With the development of multimedia processing and applications, multiple types of media security and forensic have been broadly taken into consideration. The media data includes audio, video, graphic, image and etc. T...
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Face recognition has been one of the popular and important parts in Human Computer Interaction (HCI) systems that find tremendous applications, some of which are very critical like access control, surveillance, etc. T...
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Face recognition has been one of the popular and important parts in Human Computer Interaction (HCI) systems that find tremendous applications, some of which are very critical like access control, surveillance, etc. There are numerous techniques available to process face images and hence, choosing an optimal algorithmic chain is not a straight forward job. The scenario gets much more interesting while implementing face recognition in applications connected to cloud via Internet of Things (IoT) platform. This paper reviews some of the effective face recognition algorithms and proposes an optimized algorithmic chain offering optimal classification accuracy and lower execution time;thereby making it appropriate for IoT related applications targeting human-centric systems. Also, achieving optimum efficiency by selecting appropriate number of features for a given combination of algorithms and the behaviour of algorithms due to partitioning of the images in case of Local Binary Pattern (LBP) is discussed. Results indicate enhanced classification rates with algorithmic fusion by creating chains or process flow of methods. Accuracy of up to 96% was obtained for one of the chains that were designed. Also, it is evident from the results that this chain outperforms some of the well-known state-of-the-art methods. (C) 2016 The Authors. Published by Elsevier B.v.
The main objective of this paper is to provide a comprehensive study on Sparse Representation based feature extraction techniques in the image classification domain. Sparse Representation (SR) plays a vital role in bo...
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In this work, a wavefront encoded (WFE) imaging system built using a squared cubic phase mask, designed to reduce the sensitivity of the imaging system to spherical aberration, is investigated. The proposed system all...
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In this work, a wavefront encoded (WFE) imaging system built using a squared cubic phase mask, designed to reduce the sensitivity of the imaging system to spherical aberration, is investigated. The proposed system allows the use of a space-invariant image restoration algorithm, which uses a single PSF, to restore intensity distribution in images suffering aberration, such as sample-induced aberration in thick tissue. This provides a computational advantage over depth-variant image restoration algorithms developed previously to address this aberration. Simulated PSFs of the proposed system are shown to change up to 25% compared to the 0 mu m depth PSF (quantified by the structural similarity index) over a 100 mu m depth range, while the conventional system PSFs change up to 84%. Results from experimental test-sample images show that restoration error is reduced by 29% when the proposed WFE system is used instead of the conventional system over a 30 mu m depth range. (C) 2016 Optical Society of America
Super-resolution reconstruction for image sequences is a promising imageprocessing technology that using complementary information among a set of images to reconstruct a high-resolution *** super-resolution reconstru...
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ISBN:
(纸本)9781509009107
Super-resolution reconstruction for image sequences is a promising imageprocessing technology that using complementary information among a set of images to reconstruct a high-resolution *** super-resolution reconstruction algorithms have been studied in the literature to reconstruct a high-resolution *** this paper,first,after presenting a condensed introduction of image registration algorithms including Lucchese algorithm,vandewalle algorithm and Keren algorithm,we experimentally compare the relative merits of these registration algorithms in terms of registration accuracy and noise ***,we experimentally compare four image reconstruction methods:projection onto convex sets method(POCS),iterative back-projection method(IBP),robust super resolution(Robust SR) and structure-adaptive normalized convolution(Structure-Adaptive NC),mainly in terms of Peak Signal to Noise Ratio(PSNR),in which salt and pepper noise is added in the low resolution *** is clearly demonstrated that the combination of Keren algorithm and Structure-Adaptive NC can achieve the best performance regarding the Lena image.
A typical infrared (IR) thermographic system intended for active thermal/IR nondestructive testing includes a heat source, an IR imager and a computer. The software ensures acquisition and processing of IR image seque...
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ISBN:
(纸本)9781510601024
A typical infrared (IR) thermographic system intended for active thermal/IR nondestructive testing includes a heat source, an IR imager and a computer. The software ensures acquisition and processing of IR image sequences to result in a binary map of defects or other image which is to be interpreted by a thermographer in order to meet inspection requirements. Typically, hardware developers supply a certain set of technical parameters of their units, such as heater power, imager temperature resolution, acquisition rate and a set of available data processingalgorithms. The suggested approach allows optimization of inspection parameters if thermal and optical parameters of test materials are known.
This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. T...
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This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing imageprocessingalgorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product. (c) 2015 Elsevier B.v. All rights reserved.
I consider a number of methods of automatic quadratic features adjustment for digital textural images of biological tissues in order to improve the quality of classification. The proposed approaches are based on optim...
I consider a number of methods of automatic quadratic features adjustment for digital textural images of biological tissues in order to improve the quality of classification. The proposed approaches are based on optimization procedures that use various quality criteria of a feature space as target functions. I investigate the methods based on random search, genetic algorithm, simulation of annealing, as well as the original hybrid algorithm. I presented results of experimental studies of the proposed algorithms on sets of real X-ray images of bone tissue and the lung CT images. We show that the hybrid algorithm provides more stable results regardless of the chosen quality criterion of the feature space, which is expressed in decreasing of the average percentage of incorrectly recognized images in comparison with the use of the specific optimization methods individually.
The paper presents a new low complexity edge-directed image interpolation algorithm. The algorithm uses structure tensor analysis to distinguish edges from textured areas and to find local structure direction vectors....
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Retinal image Quality Assessment (RIQA) is an essential preliminarily step in Automatic Retinal Screening systems (ARSS) to avoid misdiagnosis of retinal disease. In this work, a no-reference wavelet based RIQA algori...
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