548723
Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding
Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.
16021441
Hybrid inter- and intra-wavelet scale image restoration
This paper exploits both the inter- and intra-scale interdependencies that exist in wavelet coefficients to improve image restoration from noise-corrupted data. Using an over-complete wavelet expansion, we group the wavelet coefficients with the same spatial orientation at several scales. We then apply the linear minimum mean squared-error estimation to smooth noise. This scheme exploits the inter-scale correlation information of wavelet coefficients. To exploit the intra-scale dependencies, we calculate the co-variance matrix of each vector locally using a centered square-shaped window. Experiments show that the proposed hybrid scheme significantly outperforms methods exploiting only the intra- or inter-scale dependencies. The performance of noise removal also depends on wavelet filters. In our experiments a biorthogonal wavelet, which best characterizes the image inter-scale dependencies, achieves the best results.
12034638
A block-based inter-band lossless hyperspectral image compressor
We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image. We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intra-band compressor called LM which is worse than BH, but whose performance helps explain BH's performance.
15953437
A wavelet-based edge detection method by scale multiplication
A wavelet-based multiscale edge detection scheme is presented in this paper. By multiplying the wavelet coefficients at two adjacent scales to magnify significant structures and suppress noise, we determined edges as the local maxima directly in the scale product after an efficient thresholding, instead of first forming the edge maps at several scales and then synthesizing them together, as employed in many multiscale techniques. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.
10620171
Characterization of palmprints by wavelet signatures via directional context modeling
The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.
11724747
Wavelet estimation of fractional Brownian motion embedded in a noisy environment
This correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.
16393711
Salient Feature Selection for Visual Concept Learning
Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed. In the feature selection stage, salient patches are first detected and clustered. Then the region of dominance and salient entropy measures are calculated to reduce non-common salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning.
6734525
Propositional Satisfiability and Constraint Programming: A comparative survey
Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research cross-fertilizing occasionally. These two approaches to problem solving have a lot in common as evidenced by similar ideas underlying the branch and prune algorithms that are most successful at solving both kinds of problems. They also exhibit differences in the way they are used to state and solve problems since SAT's approach is, in general, a black-box approach, while CP aims at being tunable and programmable. This survey overviews the two areas in a comparative way, emphasizing the similarities and differences between the two and the points where we feel that one technology can benefit from ideas or experience acquired from the other.
9731248
A Novel Gabor-LDA Based Face Recognition Method
In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant vectors. Second, discriminant vectors are used to select discriminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy.
14053941
AnnoSearch: Image Auto-Annotation by Search
Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
36054855
MQML-Message Queuing Markup Language
Message Queuing allows applications to use components that communicate with one another using queued messages. In this paper, we propose a Message Queuing Markup Language (MQML), which is an XML based markup language designed to describe e-business applications and enterprise integration. The MQML language is extensible. The detailed description of the MQML is presented in this paper. An research prototype is given to validate the effectiveness and value of our proposed MQML for enterprise integration.