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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1803 条记录，以下是511-520 订阅

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Simulation of computer image processing Model Based on Random Forest Algorithm

Simulation of Computer Image Processing Model Based on Rando...

引用

3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), international conference on

作者： Haotian Liu University of Edinburgh Edinburgh UK

In essence, image recognition, as a pattern classification problem, can also be processed by classical pattern classification algorithms. Under the background of network information technology, it is need to use computer vision algorithm to process the image and realize the three-dimensional reconstruction of the image for the distortion of the true three-dimensional display image of intelligent interactive system. Random Forest (RF) is a new ensemble learning algorithm developed in recent years, which has good classification accuracy. In this article, a computer image processing model based on RF algorithm is proposed to enhance the ability of depth feature learning. The experimental results show that this image processing technology is more accurate than BP neural network and can be widely used in image processing. High-level target feature representation can often improve the recognition accuracy. However, the extraction of high-level target features generally has certain computational complexity. Compared with traditional methods, the accuracy of the proposed method is greatly improved, and the generalization performance is superior, which can identify images that have been beautified and repaired.

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Chicken Quality Evaluation Using Deep Learning 2nd

Chicken Quality Evaluation Using Deep Learning

引用

2nd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Madan, Rishi Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Toe, Teoh Teik Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Singapore Singapore

ISBN: (纸本)9789819919451

In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep Neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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Comparative Analysis of CNN Methodologies Used for Classification of Diabetic Retinopathy 2nd

Comparative Analysis of CNN Methodologies Used for Classific...

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2nd international conference on computer vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021

作者： Duth, P. Sudharshan Souza, Elton Grivith D. Department of Computer Science Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (纸本)9789811698842

Diabetic retinopathy is currently one of the most common conditions that affects the eyes. It occurs when the blood vessels present on the light-sensitive tissue is damaged which results in blindness or vision loss. It is quite challenging to identify its symptoms in its early stages and can thus lead to delay in treatments. Hence, image processing was introduced to solve this classification problem. The problem domain mainly focussed its approach in classification speed and reliability. Thus, many classification methodologies were used to compute the symptoms according to varying levels of infection. In this work, we have acquired a fundus image data set categorised into five levels of infection and used it to compare different commonly used CNN methodologies to observe their behaviour in varying simulations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Eye protection

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Design of a Lightweight Convolutional Neural Network Accelerated by FPGA

Design of a Lightweight Convolutional Neural Network Acceler...

引用

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

作者： Yingjun Xu Kun Wang Shanxi University College of Physics and Electronic Engineering Taiyuan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

image classification has widespread applications in various fields such as medical diagnosis, autonomous driving, security surveillance, and manufacturing. The existing architecture has a complex computation workload, leading to increased latency. This paper designs and implements a convolutional neural network architecture accelerated by FPGA, quantizing the parameters to int14 fixed-point integers, achieving an effective recognition rate of 98.2 % for handwritten digits. By reducing the number of parameters and arithmetic operations, the difficulty of deploying this CNN architecture on hardware devices is lowered, it also reduces the usage of storage resources, the combination of parallel computation of convolutions with pipelining operations improves the real-time performance of the overall architecture. Utilizing the resources on the ZYNQ-7020 FPGA development board to build the CNN architecture, achieving a prediction time of 86.02 μs for a single image under a clock frequency of 50 MHz, compared to existing architectures, it has significant advantages in terms of real-time performance.

关键词： Handwriting recognition Time-frequency analysis image coding Surveillance computer architecture Real-time systems Convolutional neural networks Security Field programmable gate arrays Pipeline processing

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Research on Automatic Classification and Recognition System of Works of Art Based on computer vision

Research on Automatic Classification and Recognition System ...

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Electrical, Automation and computer Engineering (ICEACE), international conference on

作者： Gao Xun Shandong Light Industry Vocational College Zibo China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

In this study, an automatic classification and recognition system based on computer vision is constructed based on ancient ceramic art works. The system uses advanced visual feature extraction technology, which can accurately extract the unique visual features of ancient ceramic works from images, including but not limited to enamel color, decoration, type, etc. At the heart of the research lies the development of innovative feature extraction algorithms that combine deep learning and traditional image processing techniques to effectively separate the subject of an artwork from a complex background and accurately identify subtle visual differences. Through training and testing on many ancient ceramic image data, the algorithm shows excellent classification performance and recognition accuracy. In the system simulation stage, a series of standardized test sets are used to evaluate the classification and recognition ability of the system. The simulation results show that the system can maintain a high level of recognition accuracy even in the face of diverse and complicated ancient ceramic works, which proves its reliability and effectiveness in practical applications.

