24872800
Face Recognition using Patch-based Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern official convexity and concavity. Spin images (Johnson and Hebert, 1999) are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate
121595784
Face recognition using shading-based curvature attributes
In this paper, we apply shape-from-shading to face images to extract fields of surface normals and make estimates of surface curvature attributes. The attributes studied include minimum and maximum curvature, mean and Gaussian curvature, and, curvedness and shape-index. The curvature attributes are encoded as histograms and are used to perform recognition using a number of distance and similarity measures including the Euclidean distance, the Shannon entropy, the Renyi entropy and the Tsallis entropy. We compare the results obtained using the different curvature attributes and the different entropy measures. Using precision-recall curves, we find that the best performance is delivered when the Shannon entropy is applied to histograms of shape index and curvedness.
32291231
Face Recognition with Region Division and Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern of facial convexity and concavity. Spin images [4] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 98% on CMU PIE sub- database.
36756485
Face Recognition with Irregular Region Spin Images
This paper explores how spin images can be constructed using shapefrom-shading information and used for the purpose of face recognition. We commence by extracting needle maps from gray-scale images of faces, using a mean needle map to enforce the correct pattern of facial convexity and concavity. Spin images [6] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on spin image histograms for an arrangement of image patches. Comparing to our previous spin image approach, the current one has two basic difference: Euclidean distance is replaced by geodesic distance; Irregular face region is applied to better fit face contour. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 93% on CMU PIE sub-database.
3388099
Compressing and companding high dynamic range images with subband architectures
High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multi-scale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.
737421
Recognising trajectories of facial identities using kernel discriminant analysis
We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the nonlinear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories. q 2003 Elsevier B.V. All rights reserved.
27961753
Effective Algorithms for Tag Snp Selection
Single nucleotide polymorphisms (SNPs), due to their abundance and low mutation rate, are very useful genetic markers for genetic association studies. However, the current genotyping technology cannot afford to genotype all common SNPs in all the genes. By making use of linkage disequilibrium, we can reduce the experiment cost by genotyping a subset of SNPs, called Tag SNPs, which have a strong association with the ungenotyped SNPs, while are as independent from each other as possible. The problem of selecting Tag SNPs is NP-complete; when there are large number of SNPs, in order to avoid extremely long computational time, most of the existing Tag SNP selection methods first partition the SNPs into blocks based on certain block definitions, then Tag SNPs are selected in each block by brute-force search. The size of the Tag SNP set obtained in this way may usually be reduced further due to the inter-dependency among blocks. This paper proposes two algorithms, TSSA and TSSD, to tackle the block-independent Tag SNP selection problem. TSSA is based on A* search algorithm, and TSSD is a heuristic algorithm. Experiments show that TSSA can find the optimal solutions for medium-sized problems in reasonable time, while TSSD can handle very large problems and report approximate solutions very close to the optimal ones.
6066153
Kernel Methods for Document Filtering
This paper describes the algorithms inplemented by the KerMIT consortium for its participation in the Trec 2002 Filtering track. The consortium submitted runs for the routing task using a linear SVM, for the batch task using the same SVM in combination with an innovation threshold-selection mechanism, and for the adaptive task using both a second-order perceptron and a combination of SVM and perceptron with uneven margin. Results seem to indicate that these algorithm performed relatively well on the extensive TREC benchmark.
9099317
String extraction from color airline coupon image using statistical approach
A novel technique is presented in this paper to extract strings in color images of both business settlement plan (BSP) and non-BSP airline coupon. The essential concept is to remove non-text pixels from complex coupon images, rather than extract strings directly. First we transfer color images from RGB to HSV space, which is approximate uniformed, and then remove the black component of images using the property of HSV space. A statistical approach called principal components analysis (PCA) is applied to extract strings by removing the background decorative pattern based on priori environment. Finally, a method to validate and improve performance is present.