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A Deep Assessment of Thermal image-Base Object Detection for a Wide Range of applications

A Deep Assessment of Thermal Image-Base Object Detection for...

Artificial Intelligence and machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Kamaleswari Pandurangan Krishnaraj Nagappan Dept. of Networking and Communication School of Computing SRM Institute Science and Technology SRM University Kattankulathur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Computer vision enables for the detection of things even in densely populated areas. This work investigates autonomous object detection and activity recognition in static and dynamic images, as well as their potential and restrictions. Several object identification challenges are addressed in relation to illness diagnosis, identifying wild animals, and identifying industrial activities, such as processing, segmentation, and extraction techniques for various object features. It is also critical to highlight the limitations, gaps, technical problems, applicability, and state-of-the-art of the various techniques. Shape-based detection, skin color extraction, edge identification, and an object detector are all approaches we use to identify and classify objects. This contrasts with motion detection, which is less precise when utilized at night. Thermal imaging has been used to track objects in a variety of applications, including surveillance systems and the identification of people and wildlife. We plan to evaluate new object detectors such as YOLO and RCNN. Another route for future research was suggested: investigate various object identification systems, particularly those that deal with thermal video or image detection.

关键词： YOLO Tracking Surveillance Motion segmentation Wildlife Detectors Feature extraction

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An Overview of Organs-on-Chips Based on Deep Learning

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Research 2022年第2期2022卷 141-160页

作者： Jintao Li Jie Chen Hua Bai Haiwei Wang Shiping Hao Yang Ding Bo Peng Jing Zhang Lin Li Wei Huang Frontiers Science Center for Flexible Eletronrics Xi'an Institu te of Flexible Electronics(IFE)and Xr'an Institute of Biomedical Materials EngineeringNorthwesterm Polytechnical UniversityXi'an 710072China Key Laboratory of Ietlieant Computing and Signal Processing of Minsry of Educaton School of Electronics and Information EngineeringAnhui UriversityHefei 230601China 38th Research Institute of China Eilectronics Technology Grouep Corporation Hiefei 230088 China Collge of Biomedical Engimeering Sichuan UntiversityChengdu 610065China Key Laboratory of Flexible Elecronics(KLOFE)and Institute of Advanced Materials(IAM)Nanjing Tech University(NanjingTech)J Nanjing 211800 China

Microfluidic-based organs-on-chips(OoCs)are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro *** miniaturization,stimulated tissue mechanical *** microenvironment of OoCs offer unique properties for biomedical ***,the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical *** learning,an emerging area of research in the field of machine learning,can automatically mine the inherent characteristics and laws of"big data"and has achieved remarkable applications in computer vision,speech recognition,and natural language *** integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development,disease modeling,and personalized *** review bricfly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful *** then analyze thecombination of OoCs and deep learning for image digitization,data analysis,and ***,the problems faced in current applications are discussed,and future perspectives and suggestions are provided to further strengthen this integration.

关键词： Deep holds beyond

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Automated skull damage detection from assembled skull model using computer vision and machine learning

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International Journal of Information Technology (Singapore) 2021年第5期13卷 1785-1790页

作者： Mangrulkar, Amol Rane, Santosh B. Sunnapwar, Vivek Department of Mechanical Engineering Sardar Patel College of Engineering Andheri (W) Mumbai Maharashtra India

In the biomedical domain, the technologies like 3D computer vision and Bio-CAD arriving significant attention for computerized diagnosis, analysis and treatment of head and neck fractures. The advanced medical scanning devices internally scan and assemble the fragmented geometric data of the human body. The assembled skull model frequently suffers from damages caused by the process of the skull assembly process. Such damaged skull data may lead to missing some vital data for further medical analysis. Thus it is necessary to have an automatic mechanism of skull prototyping or completion before detect damaged skull models and repair them automatically for medical investigation. Automatic skull damage detection approach proposed using computer vision and machine learning methods in this paper. The input skull model in 3D format converted into 2D followed by the pre-processing operation to denoise and enhance the image quality. Then the Region of Interest (ROI) performed a dynamic binary segmentation technique. The automatic and manual features extracted from ROI using Convolutional Neural Network (CNN) layers and hybrid methods respectively. The hybrid model includes the structural, regional, and histogram features followed by its concatenation and normalization. The hybrid feature set is feed to conventional machine learning methods for skull damage detection. Automatic damage detection in input skull image is performed by the consolidated deep learning model using CNN (For features extraction) and Long-Short Term Memory (LSTM) for categorization called CNN-LSTM. The experimental outcomes show the high classification accuracy using the deep learning model compared to other machine learning techniques. © 2021, Bharati Vidyapeeth's Institute of Computer applications and Management.

