Classification of images is a significant step in pattern recognition and digital imageprocessing. It is applied in various domains for authentication, identification, defense, medical diagnosis and so on. Feature ex...
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Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. this is usually due to shadowing self-obstruction or light reflecti...
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Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. this is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details and local contrast at the same visualization level. they are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform brightness, halo effect, blur edges, and so on. this article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. the introduced approach contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. the proposed algorithm was tested on the orthorectified aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000–2016. the simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization quality. c Society for Imaging Science and Technology 2019
Spectrogram inversion or phase retrieval is an old topic in digital signal processing, that has been revisited since a few years for its proved relevance to many recent applications, such as source separation, speech ...
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Spectrogram inversion or phase retrieval is an old topic in digital signal processing, that has been revisited since a few years for its proved relevance to many recent applications, such as source separation, speech enhancement and compressive sensing. Spectrogram inversion aims to reconstruct a signal from partial spectral information, such as the magnitude spectrum or the phase spectrum only, which are obtained by the short-time Fourier transform (STFT). thus, in this work the relevance of signal reconstruction is studied. First, the proposed algorithm, based on the recent theoretic relationships between STFT magnitude and phase is presented. Secondly, the proposed method is tested on clean and simulated-noisy speech. Finally, the relevance of spectrogram inversion as implemented either in our proposal or in state-of-the-art algorithms is evaluated for the particular application on speech enhancement. the results show the advantages and the limits of using spectrogram inversion in such an application.
For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorit...
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Breast cancer is one of the most common diseases in women in the world. there are various imaging techniques employed in the diagnosis. the histological image analysis supported by computational systems has proved to ...
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ISBN:
(纸本)9781728175393
Breast cancer is one of the most common diseases in women in the world. there are various imaging techniques employed in the diagnosis. the histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. the attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms withthe 10-fold cross-validation technique. In this study, we investigated the proposed approach withimages obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. the proposed approach achieved significant results of AUC and accuracy for all cases analyzed. the proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. these results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue.
Falling down is one of the main reasons for hospitalization among the elderly. Constant monitoring of such vulnerable older adults and timely detection of fall incidents may significantly improve healthcare services. ...
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ISBN:
(数字)9781728170442
ISBN:
(纸本)9781728170442
Falling down is one of the main reasons for hospitalization among the elderly. Constant monitoring of such vulnerable older adults and timely detection of fall incidents may significantly improve healthcare services. this paper presents a radar-based fall detection method using compressed features of the radar signals. the compressed features are obtained by using determinisitc row and column sensing. the time-frequency analysis is first performed on the radar time series and resulting spectrogram is projected onto a binary image representation. the binary images are then compressed using a 2D deterministic sensing technique by preserving the aspect ratio of the images in the compressed domain. the performance of the proposed method is evaluated using several classifiers such as support vector machine, nearest neighbors, linear discriminant analysis and decision tree. It is shown that the proposed compressive sensing based method can improve fall versus non-fall activities recognition, as evidenced by high classification metrics for low compression ratios.
By optimizing the data layout ahead-of-Time, graph reordering can effectively improve the memory access locality in graph processing. the reordered graphs derived by sophisticated graph reordering approaches can great...
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Low delay is an important issue in a variety of applications on heterogeneous computing platforms. A great number of algorithms including heuristics as well as metaheuristics have been designed to minimize the complet...
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From military imaging to sharing private pictures, confidentiality, integrity and authentication of images play an important role in the Internet of modern world. AES is currently one of the most famous symmetric cryp...
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In this paper, we will introduce a novel low-cost, small size, portable nail printer. the usage of this system is to print any desired pattern on a finger nail in just a few minutes. the detailed pre-processing proced...
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