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FPGA based Feature Extraction in Real time computer vision-A Comprehensive Survey 2

FPGA based Feature Extraction in Real time Computer Vision-A...

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2nd international conference on Signal and Information processing, IConSIP 2022

作者： Gupta, Isha Kaur, Deepti Prit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781728168852

Today, computer vision algorithms play a vital role in almost every domain of our day-to-day life. This powerful technology has aided in the development of answers to a wide range of difficult and complex challenges encountered across different sectors like defence, security, surveillance, robotics, healthcare, transportation, manufacturing, agriculture, sports, retail etc. Feature Extraction is one of the key components in any computer vision system and it aims at extracting the distinct and relevant features from an image that could clearly characterize different objects present in it. Almost every computer vision application like image matching, object detection and recognition, human or pedestrian detection, robotic vision etc. rely on the feature extraction techniques as one of their primary steps. Recently various hardware accelerators have been developed for these feature extraction processes and among these Field Programmable Gate Array (FPGA) is considered as the most suitable platform because of its added advantages of flexibility, parallelism, low power, and re-configurability. This paper presents, a review of the literature on FPGA implementations of various efficient feature extraction methods, such as Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HoG). The review covers the different implementation techniques followed by researchers for efficient mapping on hardware and provides a comparison of their work on the basis of parameters like speed and resource utilization. © 2022 IEEE.

关键词： Feature extraction

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vision based Driver Drowsiness Detection using Deep Learning 2

Vision based Driver Drowsiness Detection using Deep Learning

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2nd international conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Spandana, S. Srividhya, M. Rao, Yedavalli Venkata Raghava Devika, P. Srikanth, V. Parvathi, S. Koneru Lakshmaiah Education Foundation Cse Dept Bowrampet Telangana Hyderabad500043 India Department of Computer Science and Information Technology Mlr Institute of Technology Telangana India Cse Aiml Avniet Jntuh India and Ai&ds Vnr Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350370188

Every year many people lose their lives in road accidents, according to a survey many road accidents occur due to driver fatigue condition. Feeling sleepy, tiredness due to long journeys, being on alcohol while driving has costed many lives. In order to save lives a perfect technique must be implemented to detect driver drowsiness and alarm the driver before it costs the driver life. Deep Learning is an emerging technology has given solution to many problems in different domains. Deep Learning can solve this problem using Convolutional Neural Networks and image processing techniques. Proposed system solves the above-mentioned problem using object detection, driver video from the embedded devices in the vehicles is taken as input and identifies if eyes of driver in video are closed or opened and eventually an alarm sent. Object detection is done using MobileNet convolutional neural network architecture along with SSD (Single Shot Detector). On the output given from MobileNet and SSD architecture an algorithm is applied to detect drowsiness of the driver. We take 6000 images from different datasets which consists of images of people driving, eyes open, eyes closed, yawning and use them to train the model and then we label the images into different classes like face, open eyes, closed eyes. The trained model is then tested using 300 images taken randomly from the same dataset. Also we calculate evaluation measures like accuracy, precision, recall and F1 score for the model which is trained. The proposed model gives highest accuracy with less cost of implementation. Since, we use video of the driver taken from the embedded devices like mobile without any expensive hardware. The proposed model is efficient in accuracy, computation and also cost. This model can be deployed on to different embedded devices present in vehicles without any difficulty. The results of proposed model has shown accuracy of 97% and average mean precision of 0.84. © 2024 IEEE.

关键词： Object detection

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Software to Assist Visually Impaired People During the Craps Game Using Machine Learning on Python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd international conference on Smart Technologies, Systems and Applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

Pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a Python API to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost Python API, mainly aimed at visually impaired people.

关键词： Craps game Visually impaired people Non controlled environment Python API Artificial vision techniques image processing

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Neural Radiance Field Based on Deep Dynamic Optimization Sampling

Neural Radiance Field Based on Deep Dynamic Optimization Sam...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Boyuan Zhao Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Neural Radiance Field (NeRF) is an advanced image rendering technology, but generating high-quality images requires a large amount of training data and dense sampling points, which affects training efficiency. We propose the Sample-NeRF model, which generates depth information Structure From Motion (SFM) to guide sampling, and introduces position offsets to optimize the layout of sampling points and reduce invalid sampling. This model uses depth information to improve geometry learning and avoid overfitting, and can maintain high rendering quality even with fewer training samples.

