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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

共 25554 条记录，以下是451-460 订阅

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Neuromorphic Decoding of Sampling Representations of images in the Marr's Paradigm 9

Neuromorphic Decoding of Sampling Representations of Images ...

引用

9th ieee All-Russian Microwave conference, RMC 2024

作者： Antsiperov, Viacheslav Kershner, Vladislav Kotelnikov Institute of Radioengineering and Electronics of RAS Moscow Russia

ISBN: (纸本)9798331540944

The most important function of human vision is the recognition of edges, contours and textures of visible objects. By defining these elements, a person can perform higher-level visual information processing tasks, in particular, such as image segmentation, object detection and recognition. In the field of computer vision, this task is also of particular interest. In recent decades, various methods have been proposed to determine the characteristic boundaries of objects, allowing, in addition to highlighting edges and contours, to solve problems such as eliminating noise and distortion, restoring blurred and blurred images associated with scaling, highlighting individual borders of different objects in the image, restoring incomplete contours, etc. Currently, significant progress has been made in this direction, but the existing image processing algorithms are significantly inferior to the capabilities of the human visual system. Based on well-known methods for recognizing edges and contours of objects, the paper discusses current problems in this area and proposes a new method based on the neuromorphic encoding/decoding of images. © 2024 ieee.

关键词： image enhancement

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Diagnosis-oriented Medical image Compression with Efficient Transfer Learning

Diagnosis-oriented Medical Image Compression with Efficient ...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

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

ISBN: (纸本)9798350359855

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. © 2023 ieee.

关键词： image compression

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Bionic model of information processing in the retina

Bionic model of information processing in the retina

引用

2024 International conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024

作者： Bohlmann, Sabine Clausthal University of Technology Institute of Computer Science Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350394528

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences. © 2024 ieee.

关键词： image enhancement

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A Review of Segment Anything Model and Its Applications

A Review of Segment Anything Model and Its Applications

引用

2024 ieee International conference on Unmanned Systems, ICUS 2024

作者： Ma, Zengchen Sun, Yanwen Zhai, Shixun Lei, Guocheng Zhang, Kaige North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9798350384185

Segment anything model (SAM) is the first foundation model in computer vision, following the footsteps of natural language *** on SAM, researchers have conducted a suite of works on a variety of applications in the past year, including image segmentation, object detection, image editing, video object tracking, *** paper conducts a comprehensive review on the works derived from SAM and makes a comparison concerning computational efficiency, model size, and the quality of the processing results.A bunch of evidenceable results indicates that the visual foundation model is able to advance vision task performance, and it is worth further exploring the application mode from the edge ***, future developing trend on visual foundation model is discussed and concluded. © 2024 ieee.

关键词： image segmentation

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Preparing VVC for Streaming: A Fast Multi-Rate Encoding Approach

Preparing VVC for Streaming: A Fast Multi-Rate Encoding Appr...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Liu, Yiqun Amirpour, Hadi Abdoli, Mohsen Timmerer, Christian Guionnet, Thomas Ateme Rennes France Alpen-Adria-Universität Christian Doppler Laboratory ATHENA Klagenfurt Austria IRT Bcom Cesson-Sévigné France

ISBN: (纸本)9798350359855

The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like Versatile Video Coding (VVC) poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in Video Multi-method Assessment Fusion (VMAF). This improvement is observed when utilizing Versatile Video Encoder (VVenC), an open and optimized VVC encoder implementation. © 2023 ieee.

关键词： Video signal processing

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Satellite image Denoising Using Optimized Pulse Coupling Network 3

Satellite Image Denoising Using Optimized Pulse Coupling Net...

引用

3rd ieee International conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Rajesh, V. Sivakumar, P. Kalasalingam Academy of Research and Education Department of Ece Tamil Nadu Krishnan Koil626126 India

ISBN: (纸本)9798350361186

Satellite image denoising is of utmost importance in various applications weather monitoring, flood control and crop monitoring focusing on enhancing the visual quality of affected images. Denoising is a contemporary trend in the application of image processing, yet encounters numerous restrictions attributed to environmental conditions. In recent times, there has been extensive exploration of DL (deep learning) methods for the purpose of noise removal in satellite images. This work presents a DL based optimization model for Satellite image denoising. That is the DL model PCN (pulse coupling network) with AF (artificial flora) optimizer is used for parameter enhancement in PCN. PSNR and SSIM values achieved are 44.8 and 0.89 respectivelyon the FloodNet database. Thus, the analysis yielded superior performance and achieved better quality images. © 2024 ieee.

