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9th International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sujitha, S. Harshika vinoth Kumar, K. Hemavathi, v. Disha, M. Nafiza, Aamna Department of Eee New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350365092

The agricultural landscape is evolving, demanding innovative solutions to enhance productivity while ensuring the welfare of livestock. Farmguard introduces an advanced Automated Animal Detection and Monitoring System designed to revolutionize traditional farm management practices. Leveraging cutting-edge sensor technology, computer vision, and machine learning algorithms, Farmguard offers real-time, non-invasive monitoring of animal behavior, health, and movement within farm premises. This system operates seamlessly, utilizing a network of strategically placed sensors and cameras to track and identify individual animals. Through sophisticated image processing and AI-powered algorithms. This technology aims to minimize conflicts by providing early warnings about animal intrusion, enabling timely intervention strategies. By leveraging Arduino's capabilities and image processing techniques. © 2024 IEEE.

关键词： Internet of things

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Editorial: Introduction to the Special Issue on Deep Learning for High-Dimensional Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL processing 2022年第4期16卷 603-607页

作者： YUAN, X. I. N. BRADY, D. A. v. I. D. J. SUO, J. I. N. L. I. ARGUELLO, H. E. N. R. Y. RODRIGUES, M. I. G. U. E. L. KATSAGGELOS, A. G. G. E. L. O. S. K. Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China Univ Arizona Coll Opt Sci Tucson AZ 85719 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Ind Santander Dept Syst Engn & Informat Bucaramanga 680002 Colombia UCL Dept Elect & Elect Engn London WC1E 7JE England Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

The papers in this special section focus on deep learning for high-dimensional sensing. People live in a high-dimensional world and sensing is the first step to perceive and understand the environment for both human beings and machines. Therefore, high-dimensional sensing (HDS) plays a pivotal role in many fields such as robotics, signal processing, computer vision and surveillance. The recent explosive growth of artificial intelligence has provided new opportunities and tools for HDS, especially for machine vision. In many emerging real applications such as advanced driver assistance systems/autonomous driving systems, large-scale, high-dimensional and diverse types of data need to be captured and processed with high accuracy and in a real-time manner. Bearing this in mind, now is the time to develop new sensing and processing techniques with high performance to capture high-dimensional data by leveraging recent advances in deep learning (DL).

关键词： Special issues and sections Deep learning Robot sensing systems Surveillance Signal processing machine vision Computer vision Artificial intelligence Sensors

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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

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2024 IEEE International Conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru veera venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebasevisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 IEEE.

关键词： Natural language processing systems

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Development of Automatic Segmentation Techniques using Convolutional Neural Networks to Differentiate Diabetic Foot Ulcers

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INTERNATIONAL JOURNAL OF ADvANCED COMPUTER SCIENCE AND applications 2022年第11期13卷 521-526页

作者： Prakash, R. v. Kumar, K. Sundeep SEA Coll Engn & Technol Dept Comp Sci & Engn Bengaluru Karnataka India

The quality of computer vision systems to detect abnormalities in various medical imaging processes, such as dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), ultrasonography, and computed tomography, has significantly improved as a result of recent developments in the field of deep learning. There is discussion of current techniques and algorithms for identifying, categorizing, and detecting DFU. On the small datasets, a variety of techniques based on traditional machine learning and image processing are utilized to find the DFU. These literary works have kept their datasets and algorithms private. Therefore, the need for end-to-end automated systems that can identify DFU of all grades and stages is critical. The study's goals were to create new CNN-based automatic segmentation techniques to separate surrounding skin from DFU on full foot images because surrounding skin serves as a critical visual cue for evaluating the progression of DFU as well as to create reliable and portable deep learning techniques for localizing DFU that can be applied to mobile devices for remote monitoring. The second goal was to examine the various diabetic foot diseases in accordance with well-known medical categorization schemes. According to a computer vision viewpoint, the authors looked at the various DFU circumstances including site, infection, neuropathy, bacterial infection, area, and depth. machine learning techniques have been utilized in this study to identify key DFU situations as ischemia and bacterial infection.

关键词： Magnetic resonance imaging (MRI) diabetic foot ulcers (DFU) convolutional neural networks ischemia& machine learning algorithms & dual-energy x-ray absorptiometry

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Employing a Hybrid Convolutional Neural Network and Extreme Learning machine for Precision Liver Disease Forecasting

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INTERNATIONAL JOURNAL OF ADvANCED COMPUTER SCIENCE AND applications 2024年第2期15卷 708-721页

