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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Kim, Changhyeon Kim, Junha Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul South Korea Automat & Syst Res Inst ASRI Seoul South Korea

ISBN: (纸本)9781728162126

We propose an efficient edge-based stereo visual odometry (VO) using multiple quadtrees created according to image gradient orientations. To characterize edges, we classify them into eight orientation groups according to their image gradient directions. Using the edge groups, we construct eight quadtrees and set overlapping areas belonging to adjacent quadtrees for robust and efficient matching. For further acceleration, previously visited tree nodes are stored and reused at the next iteration to warm-start. We propose an edge culling method to extract prominent edgelets and prune redundant edges. The camera motion is estimated by minimizing point-to-edge distances within a re-weighted iterative closest points (ICP) framework, and simultaneously, 3-D structures are recovered by static and temporal stereo settings. To analyze the effects of the proposed methods, we conduct extensive simulations with various settings. Quantitative results on public datasets confirm that our approach has competitive performance with state-of-the-art stereo methods. In addition, we demonstrate the practical values of our system in author-collected modern building scenes with curved edges only.

关键词： Stereo image processing

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Messaging Beyond Texts with Real-time image Suggestions 20

Messaging Beyond Texts with Real-time Image Suggestions

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22nd International conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI)

作者： Kim, Joongyum Gong, Taesik Han, Kyungsik Kim, Juho Ko, JeongGil Lee, Sung-Ju Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Ajou Univ Dept Comp Sci Suwon South Korea Yonsei Univ Sch Integrated Technol Seoul South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781450375160

While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce MilliCat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat's realtime image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19x improvement) and consequently more frequent image usage (1.8x) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience.

关键词： Mobile Chat image Recommendations visual communications

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Editorial-Special Issue on Molecular communications for Diagnostics and Therapeutic Development of Infectious Diseases

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IEEE TRANSACTIONS ON MOLECULAR BIOLOGICAL AND MULTI-SCALE communications 2021年第3期7卷 117-120页

作者： Balasubramaniam, Sasitharan Barros, Michael Taynnan Veletic, Mladen Kanada, Masamitsu Pierobon, Massimiliano Vainio, Seppo Balasingham, Ilangko Univ Nebraska Lincoln Dept Comp Sci & Engn Lincoln NE 68588 USA Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Oslo Univ Hosp Intervent Ctr N-0372 Oslo Norway Michigan State Univ Dept Pharmacol & Toxicol E Lansing MI 48824 USA Univ Oulu Biochem & Mol Med Oulu 90570 Finland Norwegian Univ Sci & Technol Dept Elect Syst N-7491 Trondheim Norway

The COVID-19 pandemic in the last year has brought along numerous challenges on all fronts for humanity. Besides the high number of fatalities, economic and societal impact, the virus has also raised a question on the current approaches and methodologies for combatting pandemics. An important factor in this global fight against the pandemic, is the fact that key experts within the fields of biotechnology, virology and immunology have to return to the drawing board to develop new approaches to treat the virus. This has triggered a multi and inter-disciplinary research drive to bring in new concepts, techniques and methodologies that can be used to understand the virus in order to develop novel treatment techniques. The field of molecular communications, which is just over a decade old, is one area of research in the field of communications and networking that can contribute towards understanding of virus and their infection process. This is the aim of this special issue, where we have collected a number of publications from key researchers in the field of molecular communications that can contribute towards understanding the viral properties and behavior, which can be used to support the current as well as future pandemics.

关键词： Special issues and sections Molecular communication (telecommunication) Infectious diseases Pandemics COVID-19 Modeling

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The principles of organizing the search for an object in an image, tracking an object and the selection of informative features based on the visual perception of a person 3rd

The principles of organizing the search for an object in an ...

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3rd International conference on Data Stream and Mining and processing, DSMP 2020

作者： Boyun, Vitaliy National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9783030616557

A significant expansion of the scope of computer vision, in particular in real-time systems, places very high demands on them in terms of productivity and efficiency of information processing, and in feedback systems, it also requires information lag in it. Such requirements are not ensured by traditional approaches. The way out of the situation may be to use as a prototype the principles of organization of the human visual system, which has a very high selectivity of perception of video information. The paper presents a generalized dynamic model of the organization of these principles. It is proposed to use them to organize the search for an object in a coarse image of a scene, to track an object and, if necessary, to carry out its classification or recognition at a more detailed level. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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A Novel Deep Hybrid Spectral Network for Hyperspectral image Classification

A Novel Deep Hybrid Spectral Network for Hyperspectral Image...