关键词： Bio-cad Biomedical image processing Computer vision methods Convolutional neural network Geometric data Skull damage detection

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Demographic attribute estimation in face videos combining local information and quality assessment

Demographic attribute estimation in face videos combining lo...

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作者： Becerra-Riera, Fabiola Morales-González, Annette Méndez-Vázquez, Heydi Dugelay, Jean-Luc 7A #21406 Siboney Havana PlayaP.C.12200 Cuba Digital Security Department EURECOM Campus Sophia Tech 450 route des Chappes Biot Sophia Antipolis06410 France

Nowadays, video analysis applications are gaining popularity given the rise of CCTV systems and the availability of video cameras to the general public, such as cameras in mobile devices. Many image analysis and processing tasks have evolved toward video domain, with the advantage of redundant information obtained from several frames, which can help disambiguating many recognition outputs. In this context, there are also particular video problems to deal with, such as uncontrolled scenarios and poor image quality. Most existing works regarding facial demographic estimation are focused on still image datasets;therefore, we propose to address gender and age estimation in video scenarios. In order to handle known video problems such as low-quality image capture, occlusions and pose variations, we propose a threefold strategy to adapt current image-based attribute recognition algorithms. First, we employ a quality assessment step based on 12 metrics to select relevant good quality frames from a face video sequence. Second, we propose a component-based approach to determine the most discriminant local regions of the face for each specific attribute, under these varying conditions. Third, we evaluate different frame combination strategies to produce the final video prediction. In our experimental validation, conducted in 3 datasets (EURECOM Augmented, UvA-Nemo Smile and YouTube Faces datasets), we show the advantages of our proposed strategy for improving video-based demographic attribute classification. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： image quality

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Volumetric Feature Extraction from 2D images Using Cubixels

Volumetric Feature Extraction from 2D Images Using Cubixels

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International Conference on machine Learning and applications (ICMLA)

作者： Sanad Aburass Maha Abu Rumman Department of Computer Science Luther College Decorah Iowa USA Department of Computer Science Maharishi International University Fairfield Iowa USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This paper introduces innovative volumetric feature extraction methods for 2D images using Cubixels and the Volume of the Void (VoV) metric. Cubixels transform traditional pixels into three-dimensional entities, capturing more detailed spatial and color relationships by assigning RGB values to the dimensions of width, length, and height. The VoV metric quantifies spatial gaps between adjacent Cubixels, providing valuable insights into textural characteristics and edge acuity. We developed two methods: the first focuses on calculating VoV between Cubixels, while the second integrates gradient and curvature features to enhance geometric feature extraction. These methods were applied to the CIFAR-10 and MNIST datasets and evaluated using four convolutional neural network (CNN) models incorporating residual connections, spatial dropout, and attention mechanisms. Our experimental results demonstrate significant improvements in model performance, including higher validation accuracy and reduced overfitting, as indicated by the Overfitting Index. This study underscores the potential of volumetric feature extraction in advancing image processing and computer vision, paving the way for more sophisticated and context-aware AI applications.

关键词： Measurement Computer vision Attention mechanisms Accuracy Computational modeling Transforms Feature extraction Convolutional neural networks Indexes Overfitting

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Deep Learning-Based Identification of Birds in Agricultural Fields

Deep Learning-Based Identification of Birds in Agricultural ...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chaturvedi, Keya Nithin Skantha, M. Navanithi, V.K. Pranesh, R. Harissh, G. Tangavel, Senthil Kumar Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641042 India

ISBN: (纸本)9798331510299

Studies on bird species provide important informa- tion about the biological systems that support the transportation of all life on Earth, including humans. Understanding bird species, their characteristics, migration patterns, and life cycles is important for maintaining ecological balance and a healthy planet. Recent advances in artificial intelligence (AI) and imaging tools have greatly improved the identification of bird species, helping in ecological research and agricultural management. However, identifying bird species in the field by photographing them remains a difficult and time-consuming task due to the similarity of class and quality attributes. Our research aims to address this challenge using deep learning, a machine learning technique that uses deep neural networks to identify birds accurately. We strive to develop and evaluate various CNN models such as VGG- 19, Inception V3, DenseNet-201, EfficientNet-V2, and MobileNet-V3 to analyze the majority using advanced com- puter vision tools such as transfer learning and deep quantum learning. The model is effective. The model will form the basis for the construction of the most advanced (SOTA) classification of birds, achieving 88 percent accuracy with the DenseNet-201 model. In addition, innovations such as Bird@Edge, an instant bird identification and AI-based deterrent system designed to protect plants from harmful birds, demonstrate our mastery of applications in eight of our work in biodiversity monitoring and plant protection. Future developments will focus on improving the accuracy and functionality of the model, making it more useful for ecological protection and permaculture promotion, plant protection, biodiversity management. © 2024 IEEE.

