Cultural inheritance, activation and utilization are the value orientation of the living protection of couplet landscape. In the reconstruction of traditional village couplet landscape, taking the repair of village cu...
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Cultural inheritance, activation and utilization are the value orientation of the living protection of couplet landscape. In the reconstruction of traditional village couplet landscape, taking the repair of village cultural landscape gene as the starting point. The image recognition algorithm is used to comprehensively and systematically extract various information that can reflect the cultural connotation of the village in the original couplet landscape, find the appropriate couplet text from the database, match the calligraphy text and plaque style with high aesthetic feeling, and the optimal couplet landscape reconstruction scheme meeting the real needs is formed based on the overall perception of local residents on landscape gene repair, it is an effective strategy to realize the benign and coordinated development of traditional village cultural ecological activation and beautiful village construction.
In this paper, a new deep learning-based imagerecognition method is proposed to address the problems of existing imagerecognition methods. Through the depth neural network, the Generative adversarial network model i...
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With the extensive implementation of smart surveillance systems in the security domain, enhancing imagerecognition accuracy and efficiency has emerged as a crucial challenge. In this research, an image identification...
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imagerecognition technology has been widely used in various fields. The research on imagerecognition has been carried out for many years. In the existing algorithms, there are mainly two categories. One is the tradi...
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imagerecognition technology has been widely used in various fields. The research on imagerecognition has been carried out for many years. In the existing algorithms, there are mainly two categories. One is the traditional image recognition algorithm based on image processing, and the other is the new image recognition algorithm based on artificial intelligence. Traditional image recognition algorithms take a long time, cannot achieve real-time processing, and have low accuracy. Compared with the former algorithm, the image recognition algorithm based on artificial intelligence is simpler and faster. It can learn more advanced image features, thereby improving the accuracy of imagerecognition, so it is widely used. This paper focuses on image recognition algorithms based on convolutional neural networks, and proposes image recognition algorithms based on artificial neural networks with low complexity, high recognition accuracy, high efficiency, and fast real-time. Various algorithms are analyzed, and the application characteristics of each type of algorithm are summarized.
image semantics recognition is a long-standing research topic and has been used to many application areas, including medical diagnose, public security, etc. However, how to teach a social robot to have the intelligenc...
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ISBN:
(纸本)9781467390996
image semantics recognition is a long-standing research topic and has been used to many application areas, including medical diagnose, public security, etc. However, how to teach a social robot to have the intelligence to recognize images through user interactions still remains open and ambitious. In this paper we propose a novel framework of semi-supervised human-robot interactive imagerecognition. In our framework, the user first presents unlabeled images to a humanoid robot for recognition;then the robot answers the user what the image is based on a semi-supervised learning algorithm;thirdly if the robot's answer is wrong, the user correct the robot with the right label. With the learning process going on, the robot is trained to recognize more and more images with different semantic labels. The ability of "learning image semantics" makes the user feel that the robot is more like an "intelligent life". Extensive experiments and comparisons have proved the efficiency of our framework with encouraging results.
In recent years,deep learning has become more and more widely used in the fields of speech recognition,computer vision and *** in the field of imagerecognition,deep learning-based convolutional neural networks show a...
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In recent years,deep learning has become more and more widely used in the fields of speech recognition,computer vision and *** in the field of imagerecognition,deep learning-based convolutional neural networks show a strong technical superiority that surpasses previous machine vision *** traditional methods of machine vision have not been able to meet the practicality and security requirements of imagerecognition in the big data *** this stage,deep learning has become a hotspot in imagerecognition *** this end,the paper elaborates the background and basic ideas of deep learning,and analyzes common models of deep learning:deep belief networks,algorithm principles of convolutional neural networks,and imagerecognition efficiency.
With the development of international maritime trade, the actual number of surveillance cameras in smart ports is increasing. In response to the widely valued early warning detection and prevention system, the securit...
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Optofluidic time-stretch imaging system has enabled high-throughput phenotyping of cells with unprecedented high speed and resolution. However, significant amount of raw image data is produced, which requires recognit...
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ISBN:
(数字)9781510631021
ISBN:
(纸本)9781510631021
Optofluidic time-stretch imaging system has enabled high-throughput phenotyping of cells with unprecedented high speed and resolution. However, significant amount of raw image data is produced, which requires recognitionalgorithm with not only high accuracy but also high speed to analyze image data efficiently. In this paper, we compare the performance of popular feature extraction methods and learning-based classification algorithms on time-stretch microscopy imagerecognition. The applied imagerecognition system comprises an outlier detection step, feature extraction method and classification. The main concept of outlier detection uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to eliminate error images. Gabor wavelet, HOG (Histograms of Oriented Gradients), LBP (Local Binary Pattern) and PCA (Principal Components Analysis) are applied and compared as the feature extraction methods. Finally, with a set of extracted features, the computing time and accuracy of SVM (Support Vector Machines), LR (Logistics Regression), ResNet (Residual Neural Network) and XGBoost (Extreme Gradient Boosting) classification algorithms are evaluated. The tested cell image datasets are acquired from high-throughput imaging of numerous drug-treated and untreated cells (N = similar to 21,000) with an optofluidic time-stretch microscope. Results show that PCA feature extraction and XGBoost classification proves to be the fastest algorithms with the highest level of accuracy. DBSCAN outlier detection helps to improve the recognition accuracy by 2% approximately. Therefore, we propose a recognitionalgorithm consisting of DBSCAN outlier detection, PCA feature extraction and XGBoost classification as a promising solution to process the image data of high-throughput optofluidic time-stretch microscopy accurately and rapidly.
With the improvement of people's requirements for the accuracy of disease detection, increasing researchers apply imagerecognition to the detection and diagnosis of gastrointestinal diseases. In addition, the eva...
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With the improvement of people's requirements for the accuracy of disease detection, increasing researchers apply imagerecognition to the detection and diagnosis of gastrointestinal diseases. In addition, the evaluation of detection and diagnostic accuracy is also an important factor in the diagnosis and treatment of gastrointestinal diseases. Referring to the QoS and QoE evaluation scheme in 5G network communication, this paper proposes a secondary quality experience evaluation model for gastrointestinal diseases. Firstly, three mandatory detection implementation nodes of 5G network communication are created in the imagerecognition model, and the first node detects the detected points (images of intestinal diseases) in 5G network communication in the form of interactive information. Then, take the above detection results as the first level to obtain the quality detection strategy results, and the second node uses the policy detection server to evaluate the measurement results as the results of the second level quality evaluation strategy. Finally, the third node is used to evaluate the results of the secondary quality evaluation, and the evaluation results of gastrointestinal disease detection are returned to the output of the whole model. In this paper, 200 CT images of the gastrointestinal tract in the CT colonography dataset were used to evaluate the performance of the model. The study shows that after 200 gastrointestinal CT images are imported into the model, 173 samples have a score of more than 85% for the output of other nodes at the end of the enforcement node, accounting for 86.5% of the sample size. After 24 samples were evaluated by the second level, the output result was the first level scoring error, accounting for 12% of the sample size. Three samples were judged as abnormal images by the first level enforcement node, accounting for 1.5% of the sample size. From the results, the secondary quality experience evaluation model can improve the image re
An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A highspeed cell rec...
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An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A highspeed cell recognitionalgorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognitionalgorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
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