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International conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication, MDCWC 2020

作者： Priyadharshini, K. Sathya Bama, B. Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9789811602887

image classification is the process of allocating land cover classes to picture elements. Hyperspectral image classification of land cover is not easy due to the problematic variability among the samples for training count of band spectra. Deep learning has network which is capable of learning from unstructured or unlabeled data without supervision. The convolutional neural network (CNN) is one of the most widely employed approaches for visual data processing based on deep learning. To classify the hyperspectral data, a hybrid network with 2D and 3D CNN is developed. The spectral information and spatial information are used together for hyperspectral image analysis that enhances the experiment result considerably. With pixel as the basic analysis unit, classification technique of convolutional neural network has been developed. Principal component analysis (PCA) is implemented which lowers the dimensionality of hyperspectral data. PCA implementation reduces feature size to increase computational efficiency. The first principal component has a superior function, as it has the highest variance compared to the other components. Three hyperspectral datasets are used for the analysis such as Pavia university, Indian pines, and Salinas scene. Hyperspectral data of Indian Pines is obtained from the Northwest Indiana on June 1992 by Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Indian pine data has the size (145 × 145) pixels. The Pavia university is obtained from Reflective Optics System Imaging Spectrometer (ROSIS) Pavia, Northern Italy, in 2001. The data has 103 spectral bands with the size of 610 × 340 pixels. The Salinas scene is obtained from the AVIRIS Salinas Valley, CA, USA, in 1998, with 512 × 217 spatial dimension. The hybrid CNN is computationally efficient compared to the 3D CNN and for minimum training data it provides enhanced performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Reduced-Reference Stereoscopic image Quality Assessment Based on Entropy of Gradient Primitives 5

Reduced-Reference Stereoscopic Image Quality Assessment Base...

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5th IEEE International conference on Signal and image processing, ICSIP 2020

作者： Ma, Jian Zhao, Xinxin Xu, Youxin Anhui University School of Internet China Anhui Xinhua University School of Electronics and Communications Engineering China Marvell Technology Group Ltd. Bermuda

ISBN: (纸本)9781728168968

Stereoscopic image quality assessment (SIQA) has always been challenging due to the remarkable distinction between human monocular and binocular vision. This paper proposes a novel gradient-based dictionary learning method for SIQA, which effectively integrates the gradients are sparser than the image itself. Specifically, we first compute the gradient maps of each view image of stereopair by applying contrast sensitivity of human visual system (HVS) and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. Afterwards, the binocular perceptual information of gradient (GBPI) is represented by the distribution statistics of visual primitives in gradient maps of left and right views' images, which are extracted by sparse representation. Furthermore, the entropy of gradient maps of each views' images are utilized to represent monocular cue. Their mutual information is used to represent binocular cue. The difference of the reference and distorted images' GBPIs is taken as quality-ware features. Finally, the kernel ridge regressing (KRR) is utilized to simulate a nonlinear relationship between the quality-ware features and human opinions. The performance of the proposed metric is evaluated over the LIVE 3D phase II asymmetric datasets, and shown to be competitive with the state-of-the-art SIQA algorithms. © 2020 IEEE.

关键词： Entropy

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External Sparsity Combined with Internal Low-Rankness for image Denoising

External Sparsity Combined with Internal Low-Rankness for Im...

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International conference on Computer and communications (ICCC)

作者： Yang Ou Bailin Li Jianqiao Luo Jiacai Liao School of Mechanical Engineering Southwest Jiaotong University Chengdu China College of Mechanical and Vehicle Engineering Hunan University Changsha China

ISBN: (纸本)9781665409513

Nonlocal self-similarity (NSS) as a remarkable prior has been successfully applied to image processing including image denoising. For each local patch in an image, we can search for many nonlocally similar patches under NSS prior and stack them into a group, and then process the group instead of a patch to better capture the nonlocal structure. Most existing methods consider the NSS either of the internal degraded image or from an external clean corpus, which may limit the denoising performance. In this paper, we propose a hybrid model to catch both the external and internal NSS. Specifically, we develop a model with two regularizers, the internal NSS is exploited by a low-rankness regularizer, and the external NSS is exploited by a sparse regularizer. An alternating minimization method is developed to solve our model. Experimental results demonstrate that our algorithm achieves better results compared with several state-of-the-art denoising methods.

