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

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Recent advancements in automated screening techniques for diabetic retinopathy

Recent advancements in automated screening techniques for di...

引用

Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： V.K.U. Ahamed Gani N. Shanmugasundaram N. Thiyagarajan Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Diabetic Retinopathy (DR) is a disease caused due to too high blood sugar level of diabetes that could result in permanent vision loss. It significantly affects the blood vessels of the retina. Early identification, analysis and usual screening of this disease are important for proper processing through computer-based intelligence techniques. The main aim of this paper is to assess the modern strategies for screening, analyzing and diagnosing DR, based on studies related to the detection and evaluation of DR. This paper included research gaps and tables used to summarize various imaging methods, datasets, performance matrices used in the analysis of DR. Now the future studies make a field of the combination of the dataset in addition to strategies to make the generalized approach for detecting the lesion in DR through an automated system. Moreover, numerous studies gaps have additionally been taken into consideration for in addition studies. This overview is useful to the researchers running inside the area of clinical imaging to display screen and diagnose diseases.

关键词： image segmentation Sensitivity Retinopathy Blood vessels Retina Diabetes Telecommunication computing

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A Crowdsourcing Repeated Annotations System for Visual Object Detection 2019

A Crowdsourcing Repeated Annotations System for Visual Objec...

引用

Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Yucheng Hu Zhonghong Ou Xiangyu Xu Meina Song School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450376259

As a fundamental task in compute vision, object detection has been developed rapidly driven by the deep learning. The lack of a large number of images with ground truth annotations has become a chief obstacle to object detection applications in many fields. Eliciting labels from crowds is a potential way to obtain large labeled data. Nonetheless, existing crowdsourced techniques, e.g., Amazon Mechanical Turk (MTurk), often fail to guarantee the quality of the annotations, which have a bad influence on the accuracy of the deep detector. A variety of methods have been developed for ground truth inference and learning from crowds. In this paper, we study strategies to crowd-source repeated labels in support for these methods. The core challenge of building such a system is to reduce the difficulty to annotate multiple objects of interest and improve the data quality as much as possible. We present a system that adopts the turn-based annotation mechanism and consists of three simple sub-tasks: a single object annotation, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate and can assist the detector of obtaining higher accuracy.

关键词： image annotation Object detection Large scale annotation Crowdsourcing Repeated labels

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image Background Removal using Android

Image Background Removal using Android

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： S Senthil Pandi A Naresh Kumar B Nitheesh Kumar Sathish Kumar Kannaiah Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE REC Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The image Background Elimination (IBE) application for Android exemplifies how the proliferation of mobile technology has revolutionized productivity and ease, particularly in graphic design, object recognition, and picture editing. Addressing the crucial need for seamless background removal in image processing and computer vision, this study conducts an extensive investigation and comparison of contemporary background removal algorithms, evaluating their benefits, drawbacks, and applicability across different scenarios. The IBE application leverages state-of-the-art algorithms to process images, offering user-friendly features that enable precise and visually appealing results. Users can capture new images using an integrated camera or upload existing ones directly from their device storage. The application employs a variety of background removal methods, including color-based thresholds, Grab-cut, and deep learningbased semantic segmentation, to accurately distinguish between foreground and background regions. Additionally, it provides interactive editing tools for manual segmentation refinement, granting users more control over the output. The main feature of the proposed application is its real-time processing capability, which provides users with a live preview of the background removal process. This study has proposed a responsive user interface for creating visually appealing images with removed backgrounds. Practical use cases demonstrate the application’s effectiveness, versatility, and potential in image processing and creative content production. This study offers a comprehensive analysis of the application’s capabilities in the mobile photography technology, enhanced by user feedback and data analysis.

关键词： Training Productivity Photography Visualization Operating systems Semantic segmentation Semantics

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Mammogram classification method based on GMM and GLCM-PSO-PNN

Mammogram classification method based on GMM and GLCM-PSO-PN...

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2018 3rd international conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2018)

作者： Xiaojian Zhang Chengjian Wei Xili Wan School of computer science and technology Nanjing Tech University

Facing the condition that the inefficient training of traditional classifiers in the classification process of mammography, a classification method is proposed combining image processing and supervised learning. Firstly, the improved adaptive median filter enhances the image contrast. Then, according to the result of breast segmentation based on Gauss Mixture Model(GMM), this paper proposed a classification model based on Probabilistic Neural Network optimized(PNN) optimized by Gray Level Co-occurrence Matrix(GLCM) and Particle Swarm Optimization(PSO). The eigenvector extracted from the GLCM can be used as input to simplify the network structure. The smoothing factor optimized by PSO used to train the network can improve accuracy. The results in public mammographic patches database demonstrate that the model can classify the types of mammography effectively and perform better than the previous methods.

