Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches

The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems

experimental results of MIS, MIES, MIDES and D3P

In this technical report we present in detail the results of the first 100 experiments of stochastic irregular graph pyramid of 100100 and 200200 images i.e graphs using methods MIS, MIES, MIDES and D3P. For details about these methods and irregular images pyramid see Technical Report PRIP-TR-74 [HK02]. This report extends PRIP-TR-74

Grouping and Segmentation in a Hierarchy of Graphs

We review multilevel hierarchies under the special aspect of their potential for segmentation and grouping. The one-to-one correspondence between salient image features and salient model features are a limiting assumption that makes prototypical or generic object recognition impossible. The region’s internal properties (color, texture, shape,...) help to identify them and their external relations (adjacency, inclusion, similarity of properties) are used to build groups of regions having a particular consistent meaning in a more abstract context. Lowlevel cue image segmentation in a bottom-up way, cannot and should not produce a complete final “good” segmentation. We present a hierarchical partitioning of images using a pairwise similarity function on a graphbased representation of an image. This function measures the difference along the boundary of two components relative to a measure of differences of the components ’ internal differences. Two components are merged if there is a low-cost connection between them. We use this idea to find region borders quickly and effortlessly in a bottom-up way, based on local differences in a specific feature. The aim of this paper is to build a minimum weight spanning tree (MST) in order to find region borders quickly in a bottom-up ’stimulus-driven ’ way based on local differences in a specific feature