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

, and our in-vestigations substantiate that the number of candidates which are produced in the filter step and exactly evalu-ated in the refinement step is a fundamental efficiency parameter. After revealing the strong performance shortcomings of the state-of-the-art algorithm for k-nearest neighbor search

, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branchand-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal

Abstract—Locality Sensitive Hashing (LSH) is one of the most popular methods of the approximate near neighbor search. In applications that require the nearest neighbors of queries in a short time, LSH is sometimes used in pruning of the candidates of nearest neighbors. While the pruning reduces

In many domains such as computational geometry and database management, an object may be described by mul-tiple instances (points). Then the distance (or similarity) between two objects is captured by the pair-wise distances among their instances. In the past, numerous nearest neigh-bor (NN

Abstract Approximate nearest neighbor search (ANNS) is a basic and important technique used in many tasks

Abstract. Sampling-based motion planning has been successful in plan-ning the motion for a wide variety of robot types. An important primitive of these methods involves connecting nodes by selecting candidate neigh-bors and checking the path between them. Recently, an approach called Adaptive

neighbors of the target interfered with target processing relative to an unrelated condition. When primes were higher frequency neighbors of the target, no interfer-ence or facilitation was observed. On the other hand, with briefly presented masked primes, interference was observed with higher frequency

, MFR, and DIR when a common failure criterion is introduced: The algorithm stops if the best choice for the current node is the node from which the message came. We propose 2-hop GEDIR, DIR, and MFR methods in which node A selects the best candidate node C among its 1-hop and 2-hop neighbors according

Previous work shows that candidates receive more personal votes, frequently called “friends-and-neighbors ” votes, in areas where they have local attachments. This ar-ticle examines heterogeneity in friends-and-neighbors voting near candidates ’ counties of birth and residence in U.S. statewide