In this paper three image quality metrics are compared: mean-square error (MSB), noise quality measure (NQM), and structural information metric (SIM). Such a comparison is made in order to evaluate the performance of ...
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Traditionally, signal processing is considered simply lowlevel processing. In the past decade, however, signal processing has grown to become the area where a variety of tools are created to solve high-level problems ...
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Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many o...
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Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,m)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.
A mathematical framework for the solution of statistical inference problems on a class of random sets is proposed. It is based on a new definition of expected pattern. The least-mean-difference estimator (restoration ...
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Phase noise may be regarded as the most severe cause of performance degradation in OFDM systems. Hot-carriers (HCs), found in the CMOS synchronization circuits, are highmobility charge carriers that degrade the MOSFET...
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We present the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pair wise image distances to learn fixed-length vector representations of images. The aspects of the imag...
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Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as Texture Unit Coding (TUC) has shown promise in texture classification. This paper pre...
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Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum im...
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ISBN:
(纸本)9781601321190
Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum imagery which compromises their efficiency when image acquisition is subject to variable illumination. In this study, we propose a simple yet powerful solution that uses multiple cues to detect and locate faces accurately in near infrared (NIR) imaging systems. Specific objectives include (I) extract optimally the illumination and reflectance components of the original image using an intrinsic images decomposition technique, (2) modify the segmentation process by using an energy minimization procedure in an adaptive fashion, and (3) propose an enhanced face detection process on the basis of pupil localization. The evaluation of our face detector on image sequences of 45 different subjects from the CBSR NIR face dataset demonstrated a highly competitive accuracy with less than 5% error rate.
This paper introduces a new algorithm for scalable coding of wideband audio signals. The technique is based on quantization of bi-orthogonal wavelet transformed coefficients using a perceptual zerotree method. An init...
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This paper introduces a new algorithm for scalable coding of wideband audio signals. The technique is based on quantization of bi-orthogonal wavelet transformed coefficients using a perceptual zerotree method. An initial zerotree estimate of the wavelet coefficients is computed, followed by scalar quantization of the coefficients according to perceptual thresholds. The choice of wavelet decomposition and encoding parameters for each frame is adapted to the source characteristics employing a rate distortion criterion. The scalability of the coder is due to the tree structure, which enables graceful degradation with decrease in bit rate. Preliminary subjective tests indicate near-transparent quality for average bit rates in the range of 1.5 to 2.5 bits per sample.
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