30175148
Fingerprint enhancement with dyadic scale-space
Fingerprint enhancement is a critical step in fingerprint identification. Most of the existing enhancement uses a set of contextual fillers to enhance fingerprint. The main drawback of these methods is these contextual filters based on the local information of the fingerprint, such as ridge width, orientation, curvature et al. These information are unreliable in the areas corrupted by the noise. This paper introduces the scale space theory in the computer vision to enhance the fingerprint. In the enhancement process, decompose fingerprint into a series of images and organize the images by finer to coarser scheme. Thus a globe and integrate interpretation is available and it enable us to get rid of the influence of noise to the largest extent. Experiments show our algorithm is fast and has excellent performance.
46662719
A minutiae matching algorithm in fingerprint verification
Fingerprint matching is one of the most important problems in AFIS. In general, we use minutiae such as ridge endings and ridge bifurcation to represent a fingerprint and do fingerprint matching through minutiae matching. We propose a minutiae matching algorithm which modified Jain et al.'s algorithm (1997). Our algorithm can better distinguish two images from different fingers and is more robust to nonlinear deformation. Experiments done on a set of fingerprint images captured with an inkless scanner shows that our algorithm is fast and has high accuracy.
16586772
A new state-space approach for super-resolution image sequence reconstruction
Super-resolution imaging is to overcome the inherent limitations of image acquisition to create high-resolution images from their low-resolution counterparts. In this paper, a novel state-space approach is proposed to incorporate the temporal correlations among the low-resolution observations into the framework of the Kalman filtering. The proposed approach exploits both the temporal correlations information among the high-resolution images and the temporal correlations information among the low-resolution images to improve the quality of the reconstructed high-resolution sequence. Experimental results show that the proposed framework is superior to bi-linear interpolation, bi-cubic spline interpolation and the conventional Kalman filter approach, due to the consideration of the temporal correlations among the low-resolution images.
16178189
Super-resolution Imaging Using Grid Computing
The super-resolution (SR) imaging is to overcome the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully developed and shown as a promising stochastic approach for addressing the SR problem. However, the MCMC SR approach requires substantial amounts of computational resources, for it not only needs to generate a huge number of samples, but also requires an exhaustive search for obtaining an optimal prior image model. To tackle the above computation challenge, Grid computing is introduced for tackling the SR problem in this paper. The computationally- intensive MCMC SR task is broke down into a set of independent and small sub-tasks, which are further distributed and implemented in the grid computing environment. Their respective results are finally assembled to produce a high-resolution image as the final result of the entire MCMC SR task. Experiments are conducted to show that grid computing can effectively accelerating the execution time of the MCMC SR algorithm.
14909770
Early measurement and improvement of software quality
The paper combines relevant research in software reliability engineering and software measurement to develop an integrated approach for the early measurement and improvement of software quality. Recent research in these areas is extended 1) to select appropriate software measures based on. A formal model, 2) to construct tree-based reliability models for early problem identification and quality improvement, and 3) to develop tools to support industrial applications. Initial results applying this approach to several IBM products demonstrated the applicability and effectiveness of this approach.
205046786
A formal program complexity model and its application
Abstract This article takes an axiomatic approach to defining program complexity using complexity rankings and measures. A complexity ranking is an abstract, qualitative comparison between programs that is approximated by a complexity measure on each program. A set of axioms are stated and a classification scheme is presented with properties of each class. This is extended a series of properties developed by Weyuker. The formal model has many applications, including evaluating and classifying existing complexity measures and serving as criteria for complexity measure selection.
37244
Data Hiding in Film Grain
This paper presents a data hiding technique based on a new compression enhancement called Film Grain Technology. Film grain is a mid-frequency noise-like pattern naturally appearing in imagery captured on film. The Film Grain Technology is a method for modeling and removing the film grain, thus enhancing the compression efficiency, and then using the model parameters to create synthetic film grain at the decoder. We propose slight modifications to the decoder that enable the synthetic film grain to represent metadata available at the decoder. We examine a number of implementation approaches and report results of fidelity and robustness experiments.