This paper presents a top-down approach to stereo for use in driver assistance systems. We introduce an asymmetric configuration where monocular object detection and range estimation is performed in the primary camera and then that image patch is aligned and matched in the secondary camera. The stereo distance measure from the matching assists in target verification and improved distance measurements. This approach, Stereo-Assist, shows significant advantages over the classical bottom-up stereo approach which relies on first computing a dense depth map and then using the depth map for object detection. The new approach can provide increased object detection range, reduced computational load, greater flexibility in camera configurations (we are no longer limited to side-by-side stereo configurations), greater robustness to obstructions in part of the image and mixed camera modalities FIR/VIS can be used. We show results with two novel configurations and illustrate how monocular object detection allows for simple online calibration of the stereo rig.

We present a stochastic clustering algorithm which uses pairwise similarity of elements and show how it can be used to address various problems in computer vision, including the low-level image segmentation, mid-level perceptual grouping, and high-level image database organization. The clustering problem is viewed as a graph partitioning problem, where nodes represent data elements and the weights of the edges represent pairwise similarities. We generate samples of cuts in this graph, by using Karger's contraction algorithm (1996), and compute an "average" cut which provides the basis for our solution to the clustering problem. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. The complexity of our algorithm is very low: O(|E| log/sup 2/ N) for N objects, |E| similarity relations, and a fixed accuracy level. In addition, and without additional computational cost, our algorithm provides a hierarchy of nested partitions. We demonstrate the superiority of our method for image segmentation on a few synthetic and real images, both B&W and color. Our other examples include the concatenation of edges in a cluttered scene (perceptual grouping) and the organization of an image database for the purpose of multiview 3D object recognition.

We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent data elements and the weights of the edges represent pairwise similarities. Our algorithm generates samples of cuts in this graph, by using David Karger's contraction algorithm, and computes an "average" cut which provides the basis for our solution to the clustering problem. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. The complexity of our algorithm is very low: O(N log/sup 2/ N)for N objects and a fired accuracy level. Without additional computational cost, our algorithm provides a hierarchy of nested partitions. We demonstrate the superiority of our method for image segmentation on a few real color images. Our second application includes the concatenation of edges in a cluttered scene (perceptual grouping), where we show that the same clustering algorithm achieves as good a grouping, if not better as more specialized methods.

A key problem in appearance-based vision is understanding how to use a set of labeled images to classify new images. Systems that model human performance, or that use robust image matching methods, often use nonmetric similarity judgments; but when the triangle inequality is not obeyed, most pattern recognition techniques are not applicable. Exemplar-based (nearest-neighbor) methods can be applied to a wide class of nonmetric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques are ill-suited to deal with nonmetric dataspaces. We develop techniques for solving this problem, emphasizing two points: First, we show that the distance between images is not a good measure of how well one image can represent another in nonmetric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in nonmetric spaces, boundary points are less significant for capturing the structure of a class than in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance. We also suggest ways of applying parametric techniques to supervised learning problems that involve a specific nonmetric distance functions, showing how to generalize the idea of linear discriminant functions in a way that may be more useful in nonmetric spaces.

We present a stochastic clustering algorithm which uses pairwise similarity of elements, based on a new graph theoretical algorithm for the sampling of cuts in graphs. The stochastic nature of our method makes it robust against noise, including accidental edges and small spurious clusters. We demonstrate the robustness and superiority of our method for image segmentation on a few synthetic examples where other recently proposed methods (such as normalized-cut) fail. In addition, the complexity of our method is lower. We describe experiments with real images showing good segmentation results.

Curve matching is one instance of the fundamental correspondence problem. Our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive experiments where we apply the algorithm to silhouette matching. In these experiments, we examine partial occlusion, viewpoint variation, articulation, and class matching (where silhouettes of similar objects are matched). Based on the qualitative syntactic matching, we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images. We use this experiment to objectively evaluate our algorithm. First, we compare our results to those reported by others. Second, we use the dissimilarity values in order to organize the image database into shape categories. The veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation.

The organization of image databases can rely upon different aspects of image similarity. Here we extract silhouettes from images of three dimensional objects, and rely upon curve similarity for image classification. Our scheme avoids the embedding of images in a vector space. Instead, we propose a curve dissimilarity measure which relies upon a novel curve matching syntactic algorithm, and use it to represent the database as a complete graph, with nodes representing the images and dissimilarity values assigning weights to the edges. A robust clustering algorithm, which is based on a physical ferromagnet model, is used to find the hierarchical structure underlying the collection of images. We tested our scheme with a database of 90 real images of 6 objects, some of them very different, others rather similar. We get a perfect hierarchical classification of these images into 6 classes of objects belonging to 3 different families.

One of the key problems in appearance-based vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performance, or that use robust image matching methods, often make use of similarity judgments that are non-metric but when the triangle inequality is not obeyed, most existing pattern recognition techniques are not applicable. We note that exemplar-based (or nearest-neighbor) methods can be applied naturally when using a wide class of non-metric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We note that existing condensing techniques for finding class representatives are ill-suited to deal with non-metric dataspaces. We then focus on developing techniques for solving this problem, emphasizing two points: First, we show that the distance between two images is not a good measure of how well one image can represent another in non-metric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in non-metric spaces, boundary points are less significant for capturing the structure of a class than they are in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance using both synthetic and real images.

Traditionally, corners are found along step edges. In this paper we present an alternative approach-corners along ridges/troughs and local minima points. These features seem to be more reliable for tracking. A new approach for sub-pixel localization of these corners is suggested, using a local approximation of the image surface.