Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.

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haibin.ling @ siemens.com We propose using the proximity distribution of vectorquantized

Abstract—We construct detectors for “geometric ” objects in noisy data. Examples include a detector for presence of a line segment of unknown length, position, and orientation in two-dimensional image data with additive white Gaussian noise. We focus on the following two issues. i) The optimal detection threshold—i.e., the signal strength below which no method of detection can be successful for large dataset size. ii) The optimal computational complexity of a near-optimal detector, i.e., the complexity required to detect signals slightly exceeding the detection threshold. We describe a general approach to such problems which covers several classes of geometrically defined signals; for example, with one-dimensional data, signals having elevated mean on an interval, and, in-dimensional data, signals with elevated mean on a rectangle, a ball, or an ellipsoid. In all these problems, we show that a naive or straightforward approach leads to detector thresholds and algorithms which are asymptotically far away from optimal. At the same time, a multiscale geometric analysis of these classes of objects allows us to derive asymptotically optimal detection thresholds and fast algorithms for near-optimal detectors. Index Terms—Beamlets, detecting hot spots, detecting line segments, Hough transform, image processing, maxima of Gaussian processes, multiscale geometric analysis, Radon transform. I.

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Abstract—This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term “variable shape structure ” is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking. Index Terms—Object detection, shape modeling, probabilistic algorithms, dynamic programming. 1

Abstract — Robot manipulators generally rely on complete knowledge of object geometry in order to plan motions and compute successful grasps. However, manipulating real-world objects poses a substantial modelling challenge. New instances of known object classes may vary from learned models. Objects that are not perfectly rigid may appear in new configurations that do not match any of the known geometries. In this paper we describe an algorithm for learning generative probabilistic models of object geometry for the purposes of manipulation; these models capture both non-rigid deformations of known objects and variability of objects within a known class. Given a single image of partially occluded objects, the model can be used to recognize objects based on the visible portion of each object contour, and then estimate the complete geometry of the object to allow grasp planning. We provide two main contributions: a probabilistic model of shape geometry and a graphical model for performing correspondence between shape descriptions. We show examples of learned models from image data and demonstrate how the learned models can be used by a manipulation planner to grasp objects in cluttered visual scenes. I.

Abstract. This paper proposes a method for detecting shapes of variable structure in images with clutter. The term “variable structure ” means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance. 1

Abstract—We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L1 regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking. Index Terms—Matching, correspondence, linear programming, successive relaxation. Ç 1

In this paper, we address the issue of extracting contour of the object with a specific shape. A hierarchical graphical model is proposed to represent shape variations. A complex shape is decomposed into several components which are described as Principal Component Analysis (PCA) based models in various levels. The hierarchical representation allows for chain-like conditional dependency within a single level and bidirectional communication between different levels. Additionally, a Sequential Monte-Carlo (SMC) based inference algorithm that can explore the graphical structure is proposed to estimate the contour. The experiments performed on real-world hand and face images show that the proposed method is effective in combating occlusion and cluttered background. Moreover, it is possible to isolate the localization error to an individual component of a shape attributed to the hierarchical representation. 1.