101 research outputs found
Compressive sensing using the modified entropy functional
Cataloged from PDF version of article.In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In
this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The
proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore,
it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for
compressive sensing applications. Simulation examples with both 1D signals and images are presented.
© 2013 Elsevier Inc. All rights reserved
3D Model compression using Connectivity-Guided Adaptive Wavelet Transform built into 2D SPIHT
Cataloged from PDF version of article.Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder. © 2009 Elsevier Inc. All rights reserved
Projections Onto Convex Sets (POCS) Based Optimization by Lifting
Two new optimization techniques based on projections onto convex space (POCS)
framework for solving convex and some non-convex optimization problems are
presented. The dimension of the minimization problem is lifted by one and sets
corresponding to the cost function are defined. If the cost function is a
convex function in R^N the corresponding set is a convex set in R^(N+1). The
iterative optimization approach starts with an arbitrary initial estimate in
R^(N+1) and an orthogonal projection is performed onto one of the sets in a
sequential manner at each step of the optimization problem. The method provides
globally optimal solutions in total-variation, filtered variation, l1, and
entropic cost functions. It is also experimentally observed that cost functions
based on lp, p<1 can be handled by using the supporting hyperplane concept
Entropy-Functional-Based Online Adaptive Decision Fusion Framework with Application to Wildfire Detection in Video
Cataloged from PDF version of article.In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented
Content-adaptive color transform for image compression
Cataloged from PDF version of article.In this paper, an adaptive color transform for image compression
is introduced. In each block of the image, coefficients of the color
transform are determined from the previously compressed neighboring
blocks using weighted sums of the RGB pixel values, making the transform
block-specific. There is no need to transmit or store the transform coeffi-
cients because they are estimated from previous blocks. The compression
efficiency of the transform is demonstrated using the JPEG image coding
scheme. In general, the suggested transformation results in better peak
signal-to-noise ratio (PSNR) values for a given compression level. ( C) 2011
Society of Photo-Optical Instrumentation Engineer
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