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image processing using Cloud for Surveillance 23

Image Processing using Cloud for Surveillance

引用

Proceedings of the 5th International conference on Information Management & Machine Intelligence

作者： Shivam Tiwari Aarhee Phukan T R Vedhavathy Department of Networking and Communications SRM Institute of Science and Technology India Department of Networking and Communication SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis.

关键词： AWS Rekognition

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

引用

14th International conference on Digital image processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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What Pushes Self-supervised image Representations Away? 28th

What Pushes Self-supervised Image Representations Away?

引用

28th International conference on Neural Information processing

作者： Zielinski, Bartosz Gorszczak, Michal Jagiellonian Univ Fac Math & Comp Sci Lojasiewicza 6 PL-30428 Krakow Poland

ISBN: (纸本)9783030923068;9783030923075

Self-supervised models provide on par or superior results to their fully supervised competitors, yet it is unclear what information about images they contain. As a result, a visual probing framework was recently introduced to probe image representations for interesting visual features. While visual probing provides information about semantic knowledge, complexity, and consistency, it does not directly and exhaustively explain which visual features push self-supervised image representations away and which are neutral. In this paper, we fill this gap by proving a method that removes particular visual features from the image and analyzes how such a distortion influences the representation. Our key findings emphasize that discrepancies in features like lines and forms push self-supervised representations away more than brightness, color, shape, and especially texture changes. Our work is complementary to visual probing and provides more direct explanations of the mechanisms behind the contrastive loss.

关键词： Explainability Self-supervised representation Computer vision Deep learning

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DL-Assisted ROP Screening Technique 14th

DL-Assisted ROP Screening Technique

引用

14th International Joint conference on Biomedical Engineering Systems and Technologies

作者： Kumar, Vijay Patel, Het Azad, Shorya Paul, Kolin Surve, Abhidnya Chawla, Rohan Indian Inst Technol Delhi Khosla Sch Informat Technol New Delhi India Indian Inst Technol Delhi Dept Comp Sci & Engn New Delhi India All India Inst Med Sci Delhi Dr Rajendra Prasad Ctr Ophthalm Sci New Delhi India

ISBN: (纸本)9783031206634;9783031206641

Retinopathy of Prematurity (ROP) is the most common cause of visual impairment among premature babies throughout the world. The consequence of ROP impairment can be minimized by performing suitable screening and treatment. However, due to deficiency of health care resources, many of these premature infants remain unidentified after birth. As a result, ROP-induced visual impairment is much more prevalent in these babies. We propose a robust and intelligent approach based on deep artificial intelligence and computer vision to automatically recognise the optical disk (OD) and retinal blood vessels and categorise the severe severity (Zone-1) of ROP patients in this study. We report empirical evidence using premature infant retina images from a nearby hospital to evaluate and validate the proposed approach. The YOLO-V5 prediction model identifies the OD from premature infants retina images, according to our results. Furthermore, the preterm infants' fundus images were perfectly segmented by the computer vision-based system, which effectively separated the retinal vessels. Our system is able to obtain an accuracy of 82.5% in the Zone-1 occurrence of ROP.

关键词： Fundus image Retinopathy of Prematurity (ROP) Computer Aided Diagnosis (CAD) image processing Deep Learning (DL) YOLO-v5

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Energy-Efficient Video Streaming: A Study on Bit Depth and Color Subsampling

Energy-Efficient Video Streaming: A Study on Bit Depth and C...

引用

IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Lingfeng Qu Jong Hwan Ko Cosmin Stejerean Christian Timmerer Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of Electrical and Computer Engineering Sungkyunkwan University South Korea Meta Platforms Inc. Menlo Park USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As video dimensions – including resolution, frame rate, and bit depth – increase, a larger bitrate is required to maintain a higher Quality of Experience (QoE). While videos are often optimized for resolution and frame rate to improve compression and energy efficiency, the impact of color space is often overlooked. Larger color spaces are essential for avoiding color banding and delivering High Dynamic Range (HDR) content with richer, more accurate colors, although this comes at the cost of higher processing energy. This paper investigates the effects of bit depth and color subsampling on video compression efficiency and energy consumption. By analyzing different bit depths and subsampling schemes, we aim to determine optimized settings that balance compression efficiency with energy consumption, ultimately contributing to more sustainable and high-quality video delivery. We evaluate both encoding and decoding energy consumption and assess the quality of videos using various metrics including PSNR, VMAF, ColorVideoVDP, and CAMBI. Our findings offer valuable insights for video codec developers and content providers aiming to improve the performance and environmental footprint of their video streaming services.

关键词： Energy consumption image coding image color analysis Energy resolution Bit rate Color Streaming media Energy efficiency Decoding Quality of experience

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Complex-valued SAR image Compression: A Novel Approach for Amplitude and Phase Recovery

Complex-valued SAR Image Compression: A Novel Approach for A...