关键词： Minimization methods image coding Computational modeling Noise reduction visual effects Noise measurement image denoising

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Modeling a Low Vision Observer: Application in Comparison of image Enhancement Methods 22nd

Modeling a Low Vision Observer: Application in Comparison of...

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22nd International conference on Human-Computer Interaction, HCI International 2020

作者： Walbrecq, Cédric Lafon-Pham, Dominique Marc, Isabelle EuroMov Digital Health in Motion University of Montpellier IMT Mines Ales Ales France

ISBN: (纸本)9783030607029

Numerous image processing methods have been proposed to help low vision people, often relied on contrast enhancement algorithms. Their assessment is usually performed by tests on low vision subjects, which are expensive and time consuming. This paper presents a low vision observer model, fully customizable to fit various impaired visual performances, which may be used for early algorithm assessment, and avoiding unnecessary human tests. This model is fitted to visual performances of a subject with degenerative retinal disease, and applied to images processed by two edge enhancement algorithms, allowing to explain their performances in terms of blur reduction and color saturation improvement. © 2020, Springer Nature Switzerland AG.

关键词： image enhancement

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Malware Classification Based on GAF visualization of Dynamic API Call Sequences

Malware Classification Based on GAF Visualization of Dynamic...

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International conference on Communication Technology (ICCT)

作者： Hongmei Zhang Xiaoqian Yun Xiaofang Deng Xiaoxiong Zhong School of Information and Communication Guilin University of Electronic Technology Guilin China Peng Cheng Laboratory Shenzhen China

Due to the constant updates of malware and its variants and the continuous development of malware obfuscation techniques. Malware intrusions targeting Windows hosts are also on the rise. Traditional static analysis methods such as signature matching mechanisms have been difficult to adapt to the detection of new malware. Therefore, a novel visual detection method of malware is proposed for first-time to convert the Windows API call sequence with sequential nature into feature images based on the Gramian Angular Field (GAF) idea, and train a neural network to identify malware. The experimental results demonstrate the effectiveness of our proposed method. For the binary classification of malware, the GAF visualization image of the API call sequence is compared with its original sequence. After GAF visualization, the classification accuracy of the classic machine learning model MLP is improved by 9.64%, and the classification accuracy of the deep learning model CNN is improved by 4.82%. Furthermore, our experiments show that the proposed method is also feasible and effective for the multi-class classification of malware.

关键词： Deep learning visualization Neural networks Static analysis Feature extraction Malware communications technology

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Research on image Super-Resolution Reconstruction Based on Generative Countermeasure Network

Research on Image Super-Resolution Reconstruction Based on G...

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1st International conference on Robotics and Rehabilitation Intelligence, ICRRI 2020

作者： Jia, Rongzhao Wang, Xiaohong Liaoning Shihua University Fushun113001 China

ISBN: (纸本)9789813349315

image super-resolution reconstruction technology is an important image processing technology used to improve image quality and video resolution in the field of computer vision. Nowadays, the main methods of super-resolution reconstruction based on convolutional neural network (CNN) are mostly to get higher peak signal-to-noise ratio (PSNR), which will produce many other problems, such as, the visual sensory effect of the image is not ideal, the gradient back propagation is difficult, the training effect of long training time is not ideal, the image lacks some high-frequency information and texture details, etc. In this paper, an improved image super-resolution reconstruction method based on generative multi anti network (GAN) is proposed. For the traditional generative adversary network, a VGG19 network assistant generator and discriminator model training are added, and the loss function is improved. Combined with the GAN adversary loss, the feature loss of the auxiliary VGG19 network is added. Experimental results show that compared with the existing methods, this method improves the overall visual effect of the reconstructed image, restores more realistic texture effect and higher MOS score under the condition of ensuring PSNR. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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