8668982
Perceptually-tuned multiscale color-texture segmentation
We present a perceptually-tuned multiscale image segmentation algorithm that is based on spatially adaptive color and texture features. The proposed algorithm extends a previously proposed approach to include multiple texture scales. The determination of the multiscale texture features is based on perceptual considerations. We also examine the perceptual tuning of the algorithm and how it is affected by the presence of different texture scales. The multiscale extension is necessary for segmenting higher resolution images and is particularly effective in segmenting objects shown in different perspectives. The performance of the proposed algorithm is demonstrated in the domain of photographic images.
7832450
Adaptive perceptual color-texture image segmentation
We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.
971415
Adaptive image segmentation based on color and texture
We propose an image segmentation algorithm that is based on spatially adaptive color and texture features. The features are first developed independently, and then combined to obtain an overall segmentation. Texture feature estimation requires a finite neighborhood which limits the spatial resolution of texture segmentation, while color segmentation provides accurate and precise edge localization. We combine a previously proposed adaptive clustering algorithm for color segmentation with a simple but effective texture segmentation approach to obtain an overall image segmentation. Our focus is in the domain of photographic images with an essentially unlimited range of topics. The images are assumed to be of relatively low resolution and may be degraded or compressed.
37291833
A tight lower bound on the maximum genus of a simplicial graph
Abstract It is proved that every connected simplicial graph with minimum valence at least three has maximum genus at least one-quarter of its cycle rank. This follows from the technical result that every 3-regular simplicial graph except K 4 has a Xuong co-tree whose odd components have only one edge each. It is proved, furthermore, that this lower bound is tight. However, examples are used to illustrate that it does not apply to non-simplicial graphs. This result on maximum genus leads to several immediate consequences for average genus.
14053079
Face Samples Re-lighting for Detection Based on the Harmonic Images
Different environment illumination has a great impact on face detection. In this paper, we present a solution by the face relighting based on the harmonic images. The basic idea is that there exist nine harmonic images which can be derived from a 3D model of a face, and by which we can estimate the illumination coefficient of any face samples. To detect faces under the certain lighting conditions, we relight those original face samples to get more new face samples under the various possible lighting conditions by an illumination ratio image and then add them to the training set. By train a classifier based on Support Vector Machine (SVM), the experimental results turn out that the relighting subspace is effective during the detection under the diverse lighting conditions. We also use the relighting database to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
18216276
Locally Subcube-Connected Hypercube Networks: Theoretical Analysis and Experimental Results
We study hypercube networks with a very large number of faulty nodes. A simple and natural condition, the local subcube-connectivity, is identified under which hypercube networks with a very large number of faulty nodes still remain connected. The condition of local subcube-connectivity can be detected and maintained in a distributed manner based on localized management. Efficient routing algorithms on locally subcube-connected hypercube networks are developed. Our algorithms are distributed and local-information-based in the sense that each node in the network knows only its neighbors' status and no global information of the network is required by the algorithms. For a locally subcube-connected hypercube network that may contain up to 37.5 percent faulty nodes, our algorithms run in linear time and, for any two given nonfaulty nodes, find a routing path of length bounded by four times the Hamming distance between the two nodes. Theoretical analysis and experimental results are presented which show that, under a variety of probability distributions of node failures, hypercube networks are locally subcube-connected with a very high probability and our routing algorithms run in linear time and construct routing paths of nearly optimal length.
18640612
On approximating minimum vertex cover for graphs with perfect matching
It has been a challenging open problem whether there is a polynomial time approximation algorithm for the VERTEX COVER problem whose approximation ratio is bounded by a constant less than 2. In this paper, we study the VERTEX COVER problem on graphs with perfect matching (shortly, VC-PM). We show that if the VC-PM problem has a polynomial time approximation algorithm with approximation ratio bounded by a constant less than 2, then so does the VERTEX COVER problem on general graphs. Approximation algorithms for VC-PM are developed, which induce improvements over previously known algorithms on sparse graphs. For example, for graphs of average degree 5, the approximation ratio of our algorithm is 1.414, compared with the previously best ratio 1.615 by Halldorsson and Radhakrishnan.