作者： Deshmukh, Araddhana Arvind Krishna, R. v. v. Salman, Rahama Sandhiya, S. Balajee, J. Pilli, Daniel Symbiosis Skill & Profess Univ Sch Comp Sci & Informat Technol Cyber Secur Pune Maharashtra India Aditya Coll Engn Technol ECE Dept Surampalem 5334372 India Jazan Univ Coll Comp Sci & Informat Technol Dept Informat Technol & Secur Jazan Saudi Arabia Panimalar Engn Coll Dept IT Chennai Tamil Nadu India Mother Theresa Inst Engn & Technol Dept Comp Sci & Engn Chittoor 517408 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept MBA Chennai India

This paper discusses the critical relevance of precise forecasting in liver disease, as well as the need for early identification and categorization for immediate action and personalized treatment strategies. The paper describes a unique strategy for improving liver disease classification using ultrasound image processing. The recommended technique combines the properties of the Extreme Learning machine (ELM), Convolutional Neural Network (CNN), along Grey Wolf Optimisation (GWO) to form an integrated model known as CNN-ELM-GWO. The data is provided by Pakistan's Multan Institute of Nuclear Medicine and Radiotherapy, and it is then pre-processed utilizing bilateral and optimal wavelet filtering techniques to increase the dataset's quality. To properly extract significant visual information, feature extraction employs a deep CNN architecture using six convolutional layers, batch normalization, and max-pooling. The ELM serves as a classifier, whereas the CNN is a feature extractor. The GWO algorithm, based on grey wolf searching strategies, refines the CNN and ELM hyperparameters in two stages, progressively boosting the system's classification accuracy. When implemented in Python, CNN-ELM-GWO exceeds traditional machine learning algorithms (MLP, RF, KNN, and NB) in terms of accuracy, precision, recall, and F1-score metrics. The proposed technique achieves an impressive 99.7% accuracy, revealing its potential to significantly enhance the classification of liver disease by employing ultrasound images. The CNN-ELM-GWO technique outperforms conventional approaches in liver disease forecasting by a substantial margin of 27.5%, showing its potential to revolutionize medical imaging and prospects.

关键词： Liver disease prognosis convolutional neural network extreme learning machine grey wolf optimization patient care

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machine vision based automated 3-DOF Articulated Robot for fruit defect Identification and Segregation 14

Machine Vision based automated 3-DOF Articulated Robot for f...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramkumar, S. venusamy, Kanagaraj Eswaran, A. Jeevitha, N. Ramyapriya, G. Kannadhasan, S. Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering thandalam Chennai India Roever Engineering College Department of Electrical and Electronics Engineering Perambalur India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore Chennai India

ISBN: (纸本)9798350335095

The automation scenario in the current industrial as well as domestic applications has seen an exponential growth over the decade. Robot plays an important role in industrial automation but in some cases, it needs some extent of human intervention in quality inspection. This paper focuses on solving problems related to defect identification and segregation of fruits using 3-DOF articulated robot configuration with the integration of machine vision system and deep learning algorithms. To perceive the features of object, A USB camera is utilized. The machine vision algorithm will next use a variety of methods for processing digital images to extract the relevant data and decide whether to continue processing the product, redirect it to a different stage of production, or just throw it away. In recent days, the advancement in deep learning leads to the phenomenal growth in computer vision. There are many state-of-the-art object detection algorithms available in deep learning technology and we are using Inception-v3 algorithm that can be used for the machine vision operation in our solution. Based on the results from the algorithm, segregation operation is carried out successfully. © 2023 IEEE.

关键词： Fruits

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Superior Attribute Weighted Set for Object Skeleton Detection using ResNet50 with Edge based Segmentation Model 2

Superior Attribute Weighted Set for Object Skeleton Detectio...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Narayana, v. Lakshman vinayaki, K. vaishnavi Swetha, P. Ayyar Sri, K. Divya Chaithanya, G. Vignans Nirula Inst Technol & Sci Women Dept Comp Sci & Engn Peda Palakaluru Rd Guntur 522009 Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

Object detection is a method used in computer vision for identifying specific items inside an image or video. Most effective object detection systems make use of machine learning or deep learning. Object detection is a method of computer vision that allows us to find specific things in pictures and videos. Labeling and counting items in a scene, as well as pinpointing their locations and following their movement, are all possible because to object detection's ability to precisely identify and localize them. For instance, it is easy to recognize circles as a distinct class because of their shared characteristic of being spherical. These unique characteristics are used for object class recognition. Facial traits like as skin tone and eye distance are employed in a manner analogous to that used for fingerprinting in order to positively identify a person by their face. The object detection task is typically made much more challenging due to the test images being sampled from a distinct data distribution. Many unsupervised domain adaptation approaches have been presented to solve the difficulties introduced by the discrepancy between the domains of the training and test data. Cross-domain object detection has many applications, including autonomous driving because to the ease with which labels can be generated for a large number of scenes in video games. Object detection methods can be categorized as either neural network-based or non-neural. This research presents a Superior Attribute Weighted Set for Object Skeleton Detection using ResNet50 (SAWS-OSD-ResNet50). The proposed model when compared with the traditional methods performs better in object detection.

