Most range-based recognition systems require the calculation of a full disparity map at adequate resolutions prior to the recognition step. There also exist range-based systems that only require the computation of a sparse disparity map. We introduce a 3D shape classification method in which the disparity calculation is guided by the needs of the classification process. The method uses decision trees for shape classification and calculates the disparity only at certain locations in the image, as required by the tree structure. The calculation is very efficient as only the minimum number of disparity values are calculated. To render the classification robust, we use an ensemble of trees. The proposed ensemble method is different from the currently known ensemble methods and makes the classification system more robust to errors in the disparity calculation. The method was applied to a real world problem and good classification results were obtained. 1

This paper outlines the theoretical background and presents a new approach to human body tracking with monocular static camera. A novel "view-based representation" is introduced at the feature extraction stage. We show that ambiguities in correspondence, such as the ones that occur as the result of occlusion, can be resolved by using this approach. In particular, we store color information for each object in a vector of views, where the number of elements is determined online, using unsupervised clustering followed by the cluster validity assessment. Based on this representation a tracking system was developed. The prelimiary results presented show the discriminative potential of the proposed system.

Automatic classification of an image as a photograph of a real-scene or as a painting is potentially useful for image retrieval and Web site filtering applications. The main contribution of the paper is the proposition of several features derived from the color, edge, and gray-scale-texture information of the image that effectively discriminate paintings from photographs. For example, we found that paintings contain significantly more pure-color edges, and that certain gray-scale-texture measurements (mean and variance of Gabor filters) are larger for photographs. Using a large set of images (12000) collected from different Web sites, the proposed features exhibit very promising classification performance (over 90%). A comparative analysis of the automatic classification results and psychophysical data is reported, suggesting that the proposed automatic classifier estimates the perceptual photorealism of a given picture.

We compare the properties of intensity and color edges in photographs of real scenes and paintings. We demonstrate that paintings contain significantly more color-only edges, whereas the amount of intensity-only edges does not differ significantly between the two classes. In addition, color edge strength is significantly higher for paintings. The differences between paintings and photographs are more accentuated when high-resolution, losslessly compressed images are used. These distinguishing features can be used for the automatic differentiation between the two classes of images.

The following shape segmentation problem is addressed: find the part decomposition of a 3D object that accounts for an observed pattern of similarities among several of the object's views. This represents the inverse, ill-posed version of the direct problem of computing perceptual similarities among object views when the object parts are known. The problem is solved by inverting a proposed model for the direct similarity-from-parts problem by resorting to regularization techniques. The algorithm takes as input the geometry of the object (given as a triangular mesh), the camera positions corresponding to the rest views, and the perceptual similarities among the test views. The output of the algorithm is a segmentation of the surface of the object into connected regions, i.e., parts.

We present an algorithm for computing the relative perceptual saliencies of the features of a three-dimensional object using either goodness-of-view scores measured at several viewpoints or perceptual similarities among several object views. This technique addresses the inverse, ill-posed version of the direct problem of predicting goodness-of-view scores or viewpoint similarities when the object features are known. On the basis of a linear model for the direct problem, we solve the inverse problem using the method of regularization. The critical assumption we make to regularize the solution is that perceptual salience varies slowly on the surface of the object. The salient regions derived using this assumption empirically indicate what object structures are important in human three-dimensional object perception, a domain where theories typically have been based on somewhat ad hoc features.