关键词： Training Point cloud compression Structure from motion Heuristic algorithms Machine vision Training data Neural radiance field Rendering (computer graphics) Sampling methods Optimization

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image Captioning Based on Convolutional Neural Network and Transformer 2

Image Captioning Based on Convolutional Neural Network and T...

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2022 2nd international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Haidarh, Mosa Zhang, Shuang College of Software Northeastern University Shenyang Liaoning 110819 China

ISBN: (纸本)9783800760268

image captioning refers to the automatic description of images using words, and the task has sparked the interest of researchers in the fields of computer vision and NLP. In recent years, most works on image captioning used the encoder-decoder framework where the encoder is used to get the image features vectors, and the decoder takes these features vectors to generate the description of the image. By combining CNN and LSTM, the encoder-decoder framework for image captioning has achieved significant progress;Additionally, by including attention mechanisms into this framework, the performance of the captioning models was significantly enhanced. Furthermore, the Transformer, which depends on attention in its work, is better in terms of performance and efficiency in NLP missions compared with LSTM. Based on the previous ideas, we proposed a model based on CNN and Transformer that achieved accurate image captions. With the proposed model, the Transformer-Encoder is used to get a new representation of the image features to help the Transformer-Decoder focus on the most relevant part of the image when generating a new word. We also employ adaptive attention in the Transformer-Decoder to determine when and where the decoder uses the image information. The experiments were done on the combined datasets of Flickr30K, Flickr8K, and 1K images collected by us, and the testing results prove that the work is effective and valuable. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Natural language processing systems

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Low-light image Enhancement Algorithm Based on Transformer

Low-light Image Enhancement Algorithm Based on Transformer

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Signal processing and Intelligent Computing (SPIC), international conference on

作者： Zeyu Wang Yan Qi Institute of Information Science and Technology Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition due to the presence of large amounts of noise and low contrast. Therefore, research on low-light image enhancement is crucial. To address these issues, this paper delves into the comparison between traditional and non-traditional low-light image enhancement algorithms and has achieved the following results:We abandon the traditional approach based on a priori knowledge of images and instead redesign a new network structure for low-light image enhancement with the existing theoretical foundation for Transformer. The network performs information extraction in the channel dimension of the image through self-attention mechanism and then extracts the shallow feature information of the image through Shallow layer. After fusing these two types of information, more advanced information is extracted to guide the final image enhancement. Experimental results show that the redesigned network structure not only achieves better results but also has improved execution efficiency.

关键词： Knowledge engineering computer vision Target tracking Target recognition Noise Signal processing algorithms Feature extraction Transformers Data mining image enhancement

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Video Captioning based on image Captioning as Subsidiary Content 2

Video Captioning based on Image Captioning as Subsidiary Con...

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2nd IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Vaishnavi, J. Narmatha, V Annamalai Univ Dept Comp & Informat Sci Annamalainagar India

ISBN: (纸本)9781665411202

Video captioning is the more heuristic task of the combination of computer vision and Natural language processing while researchers are concentrated more in video related tasks. Dense video captioning is still considering the more challenging task as it needs to consider every event occurs in the video and provide optimal captions separately for all the events presents in the video with high diversity. Captioning process with less corpus leads to less performance. To avoid such issues, our proposed model constructed with the option of generating captions with high diversity. image captions are taken as subsidiary content to enlarge the diversity for captioning the videos. Attention mechanism is utilized for the generation process. Generator and three different discriminators are utilized to contribute an appropriate caption which enriches the captioning process. ActivityNet caption dataset is used to demonstrate the proposed model. Microsoft coco image dataset is considered as subsidiary content for captioning. The benchmark metrics BLEU and METEOR are used to estimate the performance of the proposed model.

