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Research on the Around View image System of Cable Winding Vehicle

Research on the Around View Image System of Cable Winding Ve...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Wei Dai Wenxin Ji Liping Han School of Electronics and Information Engineering Liaoning Technical University Huludao China Engineering Department Jixi Holy-Glory New Energy Technology Inc Jixi China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

With the rapid development of China's economy, the demand for construction vehicles is increasing. The operating environment of open-pit mines is complex, and the cable reel truck is so large that it is difficult to ensure safety only by relying on the rearview mirror. A panoramic surround-view image system can provide real-time overlooking images around the cable reeler body, effectively eliminating the blind spot in the field of vision, and providing very effective assistance to the remote operator. This paper analyzes the background and key technologies of the panoramic surround-view image system and gives a specific implementation plan according to the actual characteristics of the cable reeling truck. The system uses six fisheye cameras to capture images around the cable reeling truck because the captured images have fisheye distortion, so the images are firstly distortion-corrected, and then the corrected images are converted to top view by perspective transformation, and finally, the six images are spliced and fused. The experiment proves that the fusion algorithm correction of gradual-in and gradual-out can meet the system requirements. It effectively reduces the algorithm's complexity and has good application prospects and value.

关键词： Windings Focusing computer applications Feature extraction Distortion Cameras Real-time systems Safety Complexity theory Mirrors

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Enhanced Iris Recognition using discrete cosine transform and radon transform

Enhanced Iris Recognition using discrete cosine transform an...

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international conference on Electronics and Communication Systems (ICECS)

作者： I R Mrinalini B P Pratusha K Manikantan S Ramachandran Dept. of Electronics and Communication Engg. M.S. Ramaiah Inst. of Tech. Bangalore INDIA

Iris Recognition (IR) across illumination variations is a problem of fundamental importance in computer vision. In this paper, we propose two novel techniques, viz., Triangular DCT based Feature Extraction (T-DCT) and Radon Transform based Pre-processing, to improve the performance of an IR system. T-DCT is used for efficient feature extraction and Radon Transform is used for curve detection in iris images. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for optimal feature subset. Individual stages of the IR system are examined and an attempt is made to improve each stage. Experimental results obtained by applying the proposed algorithm on three benchmark iris databases, namely Phoenix, MMU and IITD dataset, show that the proposed system out performs other IR systems. A significant increase in the recognition rate and a substantial reduction in the number of selected features are observed.

关键词： Discrete cosine transforms Databases Feature extraction Iris recognition Histograms image edge detection

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained image Classification

Abnormal Clustering and Cross Slicing Transformer for Insect...

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

作者： Aokun Mei Hua Huo Big Data Analysis Laboratory of Henan Medical Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images.

关键词： Training image segmentation computer vision Visualization Insects image edge detection Morphology Transformers Feature extraction image classification

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image CLASSIFICATION And RECOGNITION BASED ON THE DEEP CONVOLUTIONAL NEURAL NETWORK

IMAGE CLASSIFICATION AND RECOGNITION BASED ON THE DEEP CONVO...

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2017 2nd Joint international Mechanical,Electronic and Information Technology conference(JIMET 2017)

作者： Yuan-yuan WANG Long-jun ZHANG Yang XIAO Jing XU You-jun ZHANG The College of Information and Electric Engineering Shenyang Agricultural University STATE GRID XINJIANG INFORMATION & TELECOMMUNICATION COMPANY

With the development of the information age,there were a lot of data whose features couldn't be extracted or predicted effectively in real *** of the core function of computer vision technology is to classify and recognize,with classification and recognition as its summit mission of object detection and object *** to image data were affected by multiple factors such as illumination,environment,angle,certain object features couldn’t be established by manual coding,and it is hard for high latitude data in a computer to realize real-time object detection,object localization,classification and *** the higher accuracy in classification could be obtained with the help of GPU of high performance and the large scale pretraining on the super database image Net,based on the most advanced deep convolution neural network *** complete optimization training was conducted on data sets Pascal vision Object Classes(VOC),and the real-time object detection,object localization,classification and recognition were realized by high performance GPU of NVIDIA.

