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RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking 14th

RGB-T Saliency Detection via Robust Graph Learning and Colla...

引用

14th International conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Li, Sheng Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

visual saliency detection inspired by brain cognitive mechanisms is an important component of computer vision, aiming at automatically highlight salient visual objects from the image background. In complex real scenarios, integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting saliency detection performance. In this paper, we propose a novel collaborative algorithm for saliency detection by adaptively incorporating information from grayscale and thermal images. Specifically, we first construct an affinity graph model via robust graph learning to characterize the feature similarities and location relationships between two different image patches under each modal. Based on these affinity graphs, a collaborative manifold ranking method cross-modality consistent constraints is designed to effectively and efficiently infer the salient score for each patch. Moreover, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking scheme to achieve an adaptive weighted fusion of different source data. For optimization, we propose some iterative algorithms to efficiently solve the models with several subproblems. Experiments on the grayscale-thermal datasets demonstrate the superior performance of the new method over the state-of-the-art approaches. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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Color Based image processing Techniques to Detect Oxide Film during Welding

Color Based Image Processing Techniques to Detect Oxide Film...

引用

International conference on Electronics, Information and communications (ICEIC)

作者： Shantanu Sen Gupta Tae-Ho Kwon Ki-Doo Kim School of Electrical Engineering Kookmin University Seoul Korea

ISBN: (数字)9781728162898

ISBN: (纸本)9781728162904

From naval to aerospace and industry to household, everywhere the necessity of aluminum is felt. But oxide film can be formed on this metal and its alloys during casting or welding due to high affinity of aluminum for oxygen. This phenomenon can further lead to porosity which is a vital defect. visual inspection is not sufficient due to different human constraints. So, many image processing techniques have been developed. For better welding, we propose here two useful and efficient techniques. Unlike most of the previous techniques, our proposed techniques are color based processing. In this manuscript, our main goal is to detect oxide film and we show that the task is successfully done by our methods.

关键词： automatic optical inspection image colour analysis porosity production engineering computing welding Welding automatic optical inspection production engineering computing OXIDE FILMS image colour analysis porosimetry image processing techniques aluminium Navy

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Deep Near Infrared Colorization with Semantic Segmentation and Transfer Learning

Deep Near Infrared Colorization with Semantic Segmentation a...

引用

IEEE visual communications and image processing (VCIP)

作者： Fengqiao Wang Lu Liu Cheolkon Jung School of Electronic Engineering Xidian University China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Although near infrared (NIR) images contain no color, they have abundant and clear textures. In this paper, we propose deep NIR colorization with semantic segmentation and transfer learning. NIR images are capable of capturing invisible spectrum (700-1000 nm) that is quite different from visible spectrum images. We employ convolutional layers to build relationship between single NIR images and three-channel color images, instead of mapping to Lab or YCbCr color space. Moreover, we use semantic segmentation as global prior information to refine colorization of smooth regions for objects. We use color divergence loss to further optimize NIR colorization results with good structures and edges. Since the training dataset is not enough to capture rich color information, we adopt transfer learning to get color and semantic information. Experimental results verify that the proposed method produces a natural color image from single NIR image and outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

关键词： image color analysis Semantics image segmentation Gray-scale Feature extraction Color Logic gates

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Advertisement logo compositing via adversarial geometric consistency pursuit 34

Advertisement logo compositing via adversarial geometric con...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Li, Xiang Teng, Guowei An, Ping Yao, Haiyan Chen, Yilei School of Communication and Information Engineering Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai China Anyang Institute of Technology Henan China

ISBN: (纸本)9781728137230

Advertisement logo compositing is aiming to embed some specified logos in a suitable position of target images with alike geometric distortion and same appearance characteristic. Unsupervised learning has gained considerable attention recently. To synthetically address the problems about geometric distortion and appearance realism, we propose a novel learnable module, the adversarial geometric consistency pursuit model (AGCP), which explicitly allows seamless image compositing. On one hand, we design an adversarial structure that generates composite images taking geometric correction and appearance harmonization into account. Our proposed adversarial learning approach is able to obtain better harmonization in the region of interest. On the other hand, a novel geometric consistency pursuit loss is designed which encourages the network to learn the warp parameters of target images while preserving the feature of the source object. Our comparative evaluation demonstrates the effectiveness of the proposed method. © 2019 IEEE.

关键词： image segmentation

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A Novel Automated Method for Detection of Eye Diseases Using image processing on Retinal images 5th

A Novel Automated Method for Detection of Eye Diseases Using...