关键词： image Segmentation Object Detection Computer vision image Annotation Face detection ResNet 50

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Facial Micro-Expression Recognition using Deep Spatio-Temporal Neural Networks 32

Facial Micro-Expression Recognition using Deep Spatio-Tempor...

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Conference on Signal processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Zheng, Yufeng Blasch, Erik Univ Mississippi Med Ctr Jackson MS 39216 USA MOVEJ Analyt Fairborn OH USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

In the billions of faces that are shaped by thousands of different cultures and ethnicities, one thing remains universal: the way emotions are expressed. To take the next step in human-machine interactions, a machine must be able to clarify facial emotions. Allowing machines to recognize micro-expressions gives them a deeper dive into a person's true feelings at an instant which allows designers to create more empathetic machines that will take human emotion into account while making optimal decisions;e.g., these machines will be potentially able to detect dangerous situations, alert caregivers to challenges, and provide appropriate responses. Micro-expressions are involuntary and transient facial expressions capable of revealing genuine emotions. We propose to design and train a set of neural network (NN) models capable of micro-expression recognition in real-time applications. Different NN models are explored and compared in this study to design a hybrid deep learning model by combining a convolutional neural network (CNN), a recurrent neural network (RNN, e.g., long short-term memory [LSTM]), and a vision transformer. The CNN can extract spatial features (of a neighborhood within an image) whereas the LSTM can summarize temporal features. In addition, a transformer with an attention mechanism can capture sparse spatial relations residing an image or between frames in a video clip. The inputs of the model are short facial videos, while the outputs are the micro-expressions gleaned from the videos. The deep learning models are trained and tested with publicly available facial micro-expression datasets to recognize different micro-expressions (e.g., happiness, fear, anger, surprise, disgust, sadness). The results of our proposed models are compared with that of literature-reported methods tested on the same datasets. The proposed hybrid models perform the best.

关键词： Facial micro-expression Human-machine interaction Long short-term memory (LSTM) Convolutional neural network (CNN) vision transformer Deep learning

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A Real-Time Multimodal Deep Learning for image-to-Cartoon Conversion 3

A Real-Time Multimodal Deep Learning for Image-to-Cartoon Co...

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3rd International Conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Karthik, Raja Pavan vamsi, Kalla Yadu Reddy, veeramreddy Sourya Tejarsha Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350343632

In the era of digital imagery, there is a great interest in finding new and creative ways to express ourselves and make our images look beautiful. One such fascinating method is cartoonization, a process that transforms ordinary images into visually appealing cartoon images. This paper explores the integration of cutting-edge computer vision algorithms, traditional image processing methods, and Neural Networks to achieve cartoonization. The main focus is on combining object segmentation with cartoonization in a smooth and seamless way, which offers a unique and innovative approach to improving images. By thoroughly considering various techniques and how they can be used together, our research not only gives a complete understanding of these methods but also highlights how they can transform the field of digital artistry. By exploring the integration between methods, the study sheds light on how these techniques contribute to the evolving landscape of digital artistry. The research suggests that the fusion of computer vision, traditional image processing, and machine Learning techniques holds promising potential for pushing the boundaries of creative expression in the digital realm, offering new ways for creating efficient cartoon images. © 2023 IEEE.

关键词： Cartoonification Cartooning Color Quantization Edge Detection Region-Based Convolutional Neural Networks (R-CNN)

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Detecting Functional Objects using Multi-Modal Data 5

Detecting Functional Objects using Multi-Modal Data

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Conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications v

作者： Ellis, Seth T. Harrison, Andre v. DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

The output of a semantic segmentation model on an off-road dataset can provide an accurate description of the terrain and the obstacles contained within it. This output can be leveraged to determine the presence of barriers in an image. An obstacle is anything that may obstruct a portion of the region of traversal, while we define a barrier as something that will bisect the region of traversal to create two disjoint regions that would otherwise be connected if not for its presence. Detecting instances of barriers requires more than learning the correct label for a standard 2D semantic segmentation model. This paper will present an approach to detect the presence of barriers in the scene by utilizing the traversability of the semantic classes of non-traversal and the pose of that class(es) about other classes of non-traversal in the scene to define an object as a barrier. For this approach, semantic segmentation is leveraged to assign the classes within an image as "traversable" and "non-traversable". Our approach fuses visible camera segmentation models with LiDAR point cloud data to estimate the local environment's semantic classes and 3D geometry. To assess the algorithm's accuracy, it will be presented with a multitude of scenarios that either contain barriers or not, and its output will be compared to the intention of the environment it was placed in.

关键词： Obstacle detection scene understanding multi-modal fusion computer vision Barricade detection multi-modal perception

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