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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1646 条记录，以下是1451-1460 订阅

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Enhancing Human-computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV

Enhancing Human-Computer Interaction: Hand Detection for Air...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Pranjal Shukla Prasenjit Das Department of CSE Chandigarh University Mohali Punjab India

In recent years, there has been a remarkable increase in interest and challenges in image processing and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf.

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Research on Object Detection Algorithm Based on Improved Yolov5 3rd

Research on Object Detection Algorithm Based on Improved Yol...

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3rd international conference on Artificial Intelligence, 2022

作者： Chen, Shuping Chen, Bingcai School of Computer Science and Technology Xinjiang Normal University No. 102 Xinyi Road Xinjiang Urumqi830054 China School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789811694226

Object detection is an important fundamental problem in computer vision research, and is also the basis for other high-level visual tasks such as object tracking, behavioral analysis, and image description. Since target recognition is the simultaneous recognition and assessment of multiple targets, the accuracy of target recognition is generally low. In order to improve the accuracy of target detection, this paper introduces the Efficient Channel Attention (ECA) module to optimize the original yolov5 model, which has fewer parameters and significantly improved performance. After the ECA module performs channel-by-channel global average pooling without reducing the dimensionality, it considers each channel and its k neighbors to capture local cross-channel interactions, which enhances the discrimination of features. Experiments on the coco data set show that the improved yolov5 model based on the ECA module proposed in this paper can be used 68.84% accuracy to detect these object. Compared with the comparison algorithm, the training accuracy of the yolov5 model integrated with the ECA module is improved 3.02%, which proves that the ECA module can further improve the accuracy of yolov5, and can meet the requirements of reliability and stability of target detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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computer vision assisted Deep Learning based Sitting Posture Suggestion System

Computer Vision assisted Deep Learning based Sitting Posture...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Ghanashyam Vagale GK Prajwal Rohit Kumar P. Padma Priya Dharishini Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user’s posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user’s daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm’s efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals.

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A prototype 1024 electrode embedded computer based Epiretinal Prosthesis System

A prototype 1024 electrode embedded computer based Epiretina...

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international conference on Signal processing and Integrated Networks (SPIN)

作者： N. Sharmili V. Bhujanga Rao P. Seetharamaiah N. Swapna Dept. of Computer Science and Engg. VIIT Visakhapatnam India Defense R&D Organization DRDO New Delhi India Dept. of Computer Science and Systems Engg. Andhra University Visakhapatnam India Dept. of Computer Science and Engg. RIET Rajahmundry India

ISBN: (纸本)9781467391986

Epiretinal prosthesis is a biomedical implant technology currently being developed around the world in hopes of restoring useful vision for patients suffering from retinal degenerative diseases such as Age-related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP). Epiretinal Prosthesis System (EPS) consists of four parts that includes Body Worn image Processor (BWIP), Retinal Implantable Receiver Stimulator (RIRS), wireless telemetry and electrode array. images are captured by BWIP's camera and encoded into a 1 Mbps bit stream by BWIP. The bit stream is modulated on 8.195 MHz carrier and transmitted serially to RIRS through wireless telemetry. RIRS converts the bit stream into electrical pulses and drives 1024 electrode array to stimulate the survival parts of retina electrically with visual cues for understanding the video picture/image. This paper presents the design of flexible high resolution 1024- electrode embedded computer based EPS using embedded computer based efficient control algorithms for better visual prediction. Finally, the integrity of the EPS has been verified visually through video display on LCD/LED array. The experimental results of EPS are enumerated for the object of human face using 1024-electrode/pixel LED array.

关键词： Prosthetics Electrodes Retina Wireless communication Streaming media Telemetry Radio frequency

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An image processing Method Reducing Road Marking Line Edge Features for Patrol Robots Identifying Road Boundaries 19

An Image Processing Method Reducing Road Marking Line Edge F...

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Proceedings of the 2019 3rd international conference on Advances in image processing

作者： Shuangye Chen Zhi Liu Beijing University of Technology Beijing China

ISBN: (纸本)9781450376754

When automatic patrol robots trying to identify road boundaries through edge detection, road marking lines on the road may cause errors. In this regard, this paper proposes a method to process images base on edge detection results, and builds a system based on the method for experiments. The experimental results show that the proposed method can effectively reduce the edge features of road markings lines while preserving the edge features of real boundaries, so that robots can identify real road boundaries.

关键词： image processing computer vision Hough transform Robotics Road recognition Edge detection

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Modified Level-Set for Segmenting Breast Tumor from Thermal images

Modified Level-Set for Segmenting Breast Tumor from Thermal ...

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international conference for Convergence of Technology (I2CT)

作者： Swathi Gopakumar K Sruthi Shivasubramani Krishnamoorthy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amritapuri Amrita Vishwa Vidyapeetham India Dept of Computer Science and Engineering Amrita School of Engineering Amritapuri Amrita Vishwa Vidyapeetham India

Contactless, painless and radiation-free thermal imaging technique is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer aided diagnosis of breast cancer. This work uses a modified version of level-set called marker-controlled level-set for segmentation along with pre-processing. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: a) marker-controlled Level-set segmentation of anisotropic diffusion filtered preprocessed image versus b) Segmentation using marker-controlled level-set on a Gaussian-filtered image. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer aided diagnosis of breast cancer.

