25090129
Comments on "A translation- and scale-invariant adaptive wavelet transform"
Xiong et al. presented a translationand scale-invariant adaptive wavelet transform. We show that their renormalized signal (pre-wavelet transform) is already translation- and scale-invariant. Thus, it is not just invariant under the wavelet transform. We give a necessary and sufficient condition for renormalized signals to be affine transform invariant.
6315327
Reversible data embedding using a difference expansion
Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
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.
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.
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.
4621823
On the Testable Implications of Causal Models with Hidden Variables
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.
18086799
Testing the suitability of Markov chains as Web usage models
Markov chains have been used to model Web usages and served as the basis for statistical testing, performance evaluation, and reliability analysis. However, most of such applications of Markov chains were carried out without answering the question: "Can Web usage be accurately modeled by Markov chains?" In this paper, we propose a set of tests, which are easy to perform based on information about Web link usage frequencies gathered from Web server logs, to answer this question. We applied this approach to our university's Web site, and our results indicate that Markov chains can provide fairly accurate models of Web usages.