关键词： Visualization computer vision Art Accuracy image color analysis Face recognition Feature extraction Ceramics Classification algorithms computer security

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Remote sensing image matching method based on neural network 2

Remote sensing image matching method based on neural network

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2nd international conference on computer vision, image, and Deep Learning

作者： Qiao, Weihua Shandong University of Science and Technology 579 qianwangang Road Shandong Qingdao China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The traditional remote sensing image matching method based on registration algorithm has problems in the smoothness of the matching results in practical application. Therefore, a remote sensing image matching method based on neural network is proposed. Based on neural network to extract remote sensing image control points, visual processing of remote sensing image feature points, matching remote sensing image control points, a new matching method is proposed. The experimental results show that the new matching method can effectively improve the anti-interference ability, reduce the noise in the matching behavior and improve the smoothness of the matching results. © 2021 SPIE.

关键词： image matching

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Research on Interior Design Platform Based on 3D Virtual vision

Research on Interior Design Platform Based on 3D Virtual Vis...

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Sensors, Electronics and computer Engineering (ICSECE), IEEE international conference on

作者： Qun Wang Changchun University of Architecture and Civil Engineering Changchun Jilin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

The current interior design platform can create three-dimensional drawings of interior items based on their shapes. Based on recent research, it creates realistic, three-dimensional pictures by placing things inside spaces and develops an interior design platform using a three-dimensional virtual vision. We finished the interior positioning algorithm and demand analysis for interior design, as well as analyzed the structure of the platform through a three-dimensional viewpoint. The 3D virtual vision-based interior design platform is enhanced with interactive features, anchor points, and drivers to construct its environment. Our simulation experiment shows that this platform can accurately reflect actual needs in both two-and-three-dimensional designs with more precise placement of objects within interiors.

关键词： Industries image sensors Economics Three-dimensional displays Shape Military computing image processing Virtual reality Sensor phenomena and characterization Engineering education

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image Splicing Detection Based on Hybrid CNN-Transformer Network

Image Splicing Detection Based on Hybrid CNN-Transformer Net...

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Machine vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Ningjing Jiang Dengyong Zhang Feng Li School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

image splicing is widely recognized as the predominant technique in image manipulation. Most current splicing detection methods have low accuracy and poor localization of small-sized splicing areas. Therefore, we design a hybrid CNN-Transformer network for image splicing detection. Firstly, a hybrid structure encoder is constructed, which first uses CNN to extract the input images' local features, and then inputs it to Transformer to calculate long-distance dependencies to obtain global feature representation. Then, a deformable attention enhancement module (DAEM) is designed to make the CNN local features at different scales at each stage more refined and improve the deformation adaptability of the network. Finally, features from the hybrid structured encoder are added to the decoder via skip connections, to make the decoder perform better feature reconstruction. Results on three datasets show that the network can more accurately locate tampered areas and has superior performance than other methods.

关键词： Location awareness image recognition Deformation Convolution Splicing Imaging Feature extraction Transformers Decoding image reconstruction

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Low Resource Degraded Quality Document image Binarization - Domain Adaptation is the Way 13

Low Resource Degraded Quality Document Image Binarization - ...

引用

13th Indian conference on computer vision, Graphics, and image processing, ICVGIP 2022

作者： Kundu, Ahana Bhattacharya, Ujjwal Computer Vision and Pattern Recognition Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9781450398237

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as 'ISIDDI2' and it consists of 139 images of Bangla old document pages. Samples of 'ISIDDI', another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both 'ISIDDI2' and 'ISIDDI' are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of 'ISIDDI2' or 'ISIDDI' samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory. © 2022 ACM.

关键词： Network architecture

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FPGA-Based Multi-Channel image Acquisition and processing System

FPGA-Based Multi-Channel Image Acquisition and Processing Sy...

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

作者： Xiaohui Chu Qiang Jiang Zhichao Zhang Tong Chen Xin Wang Yi Yuan School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The deep integration of new-generation information technology and manufacturing is triggering far-reaching industrial changes. Machine vision inspection is widely used in large-scale repetitive industrial production processes. In order to solve the problem of the limited field of view of the machine vision inspection system and the obstruction of the camera view during the inspection process, this article takes the glue dispenser vision inspection system as the project background, and designs a high-performance and low-cost FPGA-based multi-channel image acquisition and transmission system scheme. The system can realize parallel acquisition, decoding, storage, display, and transmission of data from 4 image sensors. The OV5640 is used as the video sensor for video capture, and video data is cached using DDR3 SDRAM and transmitted to the host computer through Gigabit Ethernet communication technology. This thesis utilizes the advantages of FPGA in the field of image processing to perform real-time pipeline operations on video data, solving the limitation of video capture by a single camera and improving the system's real-time performance. Implementing multi-channel image grayscale conversion, filtering, edge detection, and stitching fusion in FPGA.

关键词： image sensors Machine vision image edge detection Inspection Streaming media Cameras Real-time systems SDRAM Field programmable gate arrays Pipeline processing

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