关键词： image denoising

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ISVDA: An In Storage processing Accelerator for visual Data Analysis 42

ISVDA: An In Storage Processing Accelerator for Visual Data ...

引用

42nd International conference on Computer Design

作者： Zhao, Zhenhua Shen, Zhaoyan Cai, Xiaojun Shandong Univ Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9798350380415;9798350380408

Deep neural networks(DNNs) have been widely applied in visual data analysis. To accelerate DNN-based visual data inference, contemporary visual data analytics systems employ strategies like model specialization and model cascading to alleviate the computational demands of DNN processes. In addition, some existing systems seek to reduce the overhead of data decoding by directly storing decoded RGB data on storage devices, albeit at the expense of significantly increased storage costs and data transfer volumes. In this paper, we introduce ISVDA, a visual data analysis accelerator based on In-storage computing. ISVDA proposes to offload the data-intensive video preprocessing tasks, encompassing data decoding and resizing, to the computational storage device, leveraging its ample internal bandwidth and computational advantages. Concurrently, ISVDA introduces a dynamic sampling algorithm that intelligently adjusts sampling strides based on spatiotemporal information encoded in motion vectors, thereby skipping more video frames for DNN inferences in both retrospective and streaming analysis scenarios. Experiments conducted on real hardware demonstrate that compared to CPU-based preprocessing systems, ISVDA can achieve average 6.8x speedup for image data analysis and 2.8x speedup for video data analysis.

关键词： visual Data Analysis Computational Storage

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Using Texture to Classify Forests Separately from Vegetation

Using Texture to Classify Forests Separately from Vegetation

引用

2024 ieee Pacific Rim conference on communications, Computers and Signal processing, PACRIM 2024

作者： Treadwell, David R. Jacoby, Derek Parkinson, Will Maxwell, Bruce Coady, Yvonne Northeastern University Computer Science SeattleWA United States University of Victoria Computer Science VictoriaBC Canada Earth Daily Analytics Inc VancouverBC Canada

ISBN: (纸本)9798350362312

Identifying terrain within satellite image data is a key issue in geographical information sciences, with numerous environmental and safety implications. Many techniques exist to derive classifications from spectral data captured by satellites. However, the ability to reliably classify vegetation remains a challenge. In particular, no precise methods exist for classifying forest vs. non-forest vegetation in high-level satellite images. This paper provides an initial proposal for a static, algorithmic process to identify forest regions in satellite image data through texture features created from detected edges and the NDVI ratio captured by Sentinel-2 satellite images. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes. © 2024 ieee.

关键词： forest image processing satellite images terrain classification texture vegetation

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Reconfigurable Band-Stop Filter Design using Computing In Memory

Reconfigurable Band-Stop Filter Design using Computing In Me...

引用

2024 International conference on communications Control and Computing Technologies for Smart Grids

作者： Mahdavi, Mojtaba Ericsson Ericsson Res Lund Sweden

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562;9798350318555

Band-Stop filter is one of the key components in smart grids, wireless communications systems, radar, and signal processing. Due to the ability of suppressing signals at specific frequencies, these filters enable the rejection of unwanted signals in a particular frequency band in a crowded microwave spectra. Specifically, such filters can be utilized to mitigate the effect of harmonics in smart grids. The traditional band-stop filters suffer from lack of high suppression, reconfigurable bandwidth and frequency agility. This paper presents a novel approach to realize reconfigurable and flexible band-stop filters using the computing-in-memory (CIM) technique. The proposed scheme consists of a low-pass filter and a high-pass filter, with their corresponding coefficients programmed into an array of memristive devices. As a result, reconfigurable and low-complexity band-stop filters can be implemented, which support the realization of arbitrary filter impulse responses and high-order filters.

关键词： Band-Stop Filter Wireless Communication Systems Computing In Memory (CIM) Digital Signal processing (DSP) image processing Crossbar Array Memristive Devices

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Refining Coded image in Human Vision Layer Using CNN-Based Post-processing 13

Refining Coded Image in Human Vision Layer Using CNN-Based P...

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13th ieee Global conference on Consumer Electronic, GCCE 2024

作者： Shindo, Takahiro Tatsumi, Yui Watanabe, Taiju Watanabe, Hiroshi Waseda University Tokyo Japan

ISBN: (纸本)9798350355079

Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods. © 2024 ieee.

关键词： Machine vision

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