引用

IEEE visual communications and image processing (VCIP)

作者： Paras Maharjan Zhu Li University of Missouri Kansas City

Synthetic Aperture Radar (SAR) is an active and coherent imaging system that utilizes radio waves to illuminate the Earth’s surface, generating complex-valued images of the ground. It is widely employed in environmental monitoring and surveillance due to its ability to function effectively in diverse weather and lighting conditions, including day and night. SAR achieves this through the emission of coherent radio frequency signals and the capture of slant range and azimuth information. These details are stored as a complex signal in the form of Inphase (I) and Quadrature (Q) components, known as complex-valued SAR. The complex values (I/Q) can be transformed into amplitude and phase information. SAR images have a substantial size and a wide dynamic range, which pose challenges for storage and transmission. Thus, the adoption of compression techniques becomes essential to manage the large size of SAR images. In contrast to existing methods that primarily compress amplitude information, our study presents a novel pipeline to compress complex-valued SAR images using a prediction-recovery framework that utilizes a conventional image compression algorithm, High-Efficiency Video Codec (HEVC), to encode the original image to a bitstream. Subsequently, the framework decodes a prediction of the I/Q channels and then recovers the phase and amplitude via SARRecoveryNet, a deep-learning-based network, to effectively remove compression artifacts and recover information that suits different applications. By directly compressing the I/Q values as a predictor, and recovery via a deep learning module, our method preserves both the amplitude and phase information of the SAR image as part of the loss function in the training. Experimental results demonstrate that our proposed approach significantly enhances complex-valued SAR image compression in terms of rate-distortion (RD) performance and visual perception. The code will be made available on GitHub after the paper is accepted.

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Diagnosis-oriented Medical image Compression with Efficient Transfer Learning

Diagnosis-oriented Medical Image Compression with Efficient ...

引用

IEEE visual communications and image processing (VCIP)

作者： Guangqi Xie Xin Li Xiaohan Pan Zhibo Chen Unviersity of Science and Technology of China Hefei China

Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected to high-fidelity coding, leading to unnecessary transmission costs. To mitigate this, we propose diagnosis-oriented medical image compression, a special semantic compression task designed for medical scenarios, targeting to reduce the compression cost without compromising the diagnosis accuracy. However, collecting sufficient medical data to optimize such a compression system is significantly expensive and challenging due to privacy issues and the lack of professional annotation. In this study, we propose DMIC, the first efficient transfer learning-based codec, for diagnosis-oriented medical image compression, which can be effectively optimized with only few-shot annotated medical examples, by reusing the knowledge in the existing reinforcement learning-based task-driven semantic coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the partial parameters of the policy network for bit allocation within HRLVSC, which enables it to adapt to the medical images. In this work, we validate our DMIC with the typical medical task, Coronary Artery Segmentation. Extensive experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of the policy network with only 1 medical sample.

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Effectiveness Evaluation of Basic Bitstream Features in Surveillance Video Recompression Forensics Using VVC

Effectiveness Evaluation of Basic Bitstream Features in Surv...

引用

IEEE visual communications and image processing (VCIP)

作者： Yanchao Gong Kunlei Li Xiaokai Zhou Jiaxi Jing Kaifang Yang Ying Liu School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

Surveillance videos have been widely used in criminal investigation, judicial evidence collection, and other fields. The authenticity of surveillance video is a key prerequisite for whether the surveillance video is used legally. Video recompression forensics (VRF) is a widely recognized technique for video authenticity authentication. And basic bitstream feature (BBF) extraction is the primary step in VRF, which significantly affects the accuracy of VRF. The current VRF methods have not systematically focused on the different impact of different types of BBF on VRF. Most VRF methods directly extract the BBF from video bitstream to construct the advanced bitstream features, which implies that all BBF have an equal impact on the accuracy of VRF. This operation is obviously unreasonable. This paper focuses on VVC encoded surveillance videos, and an experimental scheme is first designed to evaluate the effectiveness of BBF. Based on the experimental data, the impact of different types of BBF on the accuracy of VRF is further analyzed and evaluated, and some important conclusions are drawn. These conclusions have important guiding significance for the subsequent development of efficient VRF methods suitable for surveillance videos using VVC.

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Split-Conv: A Resource-efficient Compression Method for image Quality Assessment Models

Split-Conv: A Resource-efficient Compression Method for Imag...

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IEEE visual communications and image processing (VCIP)

作者： Yiming Zhang Xiongkuo Min Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

Blind image Quality Assessment (BIQA) models based on deep neural networks (DNNs) have achieved state-of-the-art performance recently. However, the heavyweight architecture makes them hard to deploy on resource-constrained devices. Filter pruning is one of the most effective ways to compress the DNN model. However, most pruning methods need complete retraining after pruning which is too resource-consuming for IQA models with complex training processes. In this paper, we propose a resource-efficient structural pruning method for IQA models called Split-Conv, where the model only needs to be retrained on small IQA databases. Specifically, we split convolutional layers into sub-convolutional kernels and decorators, which can measure the importance of convolutional channels more precisely, thus reducing the requirement for retraining conditions. The experiments on several IQA models demonstrate the effectiveness of our IQA model pruning approach. For instance, DBCNN, StairIQA, and HyperIQA’s performances can be kept at the baseline level when 50% parameters are pruned with the model being retrained on the authentically distort IQA database containing only 586 images, and the performances of StairIQA and HyperIQA on KonIQ-10k database are still acceptable after 80% of the parameters have been pruned. Besides, due to Split-Conv’s capability to identify the important, useless, and harmful filters, the performances of some BIQA models can even be boosted after model compression with a proper pruning ratio.

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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IEEE visual communications and image processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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