A Resource-Aware Nearest-Neighbor Search Algorithm for K-Dimensional Trees

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Provided by: Technical University of Lodz
Topic: Hardware
Format: PDF
K- dimensional tree search is widely used today in computer vision - for example in object recognition to process a large set of features and identify the objects in a scene. However, the search times vary widely based on the size of the data set to be processed, the number of objects present in the frame, the size and shape of the k-dimensional tree, etc. Constraining the search interval is extremely critical for real-time applications in order to avoid frame drops and to achieve a good response time. The inherent parallelism in the algorithm can be exploited by using massively parallel architectures like many-core processors.
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