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37th ieee International Parallel and Distributed processing symposium (IPDPS)

作者： Akinola, Taoreed Li, Xiangfang Wilkins, Richard Obiomon, Pamela Qian, Lijun Prairie View A&M Univ Dept Elect & Comp Engn Prairie View TX 77446 USA

ISBN: (纸本)9798350311990

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels' intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsuthresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

关键词： Inverse Quantum Fourier Transform Computer Vision image Segmentation

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Leveraging Frequency Analysis for image Denoising Network Pruning

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ieee TRANSACTIONS ON image processing 2025年 34卷 1660-1671页

作者： Ren, Dongdong Li, Wenbin Huo, Jing Wang, Lei Pan, Hongbing Gao, Yang Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Suzhou Campus Suzhou 215163 Peoples R China Nanjing Univ Sch Intelligence Sci & Technol Suzhou Campus Suzhou 215163 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia

As a common model compression technique, network pruning is widely used to reduce storage and computational cost of deep models in the resource-constrained regime. However, most current pruning methods are designed for high-level vision tasks, with few developed for low-level vision tasks. We observed that the norm-based pruning criterion, originally designed for high-level vision tasks, is highly unsuitable for low-level image denoising networks. This difference arises because image denoising networks pursue distinct feature granularities and goals compared to typical high-level vision tasks. To address this issue, we propose a novel filter evaluation method, termed High-Frequency Components Pruning (HFCP), specifically tailored for image denoising network pruning. HFCP assesses filter importance based on high-frequency components. To the best of our knowledge, this is the first pruning method designed specifically for image denoising tasks, straightforward and applicable to various types of noise. Furthermore, HFCP enhances the pruned model's high-frequency information content with high reliability and interpretability. This facilitates the network's ability to distinguish high-frequency signals from noise. We comprehensively analyzed multiple image denoising networks and validated HFCP's effectiveness across four mainstream networks.

关键词： image denoising Training Information filters Noise reduction Noise Feature extraction Superresolution image coding Hardware Standards High-frequency components image denoising network pruning low-level vision tasks

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Enhanced Classification System for Real-Time Embedded Vision Applications

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ieee ACCESS 2024年 12卷 162311-162326页

作者： Khelifi, Ramzi Nini, Brahim Berkane, Mohamed Univ Oum El Bouaghi Res Lab Comp Sci Complex Syst ReLa CS 2 Oum El Bouaghi 04000 Algeria Univ Oum El Bouaghi Artificial Intelligence & Autonomous Things Lab Oum El Bouaghi 04000 Algeria

Embedded computer vision systems are increasingly being adopted across various domains, playing a pivotal role in enabling advanced technologies such as autonomous vehicles and industrial automation. Their cost-effectiveness, compact size, and portability make them particularly well-suited for diverse implementations and operations. In real-time scenarios, these systems must process visual data with minimal latency, which is crucial for immediate decision-making. However, these solutions continue to face significant challenges related to computational efficiency, memory usage, and accuracy. This research addresses these challenges by enhancing classification methodologies, specifically in Gray Level Co-occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) classifiers. To maintain a high level of accuracy while preserving performance, a smaller feature set is selected following a comprehensive complexity analysis and is further refined through Correlation-based Feature Selection (CFS). The proposed method achieves an overall classification accuracy of 84.76% with a feature set reduced by 79.2%, resulting in a 72.45% decrease in processing time, a 50% reduction in storage requirements, and up to a 77.8% decrease in memory demand during prediction. These improvements demonstrate the effectiveness of the proposed approach in improving the adaptability and capabilities of embedded vision systems (EVS), optimizing their performance under the constraints of real-time limited-resource environments.

关键词： Accuracy Support vector machines Real-time systems Feature extraction Memory management Computer vision Surveillance Bandwidth Wildlife Machine learning image processing Embedded computer vision limited resource systems machine learning pattern classification real-time image processing

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ON-BOARD image COMPRESSION USING CONVOLUTIONAL AUTOENCODER: PERFORMANCE ANALYSIS AND APPLICATION SCENARIOS

ON-BOARD IMAGE COMPRESSION USING CONVOLUTIONAL AUTOENCODER: ...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Guerrisi, Giorgia Schiavon, Giovanni Del Frate, Fabio Tor Vergata Univ Rome DICII I-00133 Rome Italy GEO K Srl I-00133 Rome Italy

ISBN: (纸本)9798350320107

The amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced onboard processing procedures are required. Artificial intelligence (AI) and Deep Learning (DL) can provide advanced information from EO data and thanks to specific hardware platforms these algorithms can be used also in space. We present here the Convolutional AutoEncoder (CAE)-based algorithm developed for on-board lossy image compression of the European Space Agency (ESA) Phi-Sat-2 mission. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative onboard environment. Therefore, besides analyzing the results for the local hardware environment, we investigate the performance variation for the on-board setting. Moreover, we introduced an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks.

