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Infrared image Colorization Algorithm Based on DCGAN and Its Edge Device Acceleration 4

Infrared Image Colorization Algorithm Based on DCGAN and Its...

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4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Chi, Haoyuan Zhang, Zebin Ge, Qiqi Zhu, Wenhuan Shanghai Jiao Tong University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

the image collected by the infrared night vision device is a grayscale image, but the human eye can distinguish only dozens of grayscale levels, which restricts the analysis and processing of the measurement image. Infrared image colorization can greatly improve the target tracking and recognition performance, speed and accuracy. this paper proposes an infrared coloring model based on DCGAN and the corresponding model compression algorithm. the generator of the DGCAN model adopts the Unet structure, and the discriminator adopts the PatchGAN structure to ensure better performance in the domain transfer from infrared images to color images. Hinge Loss and L1 loss are used to ensure the stability of model training, while taking into account the edge device transplantation of the model, the network is distilled during the training process, network pruning and neural network structure search and other operations to achieve model compression and deploy on edge devices superior. the experimental results of multiple datasets show that the colorization algorithm proposed in this paper can greatly improve the processing speed while maintaining the colorization performance, and realize the real-time processing of infrared image colorization. © 2022 IEEE.

关键词： Generative adversarial networks

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A Practical Approach to detect Indoor and Outdoor Scene recognition 4

A Practical Approach to detect Indoor and Outdoor Scene Reco...

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4th international conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Sharma, Vaishali Nagpal, Nitesh Shandilya, Ankit Dureja, Aman Dureja, Ajay Fet Pdm University India Department of Information Technology Bhagwan Parshuram Institute of Technology Rohini India Department of Information Technology Bharati Vidyapeeth's College of Engineering A-4 Paschim Vihar New Delhi India

ISBN: (纸本)9781450399937

In computer vision recognition of scenes is a long-time research problem. Scene can be defined as the real-time environment view which consists of a lot of views (like road, tree, building, parks, etc.) in a meaningful manner. the problem of scene recognition can be explained as assessment of labels such as "road", "building", "hall", "bedroom"or in an extra simplified way "Indoor scene"and "Outdoor scenes is classified on an input image based on the object or environment of the image. A huge amount of data is created and available every second in this growing era of digital data. Scene recognition is still a rising area that did not attain much success as compared to image recognition due to the vast variability of features in the scenic environment. Because of this reason, there is not a lot of work being completed these days in this area. this project focuses on the evaluation of problem statements using all the literature surveys completed lately and offering solutions for that problem statement. For resolving the proposed problem declaration ResNet and VGG variants are used ResNet18, 50 and 152 & VGG16, 19, and VGG19 with batch normalization are implemented. the entire scene recognition procedure is discussed in this report and the main motive is to form a foundation that can be further continued in proposing a new algorithm. Before deep learning the design and implementation of the scene recognition model depended on the low dimensional portrayal of the scene. the utilization of deep learning especially CNN for scene recognition has gotten extraordinary attention from the computer vision community. © 2022 ACM.

关键词： image recognition

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Facial Emotion recognition in Static and Live Streaming image Dataset Using CNN 4th

Facial Emotion Recognition in Static and Live Streaming Imag...

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4th international conference on Computational Intelligence, Communications and Business Analytics, CICBA 2022

作者： Seal, Aishani Saha, Ranita Kumar, Rishav Goenka, Subham Dey, Lopamudra Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031107658

Human communication relies heavily on facial expressions. Although detecting emotion from facial expression has always been a simple task for humans, doing so with a computer technique is rather difficult. It is now possible to discern emotions from images because of recent advances in computer vision and machine learning. they frequently disclose people’s true emotional situations beyond their spoken language. Furthermore, visual pattern-based understanding of human effect is a critical component of any human-machine interaction system, which is why the task of Facial Expression recognition (FER) attracts both scientific and corporate interest. Deep Learning (DL) approaches have recently achieved very high performance on FER by utilizing several architectures and learning paradigms. We have considered two types of images here, mainly static and live streaming datasets, and compared their performance using a convolution neural networks (CNN) strategy. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion recognition

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Gesture recognition Method Based on computer vision and Surface Electromyography : Implementing Intention recognition of the Healthy Side in the Hand Assessment Process

Gesture Recognition Method Based on Computer Vision and Surf...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Chaojie Yang Longhan Xie Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

