This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, addition of noise, change in 3D viewpoint, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through leastsquares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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.

Object recognition has reached a level where we can identify a large number of previously seen and known objects. However, the more challenging and important task of categorizing previously unseen objects remains largely unsolved. Traditionally, contour and shape based methods are regarded most adequate for handling the generalization requirements needed for this task. Appearance based methods, on the other hand, have been successful in object identification and detection scenarios. Today little work is done to systematically compare existing methods and characterize their relative capabilities for categorizing objects. In order to compare different methods we present a new database specifically tailored to the task of object categorization. It contains high-resolution color images of 80 objects from 8 different categories, for a total of 3280 images. It is used to analyze the performance of several appearance and contour based methods. The best categorization result is obtained by an appropriate combination of different methods.

In this report we consider the problem of 3D object recognition, and the role that perceptual grouping processes must play. In particular, we argue that a single level of perceptual grouping is inadequate, and that reliance on a single level of grouping is responsible for the specific weaknesses of several well-known recognition techniques. Instead, we argue that recognition must utilize a hierarchy of perceptual grouping processes, and describe an appearance-based system that uses four distinct levels of perceptual grouping, the upper two novel, to represent 3-D objects in a form that not only allows recognition, but reasoning about 3D manipulation of a sort that has been supported in the past only by 3D geometric models.

AbstractÐWe present a method of indexing three-dimensional objects from single two-dimensional images. Unlike most other methods to solve this problem, ours does not rely on invariant features. This allows a richer set of shape information to be used in the recognition process. We also suggest the kd-tree as an alternative indexing data structure to the standard hash table. This makes hypothesis recovery more efficient in high-dimensional spaces, which are necessary to achieve specificity in large model databases. Search efficiency is maintained in these regimes by the use of Best-Bin First search, a modified kd-tree search algorithm which locates approximate nearest-neighbors. Neighbors recovered from the index are used to generate probability estimates, local within the feature space, which are then used to rank hypotheses for verification. On average, the ranking process greatly reduces the number of verifications required. Our approach is general in that it can be applied to any real-valued feature vector. In addition, it is straightforward to add to our index information from real images regarding the true probability distributions of the feature groupings used for indexing. In this paper, we provide experiments with both synthetic and real images, as well as details of the practical implementation of our system, which has been applied in the domain of telerobotics. Index TermsÐModel-based object recognition; indexing; kd-tree algorithm; nearest-neighbors algorithm. 1

Object recognition from a single view fails when the available features are not sufficient to determine the identity of a single object, either because of similarity with another object or because of feature corruption due to clutter and occlusion. Active object recognition systems have addressed this problem successfully, but they require complicated systems with adjustable viewpoints that are not always available. In this

Abstract—We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs, where each one encodes information about some local part and the spatial relations from this part to others (that is, the part’s context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that, by integrating our representation with Boosting, the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object’s parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases, and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy. Index Terms—Object recognition, retrieval, Boosting, spatial pattern, contextual information. 1

Humans learn robust and efficient strategies for visual tasks through interaction with their environment. In contrast, most current computer vision systems have no such learning capabilities. Motivated by insights from psychology and neurobiology, I combine machine learning and computer vision techniques to develop algorithms for visual learning in open-ended tasks. Learning is incremental and makes only weak assumptions about the task environment. I begin

Tracking is frequently considered a frame-to-frame operation. As such, object recognition techniques are generally too slow to be used for tracking. There are domains, however, where the objects of interest do not move most of the time. In these domains, it is possible to watch for activity in the scene and then apply object recognition techniques to find the object's new location. This makes tracking a discrete process of watching for object disappearances and reappearances. We have developed a memory assistance tool that uses this approach to help people with slight to moderate memory loss keep track of important objects around the house. The system is currently deployed in a prototype smart home.

Appearance-based object recognition systems rely on training from imagery, which allows the recognition of objects without requiring a 3D geometric model. It has been little explored whether such systems can be trained from imagery that is unlabeled, and whether they can be trained from imagery that is not trivially segmentable.