Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 4 Articles
This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In\nparticular, a recently introduced common framework is the starting point of the study: it includes an initial facial\ndetection, the subsequent facial traits description, the data reduction step, and the final classification step. The\nalgorithmic configurations are featured by different descriptors and different strategies to build the training dataset\nand to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available\ndatasets and image sequences specifically acquired in order to evaluate the performance even under real-world\nconditions, i.e., in the presence of scaling and rotation....
Stereo matching under complex circumstances, such as low-textured areas and high dynamic range (HDR) scenes, is\nan ill-posed problem. In this paper, we introduce a stereo matching approach for real-world HDR scenes which is\nbackward compatible to conventional stereo matchers. For this purpose, (1) we compare and evaluate the\ntone-mapped disparity maps to find the most suitable tone-mapping approach for the stereo matching purpose.\nThereof, (2) we introduce a combining graph-cut based framework for effectively fusing the tone-mapped disparity\nmaps obtained from different tone-mapped input image pairs. And finally, (3) we generate reference ground truth\ndisparity maps for our evaluation using the original HDR images and a customized stereo matching method for HDR\ninputs. Our experiments show that, combining the most effective features of tone-mapped disparity maps, an\nimproved version of the disparity is achieved. Not only our results reduce the low dynamic range (LDR), conventional\ndisparity errors by the factor of 3, but also outperform the other well-known tone-mapped disparities by providing the\nclosest results to the original HDR disparity maps....
Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from\nscientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of\nquality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective\nquality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive\nbenchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were\nbenchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a\ntotal of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were\ncomputed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores.\nStatistical analysis was performed on the performance indexes to discriminate small differences between metrics.\nResults demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable\npredictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can\nbe improved by considering non-linearities of the human visual system, while further efforts are necessary to improve\nperformance of no-reference quality metrics for HDR content....
Noise estimation is fundamental and essential in a wide variety of computer vision, image, and video processing\napplications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the\nsetting of noise levels. This paper proposes a new superpixel-based framework associated with statistical analysis for\nestimating the variance of additive Gaussian noise in digital images. The proposed approach consists of three major\nphases: superpixel classification, local variance computation, and statistical determination. The normalized cut algorithm is\nfirst adopted to effectively divide the image into a set of superpixel regions, from which the noise variance is computed\nand estimated. Subsequently, the Jarqueââ?¬â??Bera test is used to exclude regions that are not normally distributed. The\nsmallest standard deviation in the remaining regions is finally selected as the estimation result. A wide variety of noisy\nimages with various scenarios were used to evaluate this new noise estimation algorithm. Experimental results\nindicated that the proposed framework provides accurate estimations across various noise levels. Comparing\nwith many state-of-the-art methods, our algorithm strikes a good compromise between low-level and high-level noise\nestimations. It is suggested that the proposed method is of potential in many computer vision, image, and video\nprocessing applications that require automation....
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