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ATSal: An Attention Based Architecture for Saliency Prediction in 360 ∘ Videos 25th

ATSal: An Attention Based Architecture for Saliency Predicti...

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25th International conference on Pattern Recognition Workshops, ICPR 2020

作者： Dahou, Yasser Tliba, Marouane McGuinness, Kevin O’Connor, Noel Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland Institut National des Télécommunications et des TIC Oran Algeria

ISBN: (纸本)9783030687953

The spherical domain representation of 360 ∘ video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV). Models of human visual attention can be used so that only a single viewport is rendered at a time, which is important when developing systems that allow users to explore ODV with head mounted displays (HMD). Accordingly, researchers have proposed various saliency models for 360 ∘ video/images. This paper proposes ATSal, a novel attention based (head-eye) saliency model for 360 ∘ videos. The attention mechanism explicitly encodes global static visual attention allowing expert models to focus on learning the saliency on local patches throughout consecutive frames. We compare the proposed approach to other state-of-the-art saliency models on two datasets: Salient360! and VR-EyeTracking. Experimental results on over 80 ODV videos (75K+ frames) show that the proposed method outperforms the existing state-of-the-art. © 2021, Springer Nature Switzerland AG.

关键词： Helmet mounted displays

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image Super-Resolution Based on Multi-scale Fusion Network

Image Super-Resolution Based on Multi-scale Fusion Network

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7th International conference on communications, Signal processing, and Systems (CSPS)

作者： Lin, Leping Huang, Huiling Ouyang, Ning Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Minist Educ Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9789811365041;9789811365034

It is important and necessary to obtain high-frequency information and texture details in the image reconstruction applications, such as image super-resolution. Hence, it is proposed the multi-scale fusion network (MCFN) in this paper. In the network, three pathways are designed for different receptive fields and scales, which are expected to obtain more texture details. Meanwhile, the local and global residual learning strategies are employed to prevent overfitting and to improve reconstruction quality. Compared with the classic convolutional neural network-based algorithms, the proposed method achieves better numerical and visual effects.

关键词： Multi-pathways Residual learning Multi-scale Receptive field Texture details

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A Systematic Literature Review on image Captioning 25th

A Systematic Literature Review on Image Captioning

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25th International conference on Human-Computer Interaction, HCII 2023

作者： Ding, Yi Tongji University Shanghai200092 China

ISBN: (纸本)9783031360039

image captioning is a technology that generates textual descriptions of images by integrating computer vision and natural language processing. This review aims to provide a comprehensive overview of current state-of-the-art image captioning models and techniques, summarize the critical approaches, evaluate their performance on benchmark datasets, highlight main challenges and limitations, and provide insights into future research directions. The systematic literature review followed a PRISMA-based approach and formulated three research questions (RQs) to construct the review. The study extracted and synthesized data from 49 eligible papers on visual and language models, datasets employed, and evaluation mechanisms used to measure performance. We found that it is evident that large-scale models and pre-training strategies have significantly advanced the field of image captioning. Specific sub-tasks for image captioning in certain applications have also contributed to the development of unique datasets. Future research directions in image captioning can be focused on solving the issues of expensive and scarce paired datasets, as well as shifting towards user-centric sub-tasks from generalization. This review can serve as a valuable resource for researchers, practitioners, and enthusiasts interested in image captioning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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visual Privacy Protection via Mapping Distortion

Visual Privacy Protection via Mapping Distortion

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Yiming Li Peidong Liu Yong Jiang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University PCL Research Center of Networks and Communications Peng Cheng Laboratory

Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios. In this paper, we propose the mapping distortion based protection (MDP) and its augmentation-based extension (AugMDP) to protect the data privacy by modifying the original dataset. In the modified dataset generated by MDP, the image and its label are not consistent (e.g., a cat-like image is labeled as the dog), whereas the DNNs trained on it can still achieve good performance on benign testing set. As such, this method can protect privacy when the dataset is leaked. Extensive experiments are conducted, which verify the effectiveness and feasibility of our method.

关键词： Training Data privacy visualization Privacy image analysis Acoustic distortion Speech recognition

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Edge-based visual Odometry with Stereo Cameras using Multiple Oriented Quadtrees

Edge-based Visual Odometry with Stereo Cameras using Multipl...

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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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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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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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