An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

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In this paper, we examine the use of machine learning to improve the robustness of systems for image analysis on the task of roof detection. We review the problem of analyzing aerial photographs, and describe an existing vision system that attempts to automate the identification of buildings in aerial images. After this, we briefly review several well-known learning algorithms that represent a wide variety of inductive biases. We report three experiments designed to illuminate facets of applying machine learning methods to the image analysis task; one experiment focuses on within-image learning, another deals with the cost of different errors, and a third addresses between-image learning. Experimental results demonstrate that machinelearned classifiers meet or exceed the accuracy of handcrafted solutions and that useful generalization occurs when training and testing on data derived from different images. 1 Introduction The number of images available to image analysts is growing rapid...

In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. We briefly detail four algorithms that we selected to improve rooftop detection. The data sets were highly skewed and the cost of mistakes differed between the classes, so we used ROC analysis to evaluate the methods under varying error costs. We report three experiments designed to illuminate facets of applying machine learning to the image analysis task. One investigated learning with all available images to determine the best performing method. Another focused on within-image learning, in which we derived training and testing data from the same image. A final experiment addressed between-image learning, in which training and testing sets came from different images. Results suggest that useful generalization occurred when training and testing on data derived from images differing in location and in aspect. They demonstrate that under most conditions, naive Bayes exceeded the accuracy of other methods and a handcrafted classifier, the solution currently used in the building detection system.

We describe a new approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. The main advantage of this approach is that it allows explicit modeling of dependencies between individual features of an object model. For instance, it can capture the fact that unmatched features due to partial occlusion are generally spatially coherent rather than independent. Efficient computation of the MAP estimate in our framework can be accomplished by finding a minimum cut on an appropriately defined graph. A special case of our framework yields even more efficient method, that does not use graph cuts. We call this technique spatially coherent matching. Our framework can also be seen as providing a probabilistic understanding of Hausdorff matching. We present ROC curves from Monte Carlo experiments that illustrate the improvement of the new spatially coherent matching technique over Hausdorff matching. 1 Introduction In this pap...

Abstract This thesis introduces a novel method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. First, a probabilistic framework is developed, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. In order to resolve ambiguous assertions from single view measurements, a sequential recognition strategy is developed in which evidence is accumulated over successive viewpoints until a definitive assertion can be made. The main contribution of the thesis is a strategy for conditioning the inference and the measurement processes with feedback from prior information. The problem of interest is that of model-based recognition, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. The robustness of the algorithm is illustrated through its application to two very different domains: (1) recognition of 3-D parametric models estimated directly from laser rangefinder data, (2) recognition of objects based on signatures extracted from optical flow images that they generate as they move with respect to a camera. The latter approach is completely novel and presents a major contribution to the field. Experimental results verify the strength of the approach at overcoming difficulties encountered in both contexts, as rapid convergence to the correct solution occurs in most cases.

In this paper, we examine the use of machine learning to improve a rooftop detection process, which is one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing vision system that automates the recognition of buildings in such images. After this, we briefly review two well-known learning algorithms, representing different inductive biases, that we selected to improve rooftop detection. An important aspect of this problem is that the data sets are highly skewed and the cost of mistakes differs for the two classes, so we evaluate the algorithms under varying misclassification costs using ROC analysis. We report three sets of experiments designed to illuminate facets of applying machine learning to the image analysis task. One set of studies focuses on within-image learning, in which both training and testing data are derived from the same image. Another addresses between-image learning, in which tr...