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.
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
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.
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.
30275744
Robust Active Shape Model Construction and Fitting for Facial Feature Localization
Active Shape Model (ASM) has proved to be a powerful tool for interpreting face images. However, it may fail in the presence of non-Gaussian noise, or outliers. In this paper, we present a framework for both automatic model construction and efficient model fitting with outliers. In model construction, the training face samples are automatically labeled by local image search using Gabor wavelet features. Then robust principle component analysis (RPCA) is applied to capture the statistics of shape variations. In model fitting, an error function is introduced to deal with the outlier problem, which provides a connection to robust M-estimation. Gauss-Newton algorithm is adopted to efficiently optimize the robust energy function. Extensive experiments demonstrate the efficiency and robustness of our approach over previous methods.
34951978
A Top-Down Fingerprint Image Enhancement Method Based on Fourier Analysis
We present a top-down fingerprint image enhancement method which is based on Fourier analysis Our method takes advantage of two filtering methods in Fourier domain proposed previously and employs a top-down iteration filtering technique, which enables the enhancement procedure not to rely severely on an accurate estimation of ridge frequency and orientation and also enables us to make use of more than the local information in local fingerprint filtering Consequently, our method is robust to handle low quality fingerprint images Results of enhancement are presented for some representative fingerprint images.
313172
Minimum register instruction sequence problem: revisiting optimal code generation for DAGs
We revisit the optimal code generation or evaluation order determination problem-the problem of generating an instruction sequence from a data dependence graph (DDG). In particular, we are interested in generating an instruction sequence S that is optimal in terms of the number of registers used by the sequence S. We call this MRIS (Minimum Register Instruction Sequence) problem. We developed an efficient heuristic solution for the MRIS problem based on the notion of instruction lineage. This solution facilitates the sharing of registers among instructions within a lineage and across lineages by exploiting the structure of a DDG. We implemented our solution on a production compiler and measured the reduction in the number of (spill) loads and (Spill) stores and the wall-clock execution time for the SPEC95 floating point benchmark suite. On average our method reduced the number of loads and stores by 11.5% and 15.9%, respectively, and decreased the total execution time by 2.5%.
10875476
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
5113927
An unsupervised segmentation framework for texture image queries
In this paper a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.
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.
5928457
Permutation graphs and Petersen graph