作者机构：
[Hui-huang Zhao] College of Computer Science and technology, Hengyang Normal University, Hengyang;421008, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang;[Han Liu] School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Cardiff;CF24 3AA, United Kingdom
通讯机构：
[Liu, H.] S;School of Computer Science and Informatics, Queen’s Buildings, 5 The Parade, United Kingdom
通讯机构：
[Zhao, Hui-Huang] H;Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Hunan, Peoples R China.;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词：
Compressed sensing;Convex optimization;Image enhancement;Image reconstruction;Signal to noise ratio;Adaptive;Adaptive methods;Block Compressive Sensing;Computational costs;Convex optimization problems;Gradient based;Peak signal to noise ratio;Sparsity;Image compression
摘要：
This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS_SP) methodology for noisy image compression and reconstruction. The AGbBCS_SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, the commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS_SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms.
作者机构：
[Zhao, Hui-Huang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Lai, Yu-Kun; Rosin, Paul L.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.;[Wang, Yao-Nan] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
通讯机构：
[Zhao, Hui-Huang] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词：
Deep neural networks;Style transfer;Soft mask;Semantic segmentation
摘要：
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques.
期刊：
The Journal of Engineering,2019年2019(23):8923-8926 ISSN：2051-3305
通讯作者：
Huihuang Zhao
作者机构：
Department of Computer Science and Technology, College of Computer Science and Technology, Hengyang Normal University, Hengyang, Hunan, People's Republic of China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, People's Republic of China;[Yun Zhang] Institute of Radio and TV Technology, Communication University of Zhejiang, 310018 Hangzhou, People's Republic of China;[Yaonan Wang] Department of Electrical and Engineering, College of Electrical and Information Engineering, Hunan University, Changsha, People's Republic of China;[Zhijun Qiao] School of Mathematical and Statistical Sciences, University of Texas, Rio Grande Valley, TX, USA
通讯机构：
[Huihuang Zhao] D;Department of Computer Science and Technology, College of Computer Science and Technology, Hengyang Normal University, Hengyang, Hunan, People's Republic of China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, People's Republic of China
摘要：
This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l 1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.
摘要：
Glass bottles are widely used as containers in the food and beverage industry, especially for beer and carbonated beverages. As the key part of a glass bottle, the bottle bottom and its quality are closely related to product safety. Therefore, the bottle bottom must be inspected before the bottle is used for packaging. In this paper, an apparatus based on machine vision is designed for real-time bottle bottom inspection, and a framework for the defect detection mainly using saliency detection and template matching is presented. Following a brief description of the apparatus, our emphasis is on the image analysis. First, we locate the bottom by combining Hough circle detection with the size prior, and we divide the region of interest into three measurement regions: central panel region, annular panel region, and annular texture region. Then, a saliency detection method is proposed for finding defective areas inside the central panel region. A multiscale filtering method is adopted to search for defects in the annular panel region. For the annular texture region, we combine template matching with multiscale filtering to detect defects. Finally, the defect detection results of the three measurement regions are fused to distinguish the quality of the tested bottle bottom. The proposed defect detection framework is evaluated on bottle bottom images acquired by our designed apparatus. The experimental results demonstrate that the proposed methods achieve the best performance in comparison with many conventional methods.
摘要：
This paper provides a novel method that can achieve better results in solder joint imagery compression and reconstruction. Wavelet packet decomposition is used to generate some frequency coefficients of images. The higher and lower frequency coefficients of the reconstruction signal are used separately to improve the reconstruction performance. A threshold that only relates to the higher frequency coefficients is defined to remove the noise in the reconstruction result in each iteration. A new control factor is further defined to control the threshold value. The control factor relates to the wavelet packet low-frequency coefficients and is updated by the wavelet packet low-frequency coefficients in each iteration. The experimental results reveal that the proposed algorithm is able to improve the performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with classical algorithms in reconstruction of different types of solder joint images. When the sample rate is increased, the proposed method improves the reconstruction results and maintains low computational cost. The proposed algorithm can retain more image structure and achieve better results than some common methods.
通讯机构：
[Zhao, Hui-Huang] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421008, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421008, Hunan, Peoples R China.
关键词：
Deep neural networks;gram matrix;local patch;Markov random field;style transfer
摘要：
This paper presents a new image synthesis method for image style transfer. For some common methods, the textures and colors in the style image are sometimes applied inappropriately to the content image, which generates artifacts. In order to improve the results, we propose a novel method based on a new strategy that combines both local and global style losses. On the one hand, a style loss function based on a local approach is used to keep the style details. On the other hand, another style loss function based on global measures is used to capture more global structural information. The results on various images show that the proposed method reduces artifacts while faithfully transferring the style image's characteristics and preserving the structure and color of the content image.
作者：
Zhao, Hui-huang*;Liu, Han;Zheng, Jin-Hua;Fu, Bin
期刊：
2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018),2018年:1-7
通讯作者：
Zhao, Hui-huang
作者机构：
[Zhao, Hui-huang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Zheng, Jin-Hua; Zhao, Hui-huang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Hunan, Peoples R China.;[Liu, Han] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, S Glam, Wales.;[Fu, Bin] Univ Texas Rio Grande Valley, Dept Comp Sci, Edinburg, TX USA.
通讯机构：
[Zhao, Hui-huang] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Hunan, Peoples R China.
会议名称：
2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
摘要：
This paper presents an occlusion robust tracking (ORT) method for multiple faces tracking. Given a video having multiple faces, we firstly detect faces in the first frame using the off-the-shelf face detector, and then extract wavelet packet transform (WPT) coefficients and color features from the detected faces, finally we design a back propagation (BP) neural network and track the faces by a particle filter and BP neural network. The main contribution is twofold. Firstly, the WPT coefficients combined with traditional color features is utilized to face tracking. It efficiently describes faces due to their discrimination and simplicity. Secondly, we propose an improved tracking method for occlusion robust tracking based on the BP neural network. When there is an occlusion, BP neural network learns from previous tracking results and is utilized to refine the current result from particle filter. Experimental results have been shown that our ORT method can handle the occlusion effectively and achieve better performance than several previous methods.
摘要：
Purpose - The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost. Design/methodology/approach - Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters. Findings - Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(kk) instead of O(1/k). Practical implications - This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery. Originality/value - According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.
