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19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Liu, Ying Feng, Hao Zhang, Weidong Fang, Jie Xiao, Peng Zhang, Dengsheng Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Shaanxi International Joint-Research Center for Wireless Communication and Information Processing Xi'an China Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Xi'an China Institute of Innovation Science & Sustainability Federation University Australia Gippsland Australia

ISBN: (纸本)9798350304398

Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.

关键词： image classification

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Sign Language Recognition using Spiking Neural Networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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OBI-BENCH: CAN LMMS AID IN STUDY OF ANCIENT SCRIPT ON ORACLE BONES?

arXiv

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arXiv 2024年

作者： Chen, Zijian Chen, Tingzhu Zhang, Wenjun Zhai, Guangtao Institute of Image Communication and Information Processing Shanghai Jiao Tong University China School of Humanities Shanghai Jiao Tong University China

We introduce OBI-Bench, a holistic benchmark crafted to systematically evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions (OBI) processing tasks demanding expert-level domain knowledge and deliberate cognition. OBI-Bench includes 5,523 meticulously collected diverse-sourced images, covering five key domain problems: recognition, rejoining, classification, retrieval, and deciphering. These images span centuries of archaeological findings and years of research by front-line scholars, comprising multi-stage font appearances from excavation to synthesis, such as original oracle bone, inked rubbings, oracle bone fragments, cropped single characters, and handprinted characters. Unlike existing benchmarks, OBI-Bench focuses on advanced visual perception and reasoning with OBI-specific knowledge, challenging LMMs to perform tasks akin to those faced by experts. The evaluation of 6 proprietary LMMs as well as 17 open-source LMMs highlights the substantial challenges and demands posed by OBI-Bench. Even the latest versions of GPT-4o, Gemini 1.5 Pro, and Qwen-VL-Max are still far from public-level humans in some fine-grained perception tasks. However, they perform at a level comparable to untrained humans in deciphering tasks, indicating remarkable capabilities in offering new interpretative perspectives and generating creative guesses. We hope OBI-Bench can facilitate the community to develop domain-specific multi-modal foundation models towards ancient language research and delve deeper to discover and enhance these untapped potentials of LMMs. © 2024, CC BY.

关键词： Benchmarking

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Structured Computational Modeling of Human Visual System for No-reference image Quality Assessment

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International Journal of Automation and computing 2021年第2期18卷 204-218页

作者： Wen-Han Zhu Wei Sun Xiong-Kuo Min Guang-Tao Zhai Xiao-Kang Yang MoE Key Lab of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of *** human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience *** traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational *** bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of *** to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,*** natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three *** support vector regression(SVR)is employed to aggregate features to the final quality *** experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

关键词： image quality assessment(IQA) no-reference(NR) structural computational modeling human visual system visual feature extraction

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Enhanced Focal Liver Lesion Classification using Channel Attention Technique

Enhanced Focal Liver Lesion Classification using Channel Att...

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Conference on information and communication Technology (CICT)

作者： Sunkanaboina Chandra Lingamaiah Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening are often subjective, time-consuming, and reliant on the expertise of the sonographer, which can impact diagnostic accuracy. To tackle these challenges, in this paper proposed a deep learning (DL) based framework to enhance the effective diagnosis of focal liver lesions. This approach leverages an channel attention mechanism integrated with the DarkNet-19 pre-trained model to improve feature extraction and boosts classification accuracy. By automating the diagnostic process, the proposed model addresses the limitations of traditional methods, providing a more efficient and reliable solution for liver disorder diagnosis. Experimental results with an ultrasound image dataset demonstrate that the proposed model significantly outperforms conventional DL methods, showcasing its advantages in performance and efficiency.

关键词： Frequency locked loops Deep learning Accuracy Ultrasonic imaging Attention mechanisms Liver diseases Feature extraction information and communication technology Lesions Reliability

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Lightweight Deep Learning Model for Melanoma Classification in Dermoscopy images for Smart Healthcare

Lightweight Deep Learning Model for Melanoma Classification ...