关键词： video captioning image captioning subsidiary images adversarial inference language generator discriminator attention mechanism

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Deep Neural Networks for image-Based Indian Sign Language Recognition: A Comprehensive Review and Practical Analysis 1

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2nd international conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kaur, Harleen Bedi, Sanjam Kaur Rao K, Mallikharjuna Lekhana, M.A. Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Addressing the communication barriers faced by individuals with vocal and hearing disabilities, sign language serves as a visual means of expression. However, the limited understanding of sign language among the general population creates significant challenges in various settings, such as banks, airports, and supermarkets. To overcome this, a sign language recognition (SLR) system is essential. This study focuses on developing a real-time word-level sign language recognition system that translates sign language into text, with a specific emphasis on Indian sign language (ISL) to cater to the needs of the deaf and hard-of-hearing community in India. Our research presents an ISL-based sign language recognition system, enabling users to capture hand movement images through a web camera, with the system accurately predicting and displaying the corresponding sign name. The captured images undergo several processing stages, incorporating computer vision techniques like grayscale conversion, dilatation, and masking. To achieve robust recognition, a convolutional neural network (CNN) is trained on the collected dataset, demonstrating exceptional performance with a 99% accuracy rate. This research contributes to the advancement of assistive technologies by providing an effective solution for real-time ISL recognition, promoting inclusive communication and accessibility for individuals with hearing and vocal impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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A Portable System for Automated Measurement of Striped Catfish Length Using computer vision 2nd

A Portable System for Automated Measurement of Striped Catfi...

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2nd international conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Phong, Le Hong Truong, Nguyen Phuc Danh, Luong Vinh Quoc Nam, Vo Hoai Tung, Nguyen Thanh Dung, Tu Thanh Can Tho Univ Coll Engn Can Tho City Vietnam Vinh Long Univ Technol Educ Vinh Long Vinh Long Vietnam Can Tho Univ Coll Aquaculture & Fisheries Can Tho City Vietnam

ISBN: (纸本)9789811925412;9789811925405

In fish farming, regular measurement of fish length is a necessary work to evaluate the growth and health status of fish. However, manual measurement of fish length using measuring board is a time-consuming task and subject to error due to human bias factor. In this paper, the authors present the use of image processing techniques and smartphone cameras for automated measurement of striped catfish length. The system was designed to be portable by taking advantage of laptop computers and high-performance cameras found on most smartphones. The image processing algorithms include the following steps: take image of fish, convert image to binary one, find contours and draw masks, extract the region of interest, determine fish orientation, find the position of fish's caudal peduncle, and calculate fish length. The system algorithms were implemented in Python programming language in combination with the OpenCV library. Experimental validation with 30 striped catfish samples shows that the designed system can provide an average accuracy of 97.71%. The proposed method could be a potential alternative of fish length measurement to support fisheries industry.

关键词： Catfish computer vision Fish length measurement OpenCV

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image Super Resolution using U-Net architecture and SRGAN: Comparative Analysis

Image Super Resolution using U-Net architecture and SRGAN: C...

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computer, Communication and Control (IC4), international conference on

作者： Pranali Dandekar Bhavika Bhojwani Aarya Balpande Sanskar Zanwar Ankan Deb Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning Shri Ramdeobaba College of Engineering and Management Nagpur India

Super-resolution is an important image processing task that improves the resolution of high-resolution images. Low resolution is used in many different fields, including medical imaging, satellite imagery, and computer vision. This paper presents a new approach to ultra-high resolution using the U-Net architecture, a deep learning framework known for its success in image segmentation and restoration *** this study, we propose an adaptive U-Net model specifically designed for ultra-high-resolution tasks. The architecture includes an encoder-decoder network with bypass connections, enabling multi-scale feature extraction and high-resolution detail reconstruction. Our model is trained on a diverse dataset of low- and high-resolution image pairs, allowing it to learn complex relationships and patterns in *** evaluate the performance of the U-Net based superresolution method using standard image quality metrics and qualitative visual evaluation. Test results show a significant improvement in image quality, with improved sharpness, texture, and detail recovery. Furthermore, our model outperforms state-of-the-art super-resolution methods in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

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