关键词： Machine Learning computer vision Deep Learning Convolutional Neural Network

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FPGA-based Moving Object Detection Leveraging Frame Difference and Morphological Filtering

FPGA-based Moving Object Detection Leveraging Frame Differen...

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computer, vision and Intelligent Technology (ICCVIT), international conference on

作者： Yuexin Wang Xiaochen Jing Wenhao Wang Mengli Zhu Dongchen Zhu Bionic Vision System Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Moving object detection plays a significant role in video surveillance. However, existing moving object detection methods often rely on software implementations, which means low real-time performance and high power consumption. This paper's core detection algorithm employs a frame difference method that is enhanced by morphological filtering. Additionally, we propose an architecture that integrates FPGA(Field Programmable Gate Array) and ARM(Advanced RISC Machine), fully leveraging the parallel computing advantages of FPGA and the high processing efficiency of ARM. The system utilizes a ZYNQ7000 SoC, coupled with an OV7725 camera for image capture and DDR3 SDRAM for data caching, to address the challenges of high-speed data processing and low power consumption. Experimental results show that the system meets the requirements for high real-time performance and low power consumption with a frame rate of 85.9375 frames per second and a total power consumption of 1.101 W.

关键词： Power demand Reduced instruction set computing Program processors Filtering Object detection Parallel processing Video surveillance Real-time systems Software Field programmable gate arrays

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Deep learning model based breast cancer histopathological image classification

Deep learning model based breast cancer histopathological im...

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IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Benzheng Wei Zhongyi Han Xueying He Yilong Yin College of Science and Technology Shandong University of Traditional Chinese Medicine Jinan China School of Computer Science and Technology Shandong University Jinan China

ISBN: (纸本)9781509045006

The automatic and precision classification for breast cancer histopathological image has a great significance in clinical application. However, the existing analysis approaches are difficult to addressing the breast cancer classification problem because the feature subtle differences of inter-class histopathological image and the classification accuracy still hard to meet the clinical application. Recent advancements in data-driven sharing processing and multi-level hierarchical feature learning have made available considerable chance to dope out a solution to this problem. To address the challenging problem, we propose a novel breast cancer histopathological image classification method based on deep convolutional neural networks, named as BiCNN model, to address the two-class breast cancer classification on the pathological image. This deep learning model considers class and sub-class labels of breast cancer as prior knowledge, which can restrain the distance of features of different breast cancer pathological images. In addition, an advanced data augmented method is proposed to fit tolerance whole slide image recognition, which can full reserve image edge feature of cancerization region. The transfer learning and fine-tuning method are adopted as an optimal training strategy to improve breast cancer histopathological image classification accuracy. The experiment results show that the proposed method leads to a higher classification accuracy (up to 97%) and displays good robustness and generalization, which provides efficient tools for breast cancer clinical diagnosis.

关键词： Feature extraction Robustness Cancer image recognition image color analysis Neurons image reconstruction

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Cost Effective Real Time vision Interface for Off Line Simulation of Fanuc Robots

Cost Effective Real Time Vision Interface for Off Line Simul...

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international conference on Signal processing and Information Security (ICSPIS)

作者： Bittu Scaria Nazarudeen A. Aziz Alavikunhu Panthakkan College of Engineering Khalifa University Abu Dhabi UAE College of Engineering & IT University of Dubai Dubai UAE

