15171558
Visual Hull Embossment by Graph Cuts
In this paper, we present a visual hull embossment algorithm which works on the base of a visual hull to reconstruct a precise model from calibrated color images. The algorithm uses the geometry of the visual hull to compute voxel normals and visibilities. Then a photometric consistency field (PCF) is computed, which describes the photometric consistency score of every voxel. The visual hull is refined in a view dependent fashion. For each view, we compute a view dependent PCF (VDPCF) by resampling the PCF and then find an optimal depth image in VDPCF with graph cut based energy minimization, which takes into consideration data, smoothness and adjustment terms simultaneously. The depth images are used for carving voxels. Our algorithm can produce precise models with a moderate speed.
426930
An admission control scheme for end-to-end statistical QoS provision in IP networks
This paper presents a distributed and scalable admission control scheme to provide end-to-end statistical QoS guarantees in IP network. The basic idea of the scheme is that the ingress routers make admission control decisions according to the network status information obtained by sending probing packets along the selected routing path. Each router passively monitors the arriving traffic and marks the probing packets with its network status. The performance of the presented scheme is evaluated with a variety of traffic models, QoS metrics and network topologies. The simulation results show that the proposed scheme can accurately control the admissible region and effectively improve the utilization of network resource.
11924885
Distributed video coding using wavelet
This paper proposes a distributed video coding scheme based on the zero tree entropy (ZTE) coding. Wyner-Ziv theory on source coding with side information is taken as the basic coding principle, which makes independent encoding and joint decoding possible. In this scheme, wavelet transform is used to exploit the spatial correlation of a Wyner-Ziv frame. The quantized wavelet coefficients are reorganized in terms of the zero tree structure so as to identify the significant and insignificant coefficients. The significance map is intra-codec and transmitted. In particular, the significant coefficients are independently encoded with turbo coder, and only the parity bits are transmitted. At the decoder, a predictive frame generated through motion-compensated prediction is used as the side information, with which the Wyner-Ziv frame can be conditionally decoded. Experimental results show that, compared to the traditional intra-frame coding and pixel-domain Wnyer-Ziv video coding, the proposed scheme can achieve a better coding performance, especially at low bit rates
7272862
Linear transform based motion compensated prediction for luminance intensity changes
A linear transform based motion compensated prediction (LT-MCP) scheme is proposed in this paper to improve the efficiency of inter-frame prediction when the intensity of luminance changes abruptly across frames. Either uniform or non-uniform intensity changes can be well processed as the proposed scheme is applied at macroblock (MB) level. LT-MCP is implemented based on the H.264 reference software to evaluate its performance. For those sequences with significant luminance changes across frames, such as Crew, we can achieve about 1.5dB improvement over the conventional MCP with intra prediction disabled and about 0.8dB gain with intra prediction enabled. For those sequences without obvious luminance changes, the proposed scheme also works better.
41595049
Robust speech recognition method based on discriminative environment feature extraction
It is an effective approach to learn the influence of environmental parameters, such as additive noise and channel distortions, from training data for robust speech recognition. Most of the previous methods are based on maximum likelihood estimation criterion. However, these methods do not lead to a minimum error rate result. In this paper, a novel discriminative learning method of environmental parameters, which is based on Minimum Classification Error (MCE) criterion, is proposed. In the method, a simple classifier and the Generalized Probabilistic Descent (GPD) algorithm are adopted to iteratively learn the envirnnmental parameters. Consequently, the clean speech features are estimated from the noisy speech features with the estimated environmental parameters, and then the estimations of clean speech features are utilized in the back-end HMM classifier. Experiments show that the best error rate reduction of 32.1% is obtained, tested on a task of 18 isolated confusion Korean words, relative to aconventional HMM system.
58983783
Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment
Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
6724826
Spectrum Sensing Using Cyclostationary Properties and Application to IEEE 802.22 WRAN
Spectrum sensing in a very low SNR environment (less than -20 dB) is considered in this paper. We make use of the noise rejection property of the cyclostationary spectrum. The sensing algorithms are based on measurement of the cyclic spectrum of the received signals. The statistics of the cyclic spectrum of the stationary white Gaussian process are fully analyzed for three measurement methods of the cyclic spectrum. The application to IEEE 802.22 WRAN is presented and the probability of false alarm is analytically derived. The operating characteristic curves for the sensing algorithms are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.
