image denoising is one of the basic low-level computervision problems, but low-light denoising is challenging due to low photon count and low SNR. Therefore, we propose an end-to-end encoded and decoded network of il...
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ISBN:
(纸本)9781450376259
image denoising is one of the basic low-level computervision problems, but low-light denoising is challenging due to low photon count and low SNR. Therefore, we propose an end-to-end encoded and decoded network of illumination compensation and image denoising in low-light condition based on deep learning, which is used to denoise low-light images and adaptively brighten images without over-amplifying the brighter part of the images with high dynamic *** the network, the illumination compensation branch network eliminates the disadvantage that the magnification must be selected externally. Different simulation gain and exposure time are used to train the multi-light compensation coefficient, which can eliminate the residual errors caused by inaccurate gain and various exposure time effectively. The results show that the model is suitable for the recovery and reconstruction of natural low-light images with different degrees of degradation due to the advantages of flexibility and data driving.
The brittle graphite machined surface quality cannot be completely evaluated only by surface roughness Ra measured by profilometer, owing to its 3D surface defects. A digital image detection method based on gray-level...
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ISBN:
(纸本)9781538684818
The brittle graphite machined surface quality cannot be completely evaluated only by surface roughness Ra measured by profilometer, owing to its 3D surface defects. A digital image detection method based on gray-level co-occurrence matrix was put forwards to detect the graphite surface quality. The results show that two feature parameters of gray-level co-occurrence matrix, including Secondary moment ASM and Entropy ENT, had good positive relevance with surface roughness Ra. The linear regression functions of ASM and ENT were established to fit Ra, which could be used to predict Ra by imageprocessing method. This method also gives a feasible way to develop a machine vision detection system to automatically detect the graphite surface quality by digital imageprocessing.
In this paper, we present a novel method to calculate trifocal tensor based on hybrid particle swarm optimization. This method takes pole coordinates in three views as particles and the fitness function is to minimize...
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ISBN:
(纸本)9781450390040
In this paper, we present a novel method to calculate trifocal tensor based on hybrid particle swarm optimization. This method takes pole coordinates in three views as particles and the fitness function is to minimize geometric error. The proposed method is evaluated both in synthetic and real data. Experiments show that our method is more robust and accuracy than other typical methods. Rotation matrices and translation vectors estimated by the proposed method have high precision compared with ground truth data.
image restoration is a key step in the field of imageprocessing. Total Variation (TV) model is widely applied in image denoising because it preserves edges and image details. However, TV model has some shortcomings, ...
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One of the key problems of computervision and automated surveillance is to determine if two snapshots of objects in a video feed correspond to the same real one. In this paper we propose an efficient GPGPU based syst...
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ISBN:
(纸本)9781467325851;9781467325837
One of the key problems of computervision and automated surveillance is to determine if two snapshots of objects in a video feed correspond to the same real one. In this paper we propose an efficient GPGPU based system for short-term matching of people in a video feed. The main contributions of our approach consist of image enhancement techniques, data preprocessing methods based on statistical sampling combined with local algorithms for finding Voronoi diagrams and efficient similarity metric based on non crossing maximum matchings in weighted graphs. Our algorithms, thanks to their local nature, are easily parallelized. We propose an implementation on GPGPU that allows real time computation in reasonable circumstances. Achieved results show that described algorithms may be used in a variety of contexts.
The edges of an image can be detected at different scales from the local maxima of its wavelet transform. An algorithm is described that reconstructs images from their edges at dyadic scales. The wavelet maxima repres...
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ISBN:
(纸本)0818620579
The edges of an image can be detected at different scales from the local maxima of its wavelet transform. An algorithm is described that reconstructs images from their edges at dyadic scales. The wavelet maxima representation is a novel reorganization of the image information that makes it possible to develop algorithms uniquely based on edges for solving imageprocessing and computervision problems. The evolution of the wavelet maxima across scales gives a precise characterization of the edge type which can be used for pattern recognition. A coding algorithm is described that selects the most important image edges in order to obtain a compact representation.
image captioning is a research hotspot in the field of image understanding. It spans the fields of computervision and natural language processing. Most of the image captioning researches use deep learning technology,...
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Aircraft, as a special strategic target, has high value in both civil and military use, and it is especially important to achieve intelligent scientific aircraft target detection compared to traditional methods. There...
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Traditional target detection methods have poor accuracy when processing lower resolution images with many pedestrians, particularly for small and blurry targets whose features are not manifested as significantly as th...
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In this paper we propose a new post-processing approach for dimensionality reduction methods based on multidimensional ensemble empirical mode decomposition (MEEMD). In the proposed method, the features are decomposed...
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ISBN:
(纸本)9781538605516
In this paper we propose a new post-processing approach for dimensionality reduction methods based on multidimensional ensemble empirical mode decomposition (MEEMD). In the proposed method, the features are decomposed into different components and then we maximize the dependency and the dispersion between classes thanks to Gaussian filter and Butterworth filter. The performance of the proposed algorithm is demonstrated in experiments by several dimensionality reduction techniques on two public databases.
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