关键词： mammogram gauss mixture model probabilistic neural network gray level co-occurrence matrix particle swarm optimization

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A Detection and Classification Method of Asphalt Pavement Crack based on vision Transformer 23

A Detection and Classification Method of Asphalt Pavement Cr...

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Proceedings of the 2023 3rd international conference on Big Data, Artificial Intelligence and Risk Management

作者： Yulong Yang Chen Guo Chen Zuo Bingxin Yang School of Information Engineering Chang? an University China College of Transportation Engineering Chang? an University China

ISBN: (纸本)9798400716478

Road surface distress detection is an important component of smart maintenance of transportation infrastructure, and crack damage is a common and far-reaching category of distress. Accurately and quickly detecting road cracks is of great significance for road prevention work. In recent years, the deep learning technology represented by convolutional neural networks has been widely used in the field of road damage detection. However, the convolutional neural network relies on a stack of convolutional layers to extract visual features from training images. Constrained by its ability to characterize long-distance correlations, it is challenging to apply convolutional neural network to tackle transverse cracks and longitudinal cracks in the real-world scenario. As an emerging deep learning architecture, Transformer model has received considerable attention in the fields of natural language processing and computer vision. In this paper, we employ the vision Transformer (ViT) to realize road damage classification. First, the histogram equalization technique is adopted to fulfill image preprocessing. The program enhances image contrast and eliminates the influence of illumination variation. Second, ViT, ResNet, DenseNet, and EfficientNet are separately implemented. We expand the training dataset with the data augmentation technique. Third, the computer evaluates the classification quality by means of accuracy, F1-score, and recall. A group of datasets of varying size are used to examine deep neural networks. The experiment result indicates that ViT outperforms CNN models in terms of classification quality. The long-range crack structures are reasonably identified by a fine-tune ViT model.

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An Anti-interfering Reconstruction Algorithm Based on Compressed Sensing

An Anti-interfering Reconstruction Algorithm Based on Compre...

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2013 3rd international conference on Electric and Electronics(EEIC 2013)

作者： Mei Du Huaici Zhao Chunyang Zhao Bo Li Shenyang Institute of automation Chinese Academy of Science University of Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision

When images compressed by traditional transformation-based compression algorithms are transmitted over wireless channels, if the gaussian random interference causes the loss of the crucial transformation coefficients, the contents of the reconstructed images will be lost obviously and this will reduce the accuracy of the subsequent detection and recognition results greatly. In order to solve this problem, this paper proposed an antiinterfering image reconstruction algorithm based on compressed sensing. This algorithm first confirmed the new compressed sensing signals and the new reconstruction matrix based on the locations of the compressed sensing signal components corresponding to the gaussian-interfered bit stream, and then reconstructed the original images employing the iterative threshold algorithm. The simulation results demonstrated that the new algorithm reconstructed exact images at low bit error rates, and reconstructed inexact images whose qualities were slightly lowered without loss of local contents at high bit error rates. As a result, our algorithm is able to overcome the deficiencies of compression algorithms based on diverse transformations and the iterative threshold algorithm, thus proposes a feasible solution scheme for the anti-interfering problem that arises in wireless image transmission.

关键词： anti-interference compressed sensing image reconstruction Gaussian random interference

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Multi-Feature Fusion Aerial image Segmentation in Complex Background 2019

Multi-Feature Fusion Aerial Image Segmentation in Complex Ba...

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Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Rui Yang Xiao jun Qian Bing bing Zhang Department of Computer Science and Technology Nanjing Normal University Nanjing China

ISBN: (纸本)9781450376259

The hybrid method of initial partitioning and region merging is widely used in aerial image segmentation. The existing initial partitioning methods are the watershed transform of the Edge Strength Map (ESM) of aerial images. Therefore, if watershed algorithm is used in images with discontinuous edges and lots of noise, it will be easy to produce "improper segmentation". In order to form high-quality initial partitions, we propose a new MRF (YMRF) image segmentation method from the perspective of fully exploiting the image spatial information. The key points of region merging are region similarity measurement, merging process and merging stopping moment, but the problem of region label selection is ignored after the region pair which will be merged is selected. So, we propose a kind of image scene to reflect the necessity of paying attention to this problem and develop a region merging label selection mechanism for the image scene. To solve the problem that merging stopping moment tends to form the result with high homogeneity in the domain, we propose a optimal merging state, which can weaken the homogeneity in the domain. Experimental results show that our algorithm is more effective than the existing methods, when they are used in our unique dataset.