36578035
Mobile robot sonar interpretation algorithm for distinguishing trees from poles
Abstract We describe an algorithm to automatically distinguish trees from round metal poles with a sonar system packaged on a mobile robot. A polynomial interpolation of the square root of the backscattered signal energy versus scan angle is first plotted. Asymmetry and fitting error are then extracted for each sweep across the object, giving a single point in an abstract phase space. Round metal poles are nearer to the origin than are trees, which scatter the sonar more irregularly due to lobulations and/or surface roughness. Results are shown for 20 trees and 10 metal poles scanned on our campus.
14749080
Mobile Robot Sonar Backscatter Algorithm for Automatically Distinguishing Walls, Fences, and Hedges
We present an algorithm to distinguish several types of brick walls, picket fences, and hedges based on the analysis of backscattered sonar echoes. The echo data are acquired by a mobile robot with a single-transducer 50 kHz pulse-echo sonar computer-controlled scanning system packaged as its sensor head. For several locations along a wall, fence, or hedge, a fan of backscatter sonar echoes is acquired, digitized, and stored on a computer as the sonar transducer is swept over a horizontal arc. Off-line, each digitized echo waveform is then rectified, smoothed, and summed to give a measure of the backscatter strength. This backscatter is then plotted versus scan angle, with a series of N-peak deformable templates fit to these data for each scan. The number of peaks in the best-fitting N-peak template indicates the presence and location of retro-reflectors, and allows automatic categorization of the various fences, hedges, and brick walls.
7918052
Fast Identification of Error-Prone Patterns for LDPC Codes under Message Passing Decoding
Although density evolution accurately predicts the asymptotic performance of LDPC codes, it provides limited information for codes of short or moderate block length. For this reason, combinatorial methods have been employed to analyze the performance of finite-length LDPC codes under message passing decoding. The error floor phenomenon is a key issue of finite-length effects, which is caused largely by poorly connected subgraphs residing in the LPDC codes structure. Because of the unequal noise immunity of different bit levels, error floors become more pronounced for LDPC-coded high-order modulation. Nevertheless, the enumeration of error-prone patterns that lead to error floors has been proven to be NP-hard, which motivates us to find substitute metrics with manageable complexity. Considering trapping sets always comprise loops of low extrinsic message degree (EMD) and the connectivity of a loop with the residual graph can be characterized by its approximate EMD (ACE), in this paper we propose a fast method to identify loops with low ACE for a given code. In light of this, the vulnerable variable nodes residing on low-ACE loops are judiciously mapped to bit levels benefitting from stronger error protection. As a result, the error floor can be significantly improved at the cost of a minor increase of hardware complexity.
7527642
Spectrum Sensing for OFDM Systems Employing Pilot Tones and Application to DVB-T OFDM
Spectrum sensing is one of the core technologies of cognitive radio (CR) systems which provide a viable solution to the problem of sparsity of wireless spectrum. Nowadays, OFDM techniques are adopted by many existing or progressing wireless communication standards. Thus, a robust spectrum sensing algorithm for OFDM modulated signals is highly desired to implement CR when the primary signal uses OFDM modulation. Motivated by this demand, a time-domain symbol cross-correlation based spectrum sensing algorithm (TDSC method) is presented in this paper. The algorithm makes use of the property that the mean of the TDSC of two OFDM symbols is not zero if they have embedded the same frequency-domain pilot tones. The statistical behavior of the proposed spectrum sensor is explicitly analyzed and a theoretical lower bound on the misdetection probability is derived in this paper. An intuitive spectrum sensing method which utilizes the cyclic prefix nature of the OFDM modulated signals (CP method) is also described in this paper as a reference for comparison. Finally, we use the DVB-T Standard [8] as an application example to illustrate the proposed spectrum sensing algorithm. The simulated channel environments are the Ricean and Rayleigh channels defined in the DVB-T Standard and the additive white Gaussian noise (AWGN) channel. Four CP ratios defined in the DVB-T Standard are simulated for the probability of false alarm equaling 0.01 and the sensing time set equal to 50 ms. Simulation results show that the TDSC method can achieve a misdetection probability of 0.1 when the SNR equals -20.5 dB for all four CP ratios. The TDSC method outperforms the CP method in all cases. Furthermore, the proposed method has approximately the same detection performance for different CP ratios while the detection performance of the CP method degrades dramatically when the CP ratio becomes small. Results also reveal that the simulated performance is very close to the theoretical lower bound indicating that the lower bound can be used as a good prediction of performance.