ISBN: (数字)9781728138732

ISBN: (纸本)9781728138749

The proliferation of automation has created an increasing demand for robots in every area of the manufacturing industry. FANUC robots are very widely used in automated production lines because of its intelligent functions with flexible open architecture. It allows the users to plugin online additional components such as vision and sensor systems to support the development of robotic systems. FANUC also has an offline simulation tool called Roboguide, which allows the users to simulate a virtual robotic cell in 3D, eliminating the need for expensive physical prototype setup. This tool supports the virtual integration of an integral Robot vision (iRvision) capabilities. However, this feature is limited to virtual vision or using very expensive FANUC compatible cameras that allow the users to take a real mode image and simulate it in the virtual world. In this paper, we propose an offline plugin for interfacing normal cameras of any type including webcams to the FANUC iRvision system for the virtual simulation of real mode images. It was developed using an Open computer vision (OCV) plugin and tested it by physically capturing the real image of the object using a webcam interfaced with the iRvision system and processed it on the virtual robot controller. The test results show that this cost-effective plugin can be used as an OCV in industrial automation and robotic building system without any compromise in terms of performance and quality.

关键词： Tools Cameras Robot vision systems Service robots Calibration

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Design and implementation of fuzzy image enhancement system in police application

Design and implementation of fuzzy image enhancement system ...

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2018 2nd international conference on Electronic Information Technology and computer Engineering (EITCE 2018)

作者： Cheng LIU Fan-liang BU People's Public Security University of China School of Information Technology and Network Security

A set of image processing system based on fuzzy image enhancement algorithm is designed and implemented in the light of the actual needs of image material evidence extraction in the criminal investigation process of public security department, which is combined with the computer technology such as C++ language and graphic development tools. The system can enhance the processing efficiency by continuously selecting different algorithms for input images. The same type of fuzzy image can be processed by various algorithms and the ideal image can be obtained by comparing the image evaluation indexes, and the optimal selection of image quality and algorithm is realized.

关键词： Retinex Design and implementation of fuzzy image enhancement system in police application image

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Real-Time Violence Detection and Alert System using MobileNetV2 and Cloud Firestore

Real-Time Violence Detection and Alert System using MobileNe...

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Networking and Communications (ICNWC), international conference on

作者： Mannu Thomas P Balamurugan Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamil Nadu

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The aggressive action greatly jeopardizes individual safety and general well-being. Various alternative tactics have been used to reduce violent behavior, including the installation and maintenance of surveillance systems. It is crucial for monitoring systems to automatically detect aggressive behavior and promptly provide warning signals to the authorities. computer vision and image processing have emerged as crucial study areas for identifying deviant and violent behavior, drawing in new researchers. The paper introduces a real- time violence detection and alert system that identifies violence in video streams by utilizing MobileNetV2. The system provides a scalable, precise, and adaptable public safety solution by using Cloud Firestore for data storage and administration, and a Telegram Bot for instant notifications.

关键词： Technological innovation Surveillance Scalability Streaming media Chatbots Real-time systems Public security

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3-D construction of artery model using real time two dimensional CTA dataset

3-D construction of artery model using real time two dimensi...

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Vrushali Sudhir Taware Pranali C. Choudhari Department of Electronics and Telecommunication Fr. C. Rodrigues Institute of Technology Navi Mumbai India

The 3D models have become popular not only in the area of finite element modeling for physiological study but also in the 3D printing and device testing fields. There are several different ways to construct the three dimensional model of blood vessel as medical image processing and CAD (computer Aided Design) computing are the vast and equally interesting topics for medical as well as technical researchers. Variety of biomedical image modalities with their own significance are also contributing to widen its future scope. This paper presents an algorithm for three dimensional re-construction of blood vessels (arteries) using two dimensional angiography data. The proposed algorithm has been designed by reviewing previous work qualitatively and considering different ways for sub-stages in this process such as, segmentation and detection of vessel, centerline extraction etc. that give best results. Also, the algorithm have been implemented using MATLAB for analysis and tested with the real-time medical dataset. This re-constructed model will not only retain its physiology and anatomy but also will offer significant clinical benefits to vascular surgeons and patients as this 3D model can be attached with the patient's report.

关键词： Three-dimensional displays Biomedical imaging Arteries Solid modeling image edge detection Algorithm design and analysis

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