25611508
Regression Nearest Neighbor in Face Recognition
In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets
83568
Fingerprint Reference Point Detection Based on Local Axial Symmetry
Reference point detection is an important process for fingerprint analysis. In this paper, we propose a novel feature which is named local axial symmetry (LAS) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. Experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods
14409052
Shear-resize factorizations for fast image registration
Owing to its effectiveness and simplicity, intensity-based method works well for registration of images. However, it needs a large amount of computation for geometric transformation. In this paper, we present two shear-resize matrix factorizations to accelerate the transformation. A transform matrix can be factorized into two shears and a fixed non-uniform resize, or three shears and a customizable resize. A customizable resize can be uniform in all dimensions or scaling just in one dimension. Shears can be implemented very fast by memory-shift, and a resize can be done by simple axis-aligned interpolation. The factorizations can be applied to both rigid-body and affine transformations. Their efficiency is performed by experiments on some standard test images and fingerprint images. The methods are quite promising for hardware implementation, and can also be extended to 3D or higher dimensional fast geometric transformation.
8628384
Fingerprint Indexing Based on LAS Registration
Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.
14923326
Co-histogram and Image Degradation Evaluation
The tool for image degradation evaluation addressed in this paper is called co-histogram, which is a statistic graph generated by counting the corresponding pixel pairs of two images. The graph is a two-dimensional joint probability distribution of the two images. A co-histogram shows how the pixels are distributed among combinations of two image pixel values. By means of co-histogram, we can have a visual understanding of PSNR, and the symmetry of a co-histogram is also significant for objective evaluation of image degradation. Our experiments with image degradation models of image compression, convolution blurring and geometric distortion perform the importance of the co-histogram.
207326756
Estimating the amount of defocus through a wavelet transform approach
This paper presents a new technique for defocus estimation of a captured image. In our method, a ratio of the wavelet coefficients of high frequency correspond to a same image point at two different levels is used. For an edge point, it is shown that the ratio is related to the amount of defocus. Let α be the ratio of wavelet coefficients at the first level to that at the second level. The value of α decreases as the amount of defocus increase. In our experiments of iris image analysis, when α is larger than 0.5 the number of feature points in an image almost remain constant. It means that the image is little defocused and available for image recognition. Compared with Fourier methods, this technique is more robust. In addition, this method is fast enough to be used in auto-focus system for tracking moving objects.
16767212
A Robust Method for Biological Sequence Clustering
In this paper, we proposed a two-phase hybrid method for biological sequence clustering, which combines the strengths of the hierarchical agglomerative clustering methods and the partition clustering methods. In phase I, the hybrid method uses the hierarchical agglomerative clustering algorithm to pre-cluster the aligned sequences, while in the second phase it takes the pre-clustering result as the initial partition for the profile hidden Markov models (HMMs) based k-means partition clustering method. Such initial partitions (generated from phase I), as against random initial partitions, are usually more reasonable and thus can avoid the inconsistency problem in the partition clustering methods due to the randomness in initial partitions. In addition, the inaccuracy of the hierarchical agglomerative clustering methods can be compensated by the profile HMM based k-means partition clustering since the latter is model-based and can better describe the dynamic properties of the data in a cluster. Experiments on a molecular sequence dataset demonstrate the effectiveness and the efficiency of the proposed hybrid clustering algorithm
9803776
Semantic propagation from relevance feedbacks
Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedback provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database, given a set of relevance feedbacks between object pairs. In particular, we present two semantic propagation algorithms that are applicable to CIBR systems with a feature vector space model and a general metric space model, respectively. Experiments on a 3D model retrieval system and a logo image retrieval system are performed to show the effectiveness of the proposed methods.
1874082
TCP-real: improving real-time capabilities of TCP over heterogeneous networks
We present a TCP-compatible and -friendly protocol which abolishes thr ee major shortfalls of TCP for reliable multimedia applications over heterogeneous networks: (i) ineffective bandwidth utilization, (ii) unnecessary congestion-oriented responses to wireless link errors (e.g., fading channels) and operations (e.g. handoffs), and (iii) wasteful window adjustments over asymmetric, low-bandwidth reverse paths. We propose TCP-Real, a high-throughput transport protocol that minimizes transmission-rate gaps, thereby enabling better performance and reasonable playback timers. In TCP-Real, the receiver decides with better accuracy about the appropriate size of the congestion window. Slow Start and timeout adjustments are used whenever congestion avoidance fails; however, rate and timeout adjustments are cancelled whenever the receiving rate indicates sufficient availability of bandwidth. We detail the protocol design and we report significant improvement on the performance of the protocol with time-constrained traffic, wireless link errors and asymmetric paths.
43360105
Factorizations of regular graphs
Let G be a k-regular graph of order 2n such that k≥n. Hilton (J. Graph Theory, 9 (1985), 193–196) proved that G contains at least ⌊k3⌋ edge-disjoint 1-factors. Hilton's theorem is improved in this paper that G contains at least ⌊k2⌋ edge-disjoint 1-factors. The following result is also proved in this paper: Let G be a 2-connected, k-regular, non-bipartite graph of order at most 3k − 3 and x, y be a pair of distinct vertices. If Gβ {x, y} is connected, then G contains an (x, y)-Hamilton path.
9240156
TeraScope: distributed visual data mining of terascale data sets over photonic networks
TeraScope is a framework and a suite of tools for interactively browsing and visualizing large terascale data sets. Unique to TeraScope is its utilization of the Optiputer paradigm to treat distributed computer clusters as a single giant computer, where the dedicated optical networks that connect the clusters serve as the computer's system bus. TeraScope explores one aspect of the Optiputer architecture by employing a distributed pool of memory, called LambdaRAM, that serves as a massive data cache for supporting parallel data mining and visualization algorithms.