With the rapid development of sensing technology, a variety of hand rehabilitation assessment systems have emerged as choices for patients engaged in home-based rehabilitation. However, challenges such as fixed assessment sequences and operational constraints persist. To enhance the efficiency of hand rehabilitation assessment systems, employing gesture recognition to detect the healthy side's movement intent for assisting in the evaluation is a viable solution. In recent years, significant improvements have been made in gesture recognition methods, with some based on computer vision(CV) and surface electromyography(sEMG) demonstrating promising results. this paper explores a gesture recognition method based on both CV and sEMG. Initially, image data and surface electromyographic signals are collected, and joint coordinate data is extracted using the Mediapipe API. Subsequently, both sets of data undergo preprocessing and feature extraction, followed by training random forest(RF) and Convolutional Neural Network(CNN) classifiers separately. A weighted decision-making approach is then employed, achieving a commendable performance in gesture recognition, with an accuracy rate of 96.66%.

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A Novel Method for Improving the Voxel-pattern-Based Euler Number Computing Algorithm of 3D Binary images 21st

A Novel Method for Improving the Voxel-Pattern-Based Euler N...

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21st international conference on image Analysis and processing (ICIAP)

作者： Yao, Bin Han, Dianzhi Kang, Shiying Chao, Yuyan He, Lifeng Shaanxi Univ Sci & Technol Artificial Intelligence Inst Sch Elect Informat & Artificial Intelligence Xian 710021 Shaanxi Peoples R China Xianyang Normal Univ Sch Comp Sci Xianyang 712000 Shaanxi Peoples R China Nagoya Sangyo Univ Fac Environm Informat & Business Nagoya Aichi 4888711 Japan Aichi Prefectural Univ Fac Informat Sci & Technol Nagoya Aichi 4801198 Japan

ISBN: (纸本)9783031133244;9783031133237

As an important topological property of a 3D binary image, the Euler number can be calculated by counting certain 2 x 2 x 2 voxel patterns in the image. this paper presents a novel method for improving the voxel-pattern-based Euler number computing algorithm of 3D binary images. In the proposed method, by changing the accessing order of voxels in 2 x 2 x 2 voxel patterns and combining the voxel patterns which provide the same Euler number increments for the given image, the average numbers of voxels to be accessed for processing a 2 x 2 x 2 voxel pattern can be decreased from 8 to 4.25, which will lead to an efficient processing. Experimental results demonstrated that the proposed method is much more efficient than the conventional voxel-pattern-based Euler number computing algorithm.

关键词： Euler number Topological property 3D image computer vision pattern recognition

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Comparative Evaluation of Deep Learning Models for image Captioning of Satellite images 4th

Comparative Evaluation of Deep Learning Models for Image Cap...

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4th international conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Shree, Aishwary Sharma, Rohit Singla, Jai Gopal Dhaka, Vijaypal Singh Verma, Sourabh Singh Malik, Arnav Manipal University Jaipur Jaipur303007 India Space Applications Centre ISRO Ahmedabad380015 India Jayshree Periwal Global School Jaipur302026 India

ISBN: (纸本)9789819769940

In our investigation, we present a thorough comparative examination of contemporary models aimed at generating descriptive narratives based on satellite image analysis, leveraging a fusion of methodologies such as computer vision (CV), natural language processing (NLP), and machine learning (ML). Employing an array of cutting-edge neural network architectures including VGG-19, ResNet-50, DenseNet-201, EfficientNet B7, UNet, and Inception V3, we meticulously analyze intricate features within satellite images to extract essential patterns and information. these features are subsequently integrated into a Long Short-Term Memory (LSTM) network, a robust variant of Recurrent Neural Network (RNN), thereby enhancing our capacity to efficiently interpret and analyze satellite image data. through exhaustive experimentation on established benchmark datasets such as the Sydney Dataset and RSIC Dataset, we conduct a comprehensive comparative analysis to assess the efficacy of diverse models. the notable advancements achieved by our model in the realm of satellite image captioning hold significant promise for practical applications in domains such as image interpretation. the observed milestones represent a substantive progression within the field, showcasing adaptable utility across diverse sectors. Particularly noteworthy is the superior performance attained through the pairing of EfficientNet B7 (ENet) with LSTM, yielding accuracy rates exceeding 90% across both the Sydney Dataset and RSIC Dataset. this underscores the effectiveness of synergizing ENet with LSTM architectures for satellite image captioning tasks, thereby emphasizing its potential for real-world deployment. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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SFRSeg-Net: Synovial Fluid Region Segmentation from Rheumatoid Arthritis Affected Small Joints Using USG for Early Detection 27th

SFRSeg-Net: Synovial Fluid Region Segmentation from Rheumato...