关键词： artificial intelligence convolutional neural networks image compression on-board processing

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ARIA APP | LITTLEONE EDITION, AN ALL-IN-ONE APPLICATION FOR processing MEDICAL imageS AND DEVELOPING U-NET MODELS 21

ARIA APP | LITTLEONE EDITION, AN ALL-IN-ONE APPLICATION FOR ...

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21st ieee International symposium on Biomedical Imaging (ISBI)

作者： Rez, Ntaniel Asvestas, Pantelis Univ West Attica Dept Biomed Engn Athens Greece

ISBN: (纸本)9798350313345;9798350313338

The efficient segmentation of regions of interest is a crucial step for various medical applications, as it is widely used for monitoring disease progression and evaluating various conditions. However, the traditional manual segmentation by trained experts is a time consuming and repetitive process. Various approaches have been proposed in recent years such as and not limited to the use of neural networks and deep learning. Inspired by the achievements of artificial intelligence in demanding computer vision tasks we present the Aria App vertical bar LittleOne edition, featuring a GUI where image processing, network development and evaluation can be conducted. In this paper, we attempt to address the challenge of identifying retinal vessels in fundus images which is accomplished using the U-Net network. Experimental results conducted within the app on the DRIVE dataset demonstrate comparable performance to recent research with a 94.9% accuracy, 93.2% sensitivity, 95.1% specificity and 94.20% AUC in the total.

关键词： Retinal vessel Segmentation U-net Aria App GUI interactive

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Automatic Chart Decoding System Based on Deep Learning and image processing 4

Automatic Chart Decoding System Based on Deep Learning and I...

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4th International symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Liang, Chaofan Xue, Fenghao Li, Zhangwei College of Software Engineering Chengdu University of Information Technology Chengdu610000 China

ISBN: (纸本)9798331541729

Statistical Charts contain a wealth of information. As an important way to visualize data presentation, statistical charts allow viewers to obtain a complete and intuitive understanding of the content shown in a very short time. At present, the research on automatic extraction and understanding of a large amount of text information has been relatively mature. However, even the latest big artificial intelligence models cannot accurately extract statistical graphs, which are personalized and contain a large amount of information. We propose an automatic bar chart data extraction process by combining deep learning and image processing technology, and construct an intelligent bar chart decoding system. The system is divided into three parts: the classification of statistical chart types, the text detection in the image, the classification of text roles and the image extraction. The original data used to create the chart in the pan-bar graph image is extracted for downstream applications. We evaluate and compare our system on public datasets. The results show that our system has better accuracy. © 2024 ieee.

关键词： Data assimilation

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image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces

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ieee TRANSACTIONS ON image processing 2024年 33卷 4044-4059页

作者： Liao, Xingran Wei, Xuekai Zhou, Mingliang Li, Zhengguo Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Lingnan Univ Dept Comp & Decis Sci Computat Intelligence Program Hong Kong Peoples R China

This study aims to develop advanced and training-free full-reference image quality assessment (FR-IQA) models based on deep neural networks. Specifically, we investigate measures that allow us to perceptually compare deep network features and reveal their underlying factors. We find that distribution measures enjoy advanced perceptual awareness and test the Wasserstein distance (WSD), Jensen-Shannon divergence (JSD), and symmetric Kullback-Leibler divergence (SKLD) measures when comparing deep features acquired from various pretrained deep networks, including the Visual Geometry Group (VGG) network, SqueezeNet, MobileNet, and EfficientNet. The proposed FR-IQA models exhibit superior alignment with subjective human evaluations across diverse image quality assessment (IQA) datasets without training, demonstrating the advanced perceptual relevance of distribution measures when comparing deep network features. Additionally, we explore the applicability of deep distribution measures in image super-resolution enhancement tasks, highlighting their potential for guiding perceptual enhancements. The code is available on website. (https://***/Buka-Xing/Deep-network-based-distribution-measures-for-full-reference-image-quality-assessment).

