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An Improved image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy 8th

An Improved Image Super-Resolution Algorithm for Percutaneo...

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8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Li, Xue Zhou, Zihan Wang, Kaifeng Liu, Haiying Qian, Yalong Duan, Xingguang Li, Changsheng Beijing Institute of Technology 5 South Zhong Guan Cun Street Haidian District Beijing China The Department of Spinal Surgery Peking University People’s Hospital No. 11 Xizhimen South Street Xicheng District Beijing China

ISBN: (纸本)9789819980178

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Endoscopy

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A Marked Point Process Model For visual Perceptual Groups Extraction

A Marked Point Process Model For Visual Perceptual Groups Ex...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Mbarki, Amal Naouai, Mohamed Univ Tunis EL MANAR Fac Sci Tunis Tunis Tunisia

ISBN: (纸本)9781728180687

Perceptual organization is the process of assigning each part of a scene to a specified association of features to be a part of the same organization. In the twenty century, Gestalt psychologists formalized how image features tend to be grouped by giving a set of organizing principles. In this paper, we propose an approach for the detection of perceptual groups in an image. We are mainly interested in features grouped by the proximity law of Gestalt. We conceive an object-based model within a stochastic framework using a marked point process (MPP). We use a Bayesian learning method to extract perceptual groups in a scene. The proposed model tested on synthetic images proves the efficient detection of perceptual groups in noisy images.

关键词： visual perception perceptual organisation image processing marked point process

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Frequency-aware Learned image Compression for Quality Scalability

Frequency-aware Learned Image Compression for Quality Scalab...

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IEEE visual communications and image processing (VCIP)

作者： Hyomin Choi Fabien Racapé Shahab Hamidi-Rad Mateen Ulhaq Simon Feltman Interdigital Emerging Technologies Lab Los Altos CA USA School of Engineering Science Simon Fraser University Burnaby BC Canada

ISBN: (纸本)9781665475938

Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.

关键词： Wavelet transforms Training image coding Codecs visual communication Scalability Rate-distortion

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A Privacy-Preserving and End-to-End-Based Encrypted image Retrieval Scheme

A Privacy-Preserving and End-to-End-Based Encrypted Image Re...

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IEEE visual communications and image processing (VCIP)

作者： Zhixun Lu Qihua Feng Peiya Li College of Information Science and Technology Jinan University Guangzhou China School of Computer Science and Technology Beijing Insitute of Technology Beijing China College of Cyber Security Jinan University Guangzhou China

ISBN: (纸本)9781665475938

Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, a new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.

关键词： Privacy image coding visual communication image retrieval Neural networks Transform coding Transforms

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International Deployment of visual IoT for Disaster Mitigation

International Deployment of Visual IoT for Disaster Mitigati...

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Australasian Telecommunication Networks and Applications conference, ATNAC

作者： Ken T. Murata Kazutaka Kikuta Tsutomu Nagatsuma Hideo Imanaka Praphan Pavarangkoon National Institute of Information and Communications Technology Tokyo Japan School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

This paper proposes a novel methodology that utilizes a newly developed visual Internet of Things (IoT) system for resilient natural disaster mitigation. This system enables the detection of disasters through remote control functions integrated with visual IoT sensors and artificial intelligence (AI)-based image processing of images captured by these sensors. The system is designed using commercial off-the-shelf (COTS) components to reduce installation costs, making it feasible for deployment worldwide, including in developing countries. In 2023, the proposal for this system was presented to the ITUD with the aim of widespread implementation in developing nations. The paper introduces innovative methodologies for visual IoT in international disaster mitigation, accompanied by use cases and detailed technological insights.

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Machine Vision Based Fruit Classification and Grading—A Review 4th

Machine Vision Based Fruit Classification and Grading—A Rev...