关键词： image segmentation Filtering Breast cancer Level set image edge detection

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image processing Based Analysis of Cracks on Vertical Walls

Image Processing Based Analysis of Cracks on Vertical Walls

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international conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)

作者： Jayneel Vora Maitree Patel Sudeep Tanwar Sudhanshu Tyagi Department of Computer Engineering Institute of Technology Nirma University Ahmedabad Gujarat India ThaparInstitute of Engineering & Technology Deemed University Patiala (Pb.) India

ISBN: (纸本)9781538638330

Building defects are a regular occurring in constructions all over. There is a need to identify the type of defect present and accordingly take the necessary steps for correcting the defect. This paper focuses on the various construction defects that take place on different types of walling systems for buildings. A major problem is the lack of knowledge regarding the construction concepts in the residents of the building. The study undertook tackles this problem using image processing and identifying various defects in an android app. This study is conducted on the walls of K-Block, Nirma University. The defects are observed and monitored through this study. The collected images are analysed and proper intimations can be pursued. Based on the recordings, various types of building defects were identified and proper gradations were given. Work is under process to monitor a house in the Virtual World, and create a visualiser of degradation of the house over the years.

关键词： Estimation image edge detection Skeleton Buildings Gray-scale Degradation

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Animal Species image Classification

Animal Species Image Classification

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international Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Likhith Prudhivi M Narayana. Ch Subrahmanyam. M Gopi Krishna. Salim Chavan Infosys Ltd Hyderabad India Electronics & Communication Engineering Anurag University Hyderabad India Electronics & Communication Engineering Vignan institute of technology and science. telangana India Electronics & Communication Engineering Vardhaman College of Engineering Shamshabad India Govindrai Wanjari College of Engineering and Technology Nagpur India

In forests, in order to protect wild animals, real-time animal species identification is done via picture recognition. Many computer vision approaches were first presented in the past, however they couldn’t meet the demands since accuracy decreased as technology evolved. Nonetheless, accuracy in the categorization, identification, and segmentation of pictures was significantly improved as a consequence of the inclusion of multiple approaches. In order to achieve a high degree of accuracy, this study intends to provide an effective approach for animal species picture categorization. Convolutional neural networks have been developed specifically for the categorization of images. To achieve high accuracy, final layer features are learned and synchronised to the pre-trained model. With the dataset, a variety of architectures for deep learning are evaluated.

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Region Proposal Approach for Human Detection on Aerial imagery

Region Proposal Approach for Human Detection on Aerial Image...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Željko Marušić Dunja Božić-Štulić Sven Gotovac Tonćo Marušić University of Mostar Mostar BIH University of Split Split Croatia

In this paper we evaluated region proposal based CNN approach in human body detection from aerial perspective. Particular emphasis is on the automation of the detection for supporting search and rescue missions. This challenging task is characterized by two important requirements. The first requirement for proposed algorithm is real-time speed of execution and the other is exceptional detection quality on complex natural environment images. Evaluation is performed on high spatial resolution images with high level of details that were collected by UAV platforms. Evaluated method based on FasterRCNN detection model showed promising preliminary results, as well as fast processing of high resolution images. Overall, detection model achieved 88.3% recall with precision of 67.3%.

关键词： image segmentation Training Proposals Feature extraction Task analysis Unmanned aerial vehicles image resolution

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image-based Classification of Skin Cancer using Convolution Neural Network

Image-based Classification of Skin Cancer using Convolution ...

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Intelligent Technologies (CONIT), international conference on

作者： Priotosh Mondal Aditi Bhatia Roshini Panjwani Shrey Panchamia Indu Dokare Department of Computer Engineering VES Institute of Technology Mumbai India

Skin cancer is a category or collection of cancer affecting the tissues and layers of skin. Skin cancer is classified into several types depending on the type of cell it affects. These types include melanoma, melanocytic nevus, basal cell carcinoma, benign keratosis, actinic keratosis, dermatofibroma, vascular lesion, and squamous cell carcinoma. Melanoma which affects the melanocytes and is considered to be the most fatal and deadly cancer, is growing at an alarming rate, especially in the western hemisphere and the Pacific region. The proposed system contained a web-based application where the image of an affected skin area can be uploaded and the likelihood of skin cancer is displayed. This system has used a convoluted neural network (CNN) based binary and multi-classification model making efficient use of image processing, computer vision, OpenCV, and Python to classify dermatoscopic lesion images into cancerous and non-cancerous along with their types. The implemented binary classifier achieves an accuracy of 92%. Further, the multi-class classification model is implemented based on CNN to classify dermatoscopic cancerous lesion images into nine types which achieved an accuracy of 97%. Among nine classes one of the classes is non-cancerous. The models aim to provide a means of diagnostic tool that will help in the preliminary diagnosis of skin lesions. Early detection and diagnosis are appropriate measures to combat the spread and lethality of skin cancer.

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