关键词： Aerial image segmentation Merging process Optimal merging state ESM Region similarity measurement Region Merging MRF Watershed algorithm Region merging label selection mechanism

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Collaborative Text and image based Information Retrieval Model using BiLSTM and Residual Networks

Collaborative Text and Image based Information Retrieval Mod...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： P. Mahalakshmi N. Sabiyath Fatima B. S. Abdur Rahman Crescent Institute of Science & Technology Chennai

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Information retrieval (IR) becomes a crucial process in different applications like digital libraries, medicine, entertainment, and so on. Presently, the deep learning (DL) techniques are found to be applicable in several computer vision areas like object tracking, image classification, and natural language processing. So, the DL models are mainly applied in this work for the retrieval of text and images effectively. This paper develops a collaborative text and image based information retrieval system using DL models. The presented model involves two distinct processes namely text retrieval and image retrieval. Primarily, the convolutional neural network (CNN) based residual network 50 (ResNet) model called CNN-ResNet50 with Manhattan distance based similarity measurement technique is applied for the effectual retrieval of images. On the other side, bi-directional long short term memory (Bi-LSTM) technique optimized by differential evolution (DE) is applied for text retrieval. The proposed system highlights that the accuracy can be gained by increasing the depth( intermediate layers ) in DL model and providing an optimal result when using differential evolution algorithm with Bi-LSTM. A detailed implementation analysis was carried out to highlight the proficient results of the presented model. The outcomes indicated that the DE-BiLSTM technique has resulted in effectual outcome with the precision of 99.08%, recall of 99.24%, and F-score of 99.21%. Similarly, the CNN-ResNet50 model has also proficient retrieved the images with the average precision of 0.96 and an average recall of 0.91.

关键词： Feature extraction Databases image retrieval Kernel Computational modeling Training Residual neural networks

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Age and Gender Classification Using Convolution Neural Network

Age and Gender Classification Using Convolution Neural Netwo...

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Delhi Section conference (DELCON), IEEE

作者： Thonduru Anjum Javeriya Sumit Kumar Banshal Gnanaprakasam Thangavel Raka Moni Department of Computer Science and Engineering Alliance University Bangalore India Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This work aims to improve the accuracy of computer vision and deep learning methods for age and gender prediction in real time from individual photos or video frames. Automated techniques to recognize these characteristics have grown in significance with the emergence of social media platforms. Still, there are issues with the current approaches, especially with regard to extraneous elements that might skew forecasts, such as cosmetics, lighting, and facial expressions. Our work aims to overcome these difficulties by applying advanced algorithms that accurately assess an individual's gender and estimate their age within a given range. The goal of the project is to analyze visual input in real-time for image and video analysis by combining convolutional neural network (CNN) with open Cv. Because the model is taught to recognize and adjust to real-world situations, it is more resilient to change in environmental variables and picture quality. Our investigation shows that while determining an individual's precise age is still challenging, accuracy is much increased when estimating age ranges. Additionally, across a range of circumstances, the model produces accurate gender predictions. These results demonstrate how age and gender estimates may be enhanced by using deep learning and real-time processing techniques. Finally, this work shows that more robust age and gender prediction algorithms may be developed, which can find applications in social media and security, among other domains, by tackling the problems posed by the intrinsic heterogeneity of real-world images. Keywords-Automatic Age and Gender Identification, Real-time processing, Deep Learning, image and Video Analysis, Age Range Classification, Facial Feature Extraction.

关键词： Biometrics Deep learning Ethics Visualization Accuracy Social networking (online) Streaming media Real-time systems Convolutional neural networks Standards

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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach

Optimization of Aquaponics System Efficiency Based on Artifi...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Jayden Liu William Jiang The Harker School San Jose USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper aims to design and implement an ML-based approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Wireless communication Wireless sensor networks Accuracy Databases image processing Computational modeling Sensors

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