2498239
Automatic threshold decision of background registration technique for video segmentation
Background registration technique is useful to solve still object problem and uncovered background problem for video segmentation. However, it is hard to automatically decide the threshold of the frame difference for background registration to make it more feasible for real-time applications. Many previous works made efforts on automatic threshold decision for change detection. In this paper, we propose a new method of automatic threshold decision algorithm in a totally different viewpoint. Not only change detection but also the quantization effect in discrete domain is concerned. A Gaussianity test is first applied to find the standard deviation of Gaussian noise from the camera. Then, the quantization effect in discrete domain is taken into consideration to derive the relation between the standard deviation and the optimal threshold value. A couple MPEG-4 sequences and experimental sequences are tested as examples. Simulation results show that the calculated threshold values are suitable for background registration to give good segmentation results.
16730298
Efficient moving object segmentation algorithm using background registration technique
An efficient moving object segmentation algorithm suitable for real-time content-based multimedia communication systems is proposed in this paper. First, a background registration technique is used to construct a reliable background image from the accumulated frame difference information. The moving object region is then separated from the background region by comparing the current frame with the constructed background image. Finally, a post-processing step is applied on the obtained object mask to remove noise regions and to smooth the object boundary. In situations where object shadows appear in the background region, a pre-processing gradient filter is applied on the input image to reduce the shadow effect. In order to meet the real-time requirement, no computationally intensive operation is included in this method. Moreover, the implementation is optimized using parallel processing and a processing speed of 25 QCIF fps can be achieved on a personal computer with a 450-MHz Pentium III processor. Good segmentation performance is demonstrated by the simulation results.
8697300
Efficient architecture of binary motion estimation for MPEG-4 shape coding
This paper presents an efficient architecture of binary motion estimation (BME) for MPEG-4 shape coding. This architecture, called DDBME, mainly consists of a data dispatch based 1-D systolic array and a 16 X 32 bit search range buffer. In DDBME, bit parallelism technique is applied on the SAD calculation of block matching algorithm. In order to support efficiently bit-data parallel processing, bit addressing should be taken into consideration. The data dispatch technique is applied on 1-D array by the hardwired data flow routing such that the bit addressing operations can be efficiently reduced. The DDBME operating at 7.29 MHz can handle the real-time requirement for encoding MPEG-4 shape sequence at core profile level 2, i.e. 2 VOs with CIF format, 30 fps and assuming each frame contains 30% boundary macroblocks in average. For the same real-time specification, the optimized software running on RISC (Ultra Sparc, 300 MHz) can only achieve 1/20 performance.
32197027
A hardware accelerator for video segmentation using programmable morphology PE array
Video segmentation is a key operation for MPEG-4 and MPEG-7. It is a computationally intensive task and cannot be afforded by general microprocessors. In this paper, we propose a hardware accelerator for video segmentation. Since most of the core operations of video segmentation algorithms can be mapped to morphological operations, the core of this accelerator is a programmable morphology PE array. With the programmable ability of each PE and the interconnection between them, this accelerator can achieve both high throughput and high flexibility. Simulation shows the proposed hardware accelerator can speed up the most important video segmentation algorithms 20 times to achieve realtime performance and has high programmability with a little hardware cost overhead.
17373350
VLSI architecture design of MPEG-4 shape coding
This paper presents an efficient VLSI architecture design of MPEG-4 shape coding, which is the key technology for supporting the content-based functionality of the MPEG-4 video standard. The real-time constraint of MPEG-4 shape coding leads to a heavy computational bottleneck on today's computer architectures. To overcome this problem, design analysis and optimization of MPEG-4 shape coding are addressed. By utilizing the RISC-based model, computational behaviors of the MPEG-4 shape coding tool are carefully examined and analyzed. The characteristic of a large amount of bit-level data processing and data transfer of MPEG-4 shape coding motivates the optimization of bit-level data operations. Applying data-flow optimization and data reuse techniques, bit-level computation-efficient architectures, such as data-dispatch-based binary-shaped motion estimation, the delay-line model, and configurable context-based arithmetic coding, are designed to accelerate bit-level processing. These hardware blocks are integrated and scheduled in a very efficient data flow to achieve real-time performance for MPEG-4 CPL2 (core profile level 2) specification at 23.5 MHz clock rate. The system architecture is implemented using Verilog HDL and synthesized with a 0.35 /spl mu/m four-layer CMOS standard library.
16535706
An efficient architecture for two-dimensional discrete wavelet transform
This paper proposes an efficient architecture for the two-dimensional discrete wavelet transform (2-D DWT). The proposed architecture includes a transform module, a RAM module, and a multiplexer. In the transform module, we employ the polyphase decomposition technique and the coefficient folding technique to the decimation filters of stages 1 and 2, respectively. In comparison with other 2-D DWT architectures, the advantages of the proposed architecture are 100% hardware utilization, fast computing time (0.5-0.67 times of the parallel filters'), regular data flow, and low control complexity, making this architecture suitable for next generation image compression systems, e.g., JPEG-2000.
44509988
A threshold criterion, auto-detection and its use in MST-based clustering
Clustering is to group data points into homogenous clusters so that data points within the same cluster are more similar than data points belonging to different clusters. There are many effective clustering algorithms for discovering arbitrary shaped clusters, but one common problem of many algorithms is the difficulty for users to decide appropriate parameters for these algorithms. To reduce the dependence of clustering performance on parameters, this paper proposes a threshold criterion for the single linkage cluster analysis and incorporates it into the Minimum Spanning Tree (MST) based clustering method. Since the threshold can be automatically decided according to the underlying data distributions, arbitrary shaped clusters can be discovered with little human intervention. The experimental results on spatial data are very encouraging.
10243856
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
9226100
Mining Positive and Negative Association Rules from XML Query Patterns for Caching
Recently, several approaches that mine frequent XML query patterns and cache their results have been proposed to improve query response time. However, frequent XML query patterns mined by these approaches ignore the temporal sequence between user queries. In this paper, we take into account the temporal features of user queries to discover association rules, which indicate that when a user inquires some information from the XML document, she/he will probably inquire some other information subsequently. We cluster XML queries according to their semantics first and then mine association rules between the clusters. Moreover, not only positive but also negative association rules are discovered to design the appropriate cache replacement strategy. The experimental results showed that our approach considerably improved the caching performance by significantly reducing the query response time.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.
7800069
Detection of summative global predicates
In distributed programs, we usually keep some global predicates from being satisfied to make it easy to run the programs correctly. A common type of global predicates are: the total number of certain tokens in the whole distributed system is always the same or in specific ranges. In this paper, we call this summative predicates, classified into the following four: (1) at some global state of the system, N/spl ne/K, (2) NK (or N/spl ges/K), and (4) N=K, where N is the total number of tokens and K is a constant. This paper investigates the methods of detecting various summative global predicates. The first class of summative predicates are trivial to detect by simply checking each message. For the second class of summative predicates, B. Groselj (1993) and V.K. Garg (1995) solved the problem by reducing the problem to a maximum network flow problem. In this paper, we propose an elegant technique, called normalization, to allow the second and third classes of summative predicates to be solved by also reducing the problem to a maximum network flow problem. For the fourth class of summative predicates, we prove that it is an NP-complete problem.