Video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching. This paper proposes two new sequence-matching techniques for copy detection and compares the performance with one of the existing techniques. Motion, intensity and color-based signatures are compared in the context of copy detection. Results are reported on detecting copies of movie clips.
In the past, recognition systems have relied solely on geometric properties of objects. This paper discusses the simultaneous use of geometric as well as reflectance properties for object recognition. Neighboring points on a smoothly curved surface have similar surface orientations and illumination conditions. Hence, their brightness values can be used to compute the ratio of their reflectance coefficients. Based on this observation, we develop an algorithm that estimates a reflectance ratio for each region in an image with respect to its background. The algorithm is computationally efficient as it computes ratios for all image regions in just two raster scans. The region reflectance ratio represents a physical property of a region that is invariant to the illumination conditions. The reflectance ratio invariant is used to recognize three-dimensional objects from a single brightness image. Object models are automatically acquired and represented using a hash table. Recognition and pose estimation algorithms are presented that use the reflectance ratios of scene regions as well as their geometric properties to index the hash table. The result is a hypothesis for the existence of an object in the image. This hypothesis is verified using the ratios and locations of other regions in the scene. The proposed approach to recognition is very effective for objects with printed characters and pictures. We conclude with experimental results on the invariance of reflectance ratios and their application to object recognition.
This paper examines the combination of the Hough transform with geometric hashing as a technique for object recognition. Geometric hashing is a technique for fast indexing into object-model databases by creating multiple invariant indices from model features; yet its description applies to objects that are modeled by point sets. Extracting points locally from image data is a noise sensitive process, and the analysis of geometric hashing on point sets shown that it is very sensitive to noise. The use of the Hough transform as a first layer for extracting features imposes constraints on the image data, and in domains in which the constraints are appropriate, there is a significant reduction in noise effects on geometric hashing. The use of arbitrary primitive features in geometric hashing schemes also has other advantages. As a concrete example, experiments are performed with objects modeled by lines. The output of the line-Hough transform on intensity images is used to directly encode invariant geometric properties of shapes. points in Hough space that have high counts are combined to yield invariant geometric indices. Objects containing lines are modeled as a collection of points in dual space, and invariant indices in dual space are found by computing invariant dual space transformations. The combination of the Hough transform and geometric hashing is shown by experiments to be noise resistant and suitable for cluttered environments.
We describe the research activities of the Exploratory Computer Vision Group at the IBM Thomas J. Watson Research Center; this is a follow-up of the work reported previously.6
The focus of the ongoing work is the development of an experimental vision system for recognition of 3D objects. The thrust of the development of the vision system is to investigate techniques that may lead to a system that scales with the size of the problem; here, by the size of the problem, we mean the complexity of the scene - the number of object in the scene - and, the number of objects in the database - i.e., the number of objects that the system can recognize.
Fusion is a recurring theme in our research. E.g., fusion of evidence about different features extracted from the data; fusion of information obtained at different points in the image; fusion of information extracted from high and low-resolution images. Therefore, rather than focussing on a particular aspect of our work, we present an overview of the work.
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