similar in spirit, which combine nonlinear smoothing of the shape boundary with the grassfire, in order to extract an axis based description, are presented independently. When diffusion is introduced into the formulation, inverse equation is no longer stable. This is the issue we will be addressing in the context of the method presented by Tari and Shah for extraction of nested symmetries from arbitrary images in arbitrary dimension. The basic tool used in the method is a specific distance function which is the steady-state solution of an elliptic boundary value problem. We present an inverse equation and show how one may obtain the whole distance surface from a sparse representation, providing a means for determining the shape boundary from the shape skeleton. The presented technique can be used for feature-preserving compression.
A novel method for simultaneous image segmentation and shape decomposition is presented. The method may be applied directly to grayscale images. The is based on the analysis of the level curves of an 'edge-strength' function which is a measure of boundaryness of the image at each point.