Multi-camera transfer GAN for person re-identification

Zhou, Shuren; Ke, Maolin; Luo, Peng

doi:10.1016/j.jvcir.2019.01.029

Cited by 110 publications

(47 citation statements)

References 2 publications

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“…PSNR is used as an objective evaluation index of image quality, which is measured by calculating the error between corresponding pixels. The PSNR's unit is decibel (dB) [16]. The larger the value, the smaller the image distortion.…”

Section: (2) Evaluation Standardsmentioning

confidence: 99%

“…In sparse representation based on the interrelated approach, Yang et al [11] and Yang et al [12] reconstructed HR image blocks and HR images by strengthening the similarity between LR and HR image blocks and their corresponding real dictionaries, so that the sparse representation of the LR block and the super-completed HR dictionary can be used to reconstruct HR image blocks and then connect HR images. A complete high-resolution image is obtained like a block [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

et al. 2019

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For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution algorithm based on dual-channel convolutional neural networks (DCCNN). The novel structure of the network model was divided into a deep channel and a shallow channel. The deep channel was used to extract the detailed texture information from the original image, while the shallow channel was mainly used to recover the overall outline of the original image. Firstly, the residual block was adjusted in the feature extraction stage, and the nonlinear mapping ability of the network was enhanced. The feature mapping dimension was reduced, and the effective features of the image were obtained. In the up-sampling stage, the parameters of the deconvolutional kernel were adjusted, and high-frequency signal loss was decreased. The high-resolution feature space could be rebuilt recursively using long-term and short-term memory blocks during the reconstruction stage, further enhancing the recovery of texture information. Secondly, the convolutional kernel was adjusted in the shallow channel to reduce the parameters, ensuring that the overall outline of the image was restored and that the network converged rapidly. Finally, the dual-channel loss function was jointly optimized to enhance the feature-fitting ability in order to obtain the final high-resolution image output. Using the improved algorithm, the network converged more rapidly, the image edge and texture reconstruction effect were obviously improved, and the Peak Signal-to-Noise Ratio (PSNR) and structural similarity were also superior to those of other solutions.

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Section: (2) Evaluation Standardsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

et al. 2019

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“…This algorithm has more accurate detection results and rotation invariance, so it is widely used in practical applications. The basic idea of Harris algorithm is: first take a partial detection window on the image, and check the average gray level change of the window when the window moves in all directions in a small range [40]. Once a certain threshold is exceeded, the central pixel can be considered a corner point.…”

Section: Harris Algorithmmentioning

confidence: 99%

Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features

Xing

2020

IEEE Access

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Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the "one-to-many" rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image.The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.

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“…In the future, we can combine the tensor model [54] and the shape-adaptive technique [55,56] to explore the spatial-spectral information adaptively and sufficiently. Deep-learning approaches have recently gained great attention in many fields [57][58][59][60][61][62][63][64]. It will be a new task to design a deep architecture to improve the performance of the HSI SR.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double ℓ 1 Constraint

Tang

Huang

et al. 2019

Remote Sensing

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Hyperspectral image (HSI) super-resolution (SR) is an important technique for improving the spatial resolution of HSI. Recently, a method based on sparse representation improved the performance of HSI SR significantly. However, the spectral dictionary was learned under a fixed size, empirically, without considering the training data. Moreover, most of the existing methods fail to explore the relationship among the sparse coefficients. To address these crucial issues, an effective method for HSI SR is proposed in this paper. First, a spectral dictionary is learned, which can adaptively estimate a suitable size according to the input HSI without any prior information. Then, the proposed method exploits the nonlocal correlation of the sparse coefficients. Doubleregularized sparse representation is then introduced to achieve better reconstructions for HSI SR. Finally, a high spatial resolution HSI is generated by the obtained coefficients matrix and the learned adaptive size spectral dictionary. To evaluate the performance of the proposed method, we conduct experiments on two famous datasets. The experimental results demonstrate that it can outperform some relatively state-of-the-art methods in terms of the popular universal quality evaluation indexes.

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Multi-camera transfer GAN for person re-identification

Cited by 110 publications

References 2 publications

RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features

Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double ℓ 1 Constraint

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