36734863
Knowledge based fingerprint image enhancement
Fingerprint enhancement is a critical step in fingerprint identification. Most of the existing enhancement algorithms are based on local ridge direction. The main drawback of these methods lies in the fact that false estimate of local ridge direction will lead to poor enhancement. But the estimate of local ridge directions is unreliable in the areas corrupted by noise where enhancement is most needed. In this paper, we proposed a rule-based method to do fingerprint enhancement. We introduced human knowledge about fingerprints into the enhancement process in the form of rules and simulate what an expert will do to enhance a fingerprint image. In our method, the skeleton image is used to give ridge connection information for the enhancement of the binary image. Experiments show our algorithm is fast and has excellent performance.
2612233
Fingerprint enhancement with dyadic scale-space
Fingerprint enhancement is a critical step in automatic fingerprint verification system. Most of the existing enhancement uses a set of contextual filters 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 etc. This information is 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 courser to finer scheme. Thus a globe and integrate interpretation is available and it enables us to get rid of the influence of noise to the largest extent. Experiments show our algorithm is fast and improve the performance of fingerprint verification system.
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.
206622046
Fingerprint Alignment Using Similarity Histogram
The performance of fingerprint matching algorithm relies heavily on the accuracy of fingerprint alignment. Falsely aligning two feature sets extracted from two finger images of a fingerprint will increase the false rejection rate (FRR). In order to improve the performance of fingerprint matching algorithm, we present a new fingerprint alignment algorithm called similarity histogram approach (SHA). First, we calculate the local similarity matrix based on minutiae and associate ridges between two fingerprints. Then, similarity histograms of transformation parameters are constructed from local similarity matrix. In the end, the optimal transformation parameters are obtained using a statistical method. Experimental results on FVC databases show that our method is effective and reliable.
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.
34795143
Automatic Fingerprint Identification Using Cluster Algorithm
9933284
A MCMC approach for Bayesian super-resolution image reconstruction
In this paper, we consider the super-resolution image reconstruction problem. We propose a Markov chain Monte Carlo (MCMC) approach to find the maximum a posterior probability (MAP) estimation of the unknown high-resolution image. Firstly, Gaussian Markov random field (GMRF) is exploited for modeling the prior probability distribution of the unknown high-resolution image. Then, a MCMC technique (in particular, the Gibbs sampler) is introduced to generate samples from the posterior probability distribution to compute the MAP estimation of the unknown high-resolution image, which is obtained as the mean of the samples. Moreover, we derive a bound on the convergence time of the proposed MCMC approach. Finally, the experimental results are presented to verify the superior performance of the proposed approach and the validity of the proposed bound.
46034477
Reliability Measurement, Analysis, and Improvement for Large Software Systems
Abstracts This chapter surveys recent developments in software reliability engineering, and presents results in measuring test activities, analyzing product reliability, and identifying high risk areas for focused reliability improvement for large software systems. Environmental constraints and existing analysis techniques are carefully examined to select appropriate analysis techniques, models, implementation strategies, and support tools suitable for large software systems. The analysis and modeling activities include measure definition, data gathering, graphical test activity and defect tracking, overall quality assessment using software reliability growth models, and identification of problematic areas and risk management using tree-based reliability models. Various existing software tools have been adapted and integrated to support these analysis and modeling activities. This approach has been used in the testing phase of several large software products developed in the IBM Software Solutions Toronto Laboratory and was demon- strated to be effective and efficient. Various practical problems and solutions in implementing this strategy are also discussed.
622959
Markov Chain Monte Carlo Super-Resolution Image Reconstruction with Simultaneous Adaptation of the Prior Image Model
In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited and shown as an effective approach to perform super-resolution image reconstruction. However, one major challenge lies at the selection of the hyperparameter of the prior image model, which affects the degree of regularity imposed by the prior image model, and consequently, the quality of the estimated high-resolution image. To tackle this challenge, in this paper, we propose a novel approach to automatically adapt the model’s hyperparameter during the MCMC process, rather than the exhaustive, off-line search. Experimental results presented show that the proposed hyperparameter adaptation method yields extremely close performance to that of the optimal prior image model case.
3092732
Inequality Constraints in Causal Models with Hidden Variables
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
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.