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27th international conference on pattern recognition, ICPR 2024

作者： Das, Puja Roy, Sourav Dey Sangma, Kaberi De, Asim Bhowmik, Mrinal Kanti Suryamaninagar799022 India Agartala799006 India

ISBN: (纸本)9783031782008

Synovial fluid imbalance in joints plays a significant role in the diagnosis of Rheumatoid arthritis (RA) at an early stage. RA mostly attacks the small joints like finger and wrist joints which makes it challenging to segment the synovial fluid from those small joints automatically. Although ultrasonography (USG) imaging is very sensitive to small joints and its fluid assessment, segmentation of synovial fluid regions from the USG images in the literature are less understood. Moreover, towards computer vision related research (especially segmentation of the suspicious abnormal regions) using USG imaging, several challenges exists including (a) USG images are prone to the certain artifacts in terms of noises because of which the presence of the different appearance of synovial fluid is less distinguishable with respect to the other anatomical appearances, (b) Also, with respect to imbalance occurrence, synovial fluid changes its shape, size and locations from one USG image to another USG image. To cope with such pre-defined challenges, we proposed a novel lightweight network named as "Synovial Fluid Region Segmentation Network (SFRSeg-Net)" for segmentation of synovial fluid regions from the USG images. the proposed network enriches by incorporating a novel Interpretable Element Wise Additive-Discrete Wavelet Transform (IEWA-DWT) based down sampling strategy to extract the significant salient features by ignoring noise imposed in USG imaging and maintain the original image integrity. As synovial fluid varies its shape in one image to another image, so boundary loss is also significant learning parameter with pixel wise region loss and its mutual combination is used as a loss function in our proposed network. Experimental results on publicly available USG imaging dataset reveal that our proposed SFRSeg-Net performed well with Dice similarity coefficient of 0.9066 ± 0.0520 which surpasses both the most recent state-of-the-art techniques and the current baseline. © the

关键词： image segmentation

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CNN-Based Video Surveillance for Fire and Localization Detection 4

CNN-Based Video Surveillance for Fire and Localization Detec...

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4th international conference on Cognitive Computing and Information processing, CCIP 2022

作者： Sudharani Shirwaikar, Rudresh Deepak Mathews, Lincy Ramaiah Institute of Technology Department of Information Science Engineering Karnataka Bangalore560054 India Agnel Institute of Design and Technology Department of Computer Engineering Goa Bardez403507 India

ISBN: (纸本)9781665456487

Convolutional neural networks (CNNs) have shown success in picture classification and other computer vision issues. the use of CNN in the fire recognition technique will greatly enhance detection accuracy, resulting in fewer fire tragedies and societal and environmental consequences. However, as inference requires a lot of memory and computing power, a significant problem is implementing CNN-based fire sensing devices in an actual video network. We describe a new, energy-efficient, Fire detection, location, and semantic understanding using a computationally efficient CNN architecture scenario based on the Squeeze Net design. It applies compact convolutional kernels and avoids huge, completely linked layers to save computational load. Despite its minimal processing requirements, results of the experiment show that our suggested approach achieves accuracy = 99.7%, F1-score = 98.49%, Precision = 98.99%, and Recall = 98.00%. Furthermore, by considering the specific characteristics of the circumstance at hand as well as the variety of fire data, the study shows how the efficiency and accuracy of the fire detection model. © 2022 IEEE.

关键词： Fires

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th international conference on computer vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. the designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. the state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. this prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. the implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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An Anti-epidemic Delivery Trolley based on YOLOv5 image recognition of Room Numbers and QR Codes

An Anti-epidemic Delivery Trolley based on YOLOv5 Image Reco...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Saibiao Jiang Jianan Fan Zhijin Sun Baoru Deng School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Computer Engineering Zhuhai College of Science and Technology Zhuhai China

Since the massive spread of the COVID-19 epidemic in 2020 and the recent spread of influenza, the contactless delivery trolley has met the demands of the government for logistics against epidemics and the needs of logistics companies for deliveries. this paper designs a recognition method based on YOLOv5 (You Only Look Once version 5) for detecting room numbers and QR codes for the needs of anti-epidemic delivery trolleys and implements tasks such as stopping the anti-epidemic trolley at a specified room number and opening different warehouses according to different QR code information. the experiments improved the recognition by filtering the pepper noise through mean filtering. Experimental results show an increase of 2.105 % in the recognition rate of pre-processed QR codes, with an average accuracy rate of 98.9%. the delivery trolley also achieves a stable recognition rate of around 98.81% for room numbers.

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