The similarity measure based nearest neighbour classifier is commonly used in object recognition or retrieval systems. The result of a query in such a system is in general the images from the database whose descriptor...
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ISBN:
(纸本)9784901122092
The similarity measure based nearest neighbour classifier is commonly used in object recognition or retrieval systems. The result of a query in such a system is in general the images from the database whose descriptors are closest to the descriptor of the query image. An important issue in large scale recognition systems is the computational burden caused by measuring the similarity of the features derived from the query image with the templates of the database objects. Hierarchical Agglomerative Clustering (HAC) algorithms are considered as a solution to this problem. In these algorithms, a dendrogram is formed down to top in the training stage. In the recognition phase, the query image moves on the dendrogram from the highest level to lower levels in order to find the best matched object. One of the most affecting issues in these algorithms is the strategy used for building the dendrogram. In this paper, different techniques adopted for this purpose are studied and compared within the framework of a face recognition system. Our experimental results demonstrate that using an appropriate merging technique, the average recognition time reduces while the performance of the system is not highly degraded.
The paper presents an efficient data encoder based on Lempel-Ziv-Welch (LZW) algorithm to be used in a lowpower capsule endoscopic system. The encoder is library-based where the size of the library can be set by the u...
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In this paper we investigate the problem of finding a delay- and degree-bounded maximum sum of nodes application level multicast tree. We then proved the problem is NP-hard, and its relationship with the well-studied ...
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An algorithm to generate background sprite images from multiview image sequences is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to provi...
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We analyze the scheduling aspects of database queries submitted to an abstract model of a very large distributed system. The essential elements of this model are: (a) a finite number of identical processing nodes with...
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In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled ...
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ISBN:
(纸本)0780341236
In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be apdated to real-time imaging scenarios.
We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN construct...
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The three-dimensional Schrödinger equation inverse scattering problem with a nonspherically-symmetric potential is related to the filtering problem of computing the linear leastsquares estimate of the three-dimen...
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We have designed a parallel architecture called the Semantic Network Array Processor (SNAP) for Natural Language Understanding (NLU) and other Artificial Intelligence applications. It is capable of executing large mar...
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This paper investigates some approaches for designing one-dimensional linear phase finite-duration impulse-responses (FIR) notch filters, which are based on the modification of several established design techniques of...
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This paper investigates some approaches for designing one-dimensional linear phase finite-duration impulse-responses (FIR) notch filters, which are based on the modification of several established design techniques of linear phase FIR band-selective filters. Based on extensive design examples and theoretical analysis, formulae have been developed for estimating the length of a linear phase FIR notch filter meeting the given specifications. In addition, the design of two-dimensional linear phase FIR notch filters is briefly considered. Illustrative examples are included.
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