关键词： biodiversity management Bird species convolutional neural networks crop protection deep learning deep quantum learning image processing

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A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local machine Learning Models for Instagram and TikTok

A Picture is Worth 500 Labels: A Case Study of Demographic D...

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IEEE Symposium on Security and Privacy

作者： Jack West Lea Thiemt Shimaa Ahmed Maggie Bartig Kassem Fawaz Suman Banerjee University of Wisconsin-Madison Technical University of Munich

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Mobile apps have embraced user privacy by moving their data processing to the user’s smartphone. Advanced machine learning (ML) models, such as vision models, can now locally analyze user images to extract insights that drive several functionalities. Capitalizing on this new processing model of locally analyzing user images, we analyze two popular social media apps, TikTok and Instagram, to reveal (1) what insights vision models in both apps infer about users from their image and video data and (2) whether these models exhibit performance disparities with respect to demographics. As vision models provide signals for sensitive technologies like age verification and facial recognition, understanding potential biases in these models is crucial for ensuring that users receive equitable and accurate *** develop a novel method for capturing and evaluating ML tasks in mobile apps, overcoming challenges like code obfuscation, native code execution, and scalability. Our method comprises ML task detection, ML pipeline reconstruction, and ML performance assessment, specifically focusing on demographic disparities. We apply our methodology to TikTok and Instagram, revealing significant insights. For TikTok, we find issues in age and gender prediction accuracy, particularly for minors and Black individuals. In Instagram, our analysis uncovers demographic disparities in extracting over 500 visual concepts from images, with evidence of spurious correlations between demographic features and certain concepts.

关键词： Analytical models Video on demand Pipelines machine learning Data models Mobile applications Web sites

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Steel Pipe Counting System based on image Recognition 13

Steel Pipe Counting System based on Image Recognition

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13th International Conference on machine vision

作者： Jia, Yuxin Xu, Jie Lei, Qujiang Li, Xiuhao Gui, Guangchao Wang, Weijun Chinese Acad Sci Guangzhou Inst Adv Technol Guangzhou Peoples R China

ISBN: (纸本)9781510640412

Traditional steel pipe counting can only be performed manually, which has the problems of large workload, error-prone and low efficiency. This paper proposes a steel pipe counting system based on image recognition. First of all, the steel pipe image is collected from the camera, and then the digital image processing technology is used to perform image enhancement, edge detection and morphological operation and other preprocessing, then the steel pipe is identified and counted by the Hough circle transformation, and finally the steel pipe automatic is developed using VC++ software Technical software. Experiments show that the automatic counting accuracy and efficiency of steel pipes in this system are high.

关键词： image recognition steel pipe counting Hough transform

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Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

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machine vision AND applications 2024年第4期35卷 75-75页

作者： Kirupakaran, Anish Monsley Laskar, Rabul Hussain Natl Inst Technol Silchar Dept ECE Cachar 788010 Assam India

Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted with case-sensitive characters in the English alphabet. The literature underscores a notable gap, the absence of an open-source multi-scale un-instructional gesture database featuring a comprehensive dictionary. In response, we have created the NITS (gesture scale) database, which encompasses isolated mid-air gesticulations of ninety-five alphanumeric characters. In this research, we present a scale-centric framework that addresses three critical aspects: (1) detection of smaller gesture objects: our framework excels at detecting smaller gesture objects, such as a red color marker. (2) Removal of redundant self co-articulated strokes: we propose an effective approach to eliminate redundant self co-articulated strokes often present in gesture trajectories. (3) Scale-variant approach for recognition: to tackle the scale vs. size ambiguity in recognition, we introduce a novel scale-variant methodology. Our experimental results reveal a substantial improvement of approximately 16% compared to existing state-of-the-art recognition models for mid-air gesture recognition. These outcomes demonstrate that our proposed approach successfully emulates the perceptibility found in the human visual system, even when utilizing data from monophthalmic vision. Furthermore, our findings underscore the imperative need for comprehensive studies encompassing scale variations in gesture recognition.

关键词： Gesture computing Computer vision Deep learning Scale variance Human-computer interaction

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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