引用

5th International conference on Information, Communication and Computing Technology, ICICCT 2020

作者： Mourya, Amit Kumar Verma, Lalita Electronics and Communication Department Mangalmay Institute of Engineering and Technology Greater Noida India Computer Science and Engineering Mangalmay Institute of Engineering and Technology Greater Noida India

ISBN: (纸本)9789811596704

More than 2.2 billion of people are losing their vision of eye worldwide. These people have vision infirmity due to Cataract, Diabetic retinopathy, Glaucoma, refractive error, macular degeneration etc. In India also there are more than 80 million of people are suffering from moderate visual infirmity. Major problems in the eye diseases are no early symptoms and high cost of treatment, so the diseases get severe. In this paper, I have proposed an automated method of detection of eye diseases in early stages. So the people can cure them before they become severe and incurable. I have considered three main factors which are responsible for vision loss. These factors are Glaucoma, Macular Degeneration and Diabetic Retinopathy. For detection of these diseases Glaucoma, Macular Degeneration and Diabetic Retinopathy several features are extracted which are cup to disk ratio, extraction of blood vessels, optic cup segmentation, optic disk segmentation, brighter part extraction etc. This proposed method has achieved very good results which are verified by the experts. Results mark that the proposed method has very fine accuracy and effectiveness in detection of these diseases over the pre-existing method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Densely Connected AutoEncoders for image Compression 19

Densely Connected AutoEncoders for Image Compression

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2nd International conference on image and Graphics processing (ICIGP) / 5th International conference on Virtual Reality

作者： Song Zebang Sei-ichiro, Kamata Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781450360920

image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.

关键词： Lossy image compression U-net like structure Densely AutoEncoders convolutional neural networks (CNNs)

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

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IEEE visual communications and image processing (VCIP)

作者： Tingting Zhong Xin Jin Kedeng Tong Shenzhen Key Lab of Broadband Network and Multimedia Tsinghua Shenzhen International Graduate School Shenzhen China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient Square rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.

关键词： image coding Transform coding Spatial resolution Three-dimensional displays Convolutional codes Training Decoding

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

IEEE visual communications and image processing (VCIP)

作者： Shuo Gao Yueyi Zhang Dong Liu Zhiwei Xiong University of Science and Technology of China Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Three-dimensional displays image coding Solid modeling Brain modeling Two dimensional displays Context modeling Correlation

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Learning Redundant Sparsifying Transform based on Equi-Angular Frame

Learning Redundant Sparsifying Transform based on Equi-Angul...

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IEEE visual communications and image processing (VCIP)

作者： Min Zhang Yunhui Shi Xiaoyan Sun Nam Ling Na Qi Beijing Advanced Innovation Center for Future Internet Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing China University of Science and Technology of China Hefei China Santa Clara University Santa Clara California U.S.A

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Due to the fact that sparse coding in redundant sparse dictionary learning model is NP-hard, interest has turned to the non-redundant sparsifying transform as its sparse coding is computationally cheap. However, natural images typically contain diverse textures that cannot be sparsified well by a non-redundant system. In this paper we propose a new approach for learning redundant sparsifying transform based on equi-angular frame, where the frame and its dual frame are corresponding to applying the forward and the backward transforms. The uniform mutual coherence in the sparsifying transform is enforced by the equi-angular constraint, which better sparsifies diverse textures. In addition, an efficient algorithm is proposed for learning the redundant transform. Experimental results for image representation illustrate the superiority of our proposed method over non-redundant sparsifying transforms. The image denoising results show that our proposed method achieves superior denoising performance, in terms of subjective and objective quality, compared to the K-SVD, the data-driven tight frame method, the learning based sparsifying transform and the overcomplete transform model with block cosparsity (OCTOBOS).

关键词： Transforms Computational modeling image denoising Adaptation models Signal processing algorithms Signal processing Encoding

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Simple ConvNet Based on Bag of MLP-Based Local Descriptors 26th

Simple ConvNet Based on Bag of MLP-Based Local Descriptors

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26th International conference on Neural Information processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Kobayashi, Takumi Ide, Hidenori Watanabe, Kenji Natl Inst Adv Ind Sci & Technol Umezono 1-1-1 Tsukuba Ibaraki Japan

ISBN: (纸本)9783030368081;9783030368074

Deep convolutional neural network (ConvNet) is applied to versatile image recognition tasks with great success, though demanding high computation cost. Toward efficient computation, we propose a simple ConvNet architecture based on local descriptors in the bag-of-features framework. The local descriptors are formulated in a simple form of MLP and thus are efficiently computed on various ROI in a flexible manner. The proposed method is effectively trained in an end-to-end manner by reformulating the MLP descriptor into the form of deep ConvNet stacking convolution layers linearly. Through projection-based visual word encoding, the local descriptors are aggregated and fed into a classifier for image recognition tasks, which enables us to compute the network forwarding pass by matrix-vector multiplication. In the experiments on image classification, the proposedmethod is analyzed thoroughly, exhibiting favorable generalization performance on various tasks.

关键词： image recognition

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