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International Conference on Wireless communications, Signal processing and Networking (WiSPNET)

作者： Pentapati Naga Sree Charan Teja Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Among various types of skin cancers, melanoma is the most aggressive and deadly. There is a notable growth in the implementation of deep learning (DL) methods to identify skin malignancies in dermoscopy images. This paper introduces a lightweight DL-based approach designed for seamless integration into low-memory devices within healthcare applications. The proposed method incorporates three lightweight convolutional neural network (CNN) models: MobileNet-v2, SqueezeNet, and GoogLeNet. Initially, test features are computed from fine-tuned deep CNN models. Subsequently, probability scores for each class are derived by training and testing a random forest classifier with features extracted from the models. Then, the proposed method uses an average ensemble voting technique on the probability scores to enhance the classification performance compared to the individual models. The proposed of lightweight CNN model demonstrated an accuracy of 85.19 % which is better than existing works.

关键词： Deep learning Wireless communication Adaptation models Computational modeling Melanoma Medical services Feature extraction

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A Computer Aided Detection System for Breast Cancer using Lightweight CNN Models for Smart Healthcare

A Computer Aided Detection System for Breast Cancer using Li...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Varun C Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological whole slide images to identify cancerous anomalies. However, pathologists face severe challenges such as fatigue, subjectivity, and inter-observer variability in the early detection of BC. Understanding the intricacies of BC from molecular tissue structures is complex, and inexpertise leads to adverse outcomes. This paper proposes a computed aided detection (CAD) system that can assist histopathologists in the early detection of BC, potentially reducing the abnormalities and diagnostic time. Leveraging the power of convolutional neural networks (CNNs), a stacked ensemble-based model is developed to identify benign and malignant cancerous tissues using histopathological images. The ensemble models comprise three deep CNNs, namely MobileNetV2, ShuffleNet, and SqueezeNet, trained on the BreakHis dataset. Finally, individual CNNs predictions are fed to the average voting-based classifier to identify benign and malignant tissues. The stacked ensemble-based deep CNN model outperformed the individual CNN models in BC prediction, achieving superior accuracy and robustness.

关键词： Solid modeling Computational modeling Medical services Predictive models Breast cancer Robustness Quality assessment

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Transclaw U-Net: Claw U-Net With Transformers for Medical image Segmentation 5

Transclaw U-Net: Claw U-Net With Transformers for Medical Im...

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5th International Conference on information communication and Signal processing, ICICSP 2022

作者： Yao, Chang Hu, Menghan Li, Qingli Zhai, Guangtao Zhang, Xiao-Ping East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Ryerson University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9781665485890

In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer part is added after three convolution operations to fuse shallow features extracted by convolution operations for maximally encoding the long-range spatial features between patches. The 'Claw' in TransClaw U-net means that we add the bottom upsampling part to retain the deepest feature information for detail segmentation. In addition, the modified three-channel global attention mechanism to blend the outputs of three channels (the encoding part, the bottom upsampling part and the decoding part) to effectively extract image contours. The experimental results on Synapse Multi-organ Segmentation Dataset show that TransClaw U-Net performs better than other networks. The results of ablation experiments prove the effectiveness of the three improved components of the network and influence of input image size and skip-connection numbers on network performance. The source code will be publicly available once the paper is accepted. © 2022 IEEE.

关键词： Convolution

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Stability and Performance Analysis of Interfered Digital Systems with Generalized Overflow Nonlinearities

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IFAC-PapersOnLine 2024年 57卷 339-344页

作者： Srinivasulu Jogi Mounika Pulikonda Priyanka Kokil Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli Tamil Nadu 620015 India Advanced Signal and Image Processing (ASIP) Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai 600127 India

This paper investigates the problem of local stability for fixed-point interfered digital filters with generalized overflow nonlinearities. First, a familiar form of overflow nonlinearity function, covering the nonlinearities like zeroing, saturation, two's complement, and triangular is established. Second, the asymptotic stability condition for the digital filters is formulated in local context without external interference. Third, with external interference the work is extended to investigate the local stability and to attain H ∞ performance of the digital filter using generalized nonlinearity function. Further, the conventional global stability results can be established as special cases of presented local approach. Finally, sufficient numerical examples are presented to highlight the merit of proposed approach.

关键词： Asymptotic stability External interference Generalized overflow nonlinearity Limit cycle oscillations Local stability

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