8764549
Efficient rate-distortion estimation for H.264/AVC coders
In video coders, the optimal coding mode decision for each coding block can be achieved by exhaustively calculating the rate-distortion cost, which simultaneously considers the distortion performance and the coding bit consumption of all possible modes. The best mode is chosen from the one with the minimum Lagrange cost. To avoid the expensive computation of Lagrange costs, in this paper, we propose transform-domain bit-rate estimation and distortion measures, based on quantized and inverse quantized integer transform coefficients, for the inter-mode decision in H.264/AVC coders. With the proposed scheme, entropy coding, inverse transform, and pixel-reconstructions are not required in the process. With ignorable degradation in coding performance, simulations demonstrate that the proposed estimation method achieves about 40% reduced computation time of rate-distortion cost for the inter-mode decision and saves about 17% total encoding time while combining with fast motion estimation and fast mode decision algorithms.
26719731
Recursive discrete Fourier transform with unified IIR filter structures
In this paper, we propose two unified IIR filters to recursively compute the discrete Fourier transform (DFT). With the advantage of local connection, regularity and modularity, the proposed methods can compute all DFT coefficients with the same filter structure. Based on stability behavior, the proposed IIR filter structures with optimal coefficients can achieve more accurate results than the traditional ones whose filter coefficients should be changed for computing the different DFT coefficients. Simulation results verify the aforementioned theoretical observations.
17739128
Modified Hough Transforms for Object Feature Extraction
In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly detect natural object parameters. Using designed test patterns and real images, simulations show that the proposed weighting functions are helpful in detecting noise-corrupted object features. Due to its robustness, the MHT can be easily figured with a coarse-to-fine adaptive search mechanism to reduce the huge amount of computation for feature parameters extraction.
36888588
Efficient connected-index finite-length arithmetic codes
In this paper, we propose a connected-index approach to construct efficient finite-length arithmetic codes by splitting the information of the last nonfitting symbol into the current and subsequent codewords. The proposed arithmetic codes, which limit the error propagation in about one block, require neither a post-appended end-of-block symbol, nor pre-affixed side-information, to characterize the number of encoded symbols. Hence, the proposed finite-length arithmetic codes can nearly achieve the coding efficiency attained by infinite-length arithmetic codes. With high coding efficiency, limited error-propagation, and the regular process, the proposed coding approach is suitable for information exchange with small packets in modern high-speed network systems.
5502812
A Continuous Genetic Algorithm for Global Optimization
17816838
Neighbor-Constrained Segmentation with 3D Deformable Models
A novel method for the segmentation of multiple objects from 3D medical images using inter-object constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring positions and shapes of a set of training images. By estimating the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. We feel that this method is useful in situations where there is limited inter-object information as opposed to robust global atlases. Results and validation from various experiments on synthetic data and medical imagery in 2D and 3D are demonstrated.
154630
Dual Labeling: Answering Graph Reachability Queries in Constant Time
Graph reachability is fundamental to a wide range of applications, including XML indexing, geographic navigation, Internet routing, ontology queries based on RDF/OWL, etc. Many applications involve huge graphs and require fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the vertices, such that the reachability between any two vertices may be decided using their labels only. For sparse graphs, 2-hop based reachability labeling schemes answer reachability queries efficiently using relatively small label space. However, the labeling process itself is often too time consuming to be practical for large graphs. In this paper, we propose a novel labeling scheme for sparse graphs. Our scheme ensures that graph reachability queries can be answered in constant time. Furthermore, for sparse graphs, the complexity of the labeling process is almost linear, which makes our algorithm applicable to massive datasets. Analytical and experimental results show that our approach is much more efficient than stateof- the-art approaches. Furthermore, our labeling method also provides an alternative scheme to tradeoff query time for label space, which further benefits applications that use tree-like graphs.
29686713
A note on Ramsey numbers with two parameters
The Ramsey number R(G1, G2) is the smallest integer p such that for any graph G on p vertices either G contains G1 or G- contains G2, where G- denotes the complement of G. In this paper, some new bounds with two parameters for the Ramsey number R(G1, G2), under some assumptions, are obtained. Especially, we prove that R(K6 - e, K6) ≤ 116 and R(K6 - e, K7) ≤ 202, these improve the two upper bounds for the classical Ramsey number in [S.P. Radziszowski, Small Ramsey number, Electron. J. Combin. DSI (2002) 1-36].
8294856
Clustering moving objects
Due to the advances in positioning technologies, the real time information of moving objects becomes increasingly available, which has posed new challenges to the database research. As a long-standing technique to identify overall distribution patterns in data, clustering has achieved brilliant successes in analyzing static datasets. In this paper, we study the problem of clustering moving objects, which could catch interesting pattern changes during the motion process and provide better insight into the essence of the mobile data points. In order to catch the spatial-temporal regularities of moving objects and handle large amounts of data, micro-clustering [20] is employed. Efficient techniques are proposed to keep the moving micro-clusters geographically small. Important events such as the collisions among moving micro-clusters are also identified. In this way, high quality moving micro-clusters are dynamically maintained, which leads to fast and competitive clustering result at any given time instance. We validate our approaches with a through experimental evaluation, where orders of magnitude improvement on running time is observed over normal K-Means clustering method [14].