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4th International conference on communications and Cyber-Physical Engineering, ICCCE 2021

作者： Chaudhari, Dipali Waghmare, Surendra G.H. Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (纸本)9789811679841

The appearance or external features is one of the important aspect, it impacts the consumer’s market value, desires, and choice but also, to a certain degree, its internal consistency. Color, texture, size, shape, and visual faults are commonly addressed while evaluating the external quality of food. Manually managing the external quality of fruit is possible, but it takes time and effort. For decades, computer vision technologies have been widely employed in the food business for automated external quality management, and they have proven to be a scientific and useful tool. Based on image processing investigations, the application of machine and computer vision approaches in external fruit quality control has been described. This paper provides a detailed description of the fruit detection and classification procedure. The typical characteristics of fruits such as color, shape, and texture are presented with specific extraction techniques such as SURF, HoG, and LBP. ML methods like KNN, SVM, and CNN are also discussed. The approach, benefits, drawbacks, and challenges of fruit classification and grading are explored in this study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image classification

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Geometric-visual descriptor for improved image based localization

Geometric-visual descriptor for improved image based localiz...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Ouni, Achref Royer, Eric Chevaldonne, Marc Dhome, Michel Univ Clermont Auvergne Inst Pascal Clermont Ferrand France

ISBN: (纸本)9781728180687

This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometricvisual descriptor improves standard image based localization approaches.

关键词： Descriptors Retrieval images Pose estimation Localization

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Recurrent Network with Enhanced Alignment and Attention-Guided Aggregation for Compressed Video Quality Enhancement

Recurrent Network with Enhanced Alignment and Attention-Guid...

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IEEE visual communications and image processing (VCIP)

作者： Xiaodi Shi Jucai Lin Dong Jiang Chunmei Nian Jun Yin Zhejiang Dahua Technology Co. Ltd Zhejiang Provincial Key Laboratory of Harmonized Application of Vision & Transmission Hangzhou China

ISBN: (纸本)9781665475938

Recently, various compressed video quality enhancement technologies have been proposed to overcome the visual artifacts. Most existing methods are based on optical flow or deformable alignment to explore the spatiotemporal information across frames. However, inaccurate motion estimation and training instability of deformable convolution would be detrimental to the reconstruction performance. In this paper, we design a bi-directional recurrent network equipping with enhanced deformable alignment and attention-guided aggregation to promote information flows among frames. For the alignment, a pair of scale and shift parameters are learned to modulate optical flows into new offsets for deformable convolution. Furthermore, an attention aggregation strategy oriented at preference is designed for temporal information fusion. The strategy synthesizes global information of inputs to modulate features for effective fusion. Extensive experiments have proved that the proposed method achieves great performance in terms of quantitative performance and qualitative effect.

关键词： Training visualization image coding Convolution visual communication Motion estimation Quality assessment

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6th International conference on Computer Vision and image processing, CVIP 2021

6th International Conference on Computer Vision and Image Pr...

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6th International conference on Computer Vision and image processing, CVIP 2021

ISBN: (纸本)9783031113451

The proceedings contain 97 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;VISION HELPER: CNN Based Real Time Navigator for the visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded Vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on Thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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Semantic Compensation Based Dual-Stream Feature Interaction Network for Multi-oriented Scene Text Detection

Semantic Compensation Based Dual-Stream Feature Interaction ...

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IEEE visual communications and image processing (VCIP)

作者： Siyan Wang Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781665475938

Due to the various appearances of scene text instances and the disturbance of background, it is still a challenging task to design an effective and accurate text detector. To tackle this problem, in this paper we propose a novel dual-stream scene text detector considering semantic compensation and feature interaction. The detector extracts image features from two input images of different resolution, which improves its perceptive ability and contributes to detecting large and long texts. Specifically, we propose a Semantic Compensation Module (SCM) to aggregate features between the two streams, which compensates semantic information in features at each level via an attention mechanism. Moreover, we design a Feature Interaction Module (FIM) to obtain more expressive features. Experiments conducted on three benchmark datasets, ICDAR2015, MSRA-TD500 and ICDAR2017-MLT, demonstrate that our proposed method has competitive performance and strong robustness.

关键词： image resolution visual communication Semantics Neural networks Detectors Streaming media Benchmark testing

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