关键词： Pollution measurement Distortion measurement Task analysis image quality Distortion Degradation computational modeling image quality assessment distribution measures perceptual degradation deep neural network image representations perceptual optimization training-free

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Efficient Adaptive Feature Fusion Network for Remote-Sensing image Super-Resolution

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ieee SIGNAL processing LETTERS 2024年 31卷 3089-3093页

作者： Hao, Shuai Liu, Shuai Jia, Xu Lu, Huchuan He, You Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300401 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518000 Peoples R China

image super-resolution is a fundamental low-level vision task aimed at recovering high-resolution images with fine details. Deep learning has significantly enhanced the performance of super-resolution techniques for remote sensing imagery. However, increasing the depth of networks and the size of their parameters has resulted in substantial computational and storage burdens. To address this challenge, we propose an adaptive approach that learns both local and global information for each region. We introduce a lightweight hybrid model named the Efficient Adaptive Feature Fusion Network, which combines CNNs and Transformers to fully exploit the texture information in remote sensing images. This model leverages local details and long-range dependencies within images in an adaptive manner to achieve superior super-resolution. Specifically, a set of Transformers is employed to model the self-similarity between pixels and perform dense texture pattern predictions at each pixel, while a set of CNNs captures local details within the images. The computed global and local features serve as inputs to the proposed Adaptive Contextual Fusion Block, which learns to fuse local and global information across different regions to generate robust image super-resolution features. We conduct extensive experimental evaluations of the proposed method on the UCMerced and AID datasets, demonstrating its outstanding performance in terms of PSNR and SSIM metrics. Comprehensive experiments validate the effectiveness of our approach, showing that the proposed method achieves an excellent balance between performance and complexity.

关键词： Feature extraction Superresolution Transformers Remote sensing Adaptation models Data mining computational modeling Adaptive systems Sensors Mathematical models Remote-sensing image transformer image super-resolution

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BARRN: A Blind image Compression Artifact Reduction Network for Industrial IoT Systems

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第9期19卷 9479-9490页

作者： Li, Jiafeng Liu, Xiaoyu Gao, Yuqi Zhuo, Li Zhang, Jing Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China

Most industrial Internet of Things (IoT) devices reduce the capture image size using high-ratio joint photographic experts group (JPEG) compression, saving storage space, and transmission bandwidth consumption. However, the resulting compression artifacts considerably affect the accuracy of subsequent tasks. Most artifact reduction algorithms do not consider the limitations of storage space and computing power of edge devices. In this study, a blind artifact reduction recurrent network (BARRN), which can reduce compression artifacts when the quality factors are unknown, is proposed. First, a structure based on recurrent convolution is designed for the specific requirements of industrial IoT image acquisition devices;the network can be scaled according to system resource constraints. Second, a more efficient convolution group, capable of adaptively processing different degradation levels, is proposed for optimal use of the limited computational resources. The experimental results demonstrate that the proposed BARRN can meet the needs of industrial systems with high computational efficiency.

关键词： Artifact reduction (AR) blind JPEG compression recurrent convolution image restoration industrial Internet of Things (IoT)

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Few-Shot Semantic Segmentation for Consumer Electronics: An Inter-Class Relation Mining Approach

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ieee TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3709-3721页

作者： Huang, Huafei Yuan, Xu Yu, Shuo Zhao, Wenhong Alfarraj, Osama Tolba, Amr Xia, Feng Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Social Comp & Cognit Intelligence Minist Educ Dalian 116024 Peoples R China Zhejiang Univ Technol Ultraprecis Machining Ctr Hangzhou 310014 Peoples R China King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia

Few-shot semantic segmentation (FSS), which can perform segmentation using only a limited number of annotated examples, is a promising technique that has been embedded in many electronic products. Existing approaches usually achieve segmentation for the query image by computing the similarity between the support and query images. However, when segmenting a new query image, the model prediction may be interfered with by distinct classes with similar semantic information, leading to unsatisfactory results. This may greatly weaken the generalization of FSS in real-world scenarios. In response to this challenge, we propose a few-shot semantic segmentation model based on inter-class relation mining named IRMNet. Firstly, we devise a class filter module that accurately selects useful semantic information by mining the class relations between the query and support images. Then, we use a class generation module that applies a diffusion model to generate rough segmentation masks for query images to augment supervision signals. Finally, we conduct extensive experiments on the PASCAL- 5(i) and FSS-1000 datasets. The evaluation results show that IRMNet can achieve superior performance compared to other baselines. The advancement of FSS in this work can contribute to enhancing visual intelligence in real-world consumer electronics.

关键词： Semantic segmentation Interference Feature extraction Semantics Consumer electronics computational modeling Predictive models image processing diffusion model few-shot learning relation mining

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