13343820
The error concealment feature in the H.26L test model
This paper presents the error concealment (EC) feature implemented by the authors in the test model of the draft ITU-T video coding standard H.26L. The selected EC algorithms are based on weighted pixel value averaging for INTRA. pictures and boundary-matching-based motion vector recovery for INTER pictures. The specific concealment strategy and some special methods, including handling of B-pictures, multiple reference frames and entire frame losses, are described. Both subjective and objective results are given based on simulations under Internet conditions. The feature was adopted and is now included in the latest H.26L reference software TML-9.0.
15352869
Error Resilient Video Coding using Redundant Pictures
Coding of redundant pictures is supported in the latest international video coding standard H.264 (also known as MPEG-4 part 10 or AVC). This paper proposes a standard-compliant way to encode and decode redundant pictures for improved error resilience. The method is based on a combination of picture-level reference picture selection, reference picture list ordering, and a hierarchical allocation of redundant pictures, which can efficiently prevent temporal error propagation without relying on feedback information. Simulation results show that the method outperforms the optimal loss-aware rate-distortion optimized intra refresh method. The proposed algorithm has been adopted into the H.264 joint model.
14888347
Error Resilient Mode Decision in Scalable Video Coding
Error resilient macroblock mode decision has been extensively investigated in the literature for single-layer video coding, for which error resilient mode decision is also called as intra refresh. In this paper, we present a loss-aware rate-distortion optimized macroblock mode decision algorithm for scalable video coding, wherein more macroblock coding modes than intra and inter are involved. Thanks to the good performance, the proposed method has been adopted into the joint scalable video model by the joint video team.
2496902
Improved Error Concealment Using Scene Information
Signaling of scene information in coded bitstreams was proposed by the authors and adopted into the emerging video coding standard H.264 (also known as MPEG-4 part 10 or AVC) as a supplemental enhancement information (SEI) message. This paper proposes some improved error concealment methods for intra coded pictures and scene transition pictures using the signaled scene information. Simulation results show that the proposed methods outperform conventional techniques significantly.
14708720
Coding of faded scene transitions
Coding of a scene transition is often a challenging problem, from the compression efficiency point of view, because motion compensation may not be a powerful enough method to represent changes between pictures in the transition. This paper proposes a overlay coding technique for coding faded scene transitions. As shown by extensive simulations, over 50% bit-rate savings in both cross-fades and through-black fades compared to earlier techniques can be achieved. Overlay coding suits situations where video is edited manually or automatically.
14856574
Error Resilient Video Coding Using Unequally Protected Key Pictures
This paper proposes the use of unequally protected key pictures to prevent temporal error propagation in error-prone video communications. The key picture may either be an intra-coded picture or a picture using a long-term motion compensation reference picture through reference picture selection. The inter-coded key picture uses previous key pictures as motion compensation reference. Key pictures are better protected than other pictures using forward error correction in either source or transport coding. Simulation results show significantly improved error resiliency performance of the proposed technique compared to conventional methods.
15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
6612123
Automatic Misconfiguration Troubleshooting with PeerPressure
Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. 
 
In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. 
 
In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
17258193
Spatiotemporal video segmentation based on graphical models
This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.
18448866
Estimation of Multiple Illuminants from a Single Image of Arbitrary Known Geometry
We present a new method for the detection and estimation of multiple illuminants, using one image of any object with known geometry and Lambertian reflectance. Our method obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. Thus, the recovered multiple illuminants can be used both for image-based rendering and for shape reconstruction. We first develop our method for the case of a sphere with known size, illuminated by a set of directional light sources. In general, each point of such a sphere will be illuminated by a subset of these sources. We propose a novel, robust way to segment the surface into regions, with each region illuminated by a different set of sources. The regions are separated by boundaries consisting of critical points (points where one illuminant is perpendicular to the normal). Our region-based recursive least-squares method is impervious to noise and missing data and significantly outperforms a previous boundary-based method using spheres[21]. This robustness to missing data is crucial to extending the method to surfaces of arbitrary smooth geometry, other than spheres. We map the normals of the arbitrary shape onto a sphere, which we can then segment, even when only a subset of the normals is available on the scene. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects.