Lane Mark Identifying and Tracking Base on Edge Enhancement

In order to improve the accuracy of lane mark line identifying and tracking, this paper uses the LOG operator for edge enhancement, so the useful information is changed into straight lines and the useful feature is obvious. And the paper uses the algorithm of 2-D gray histogram to segment the image. Then it uses Hough transformation to identify the lane mark’s two edges and account its intercept and slope, then draws the midline as the final identifying result. Finally in order to reduce the count time, the paper uses the identifying results of the last frame image limit the current frame image recognition areas in tracking lane mark. The experiments results show that the lane mark can be tracking dependably and the algorithms are real-time, moreover, when the algorithm is failure, the system can also recover in time, and locks the tracking target accurately again.

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219086 ◽

2021 ◽

pp. 1-10

Author(s):

Lipeng Si ◽

Baolong Liu ◽

Yanfang Fu

Keyword(s):

Neural Network ◽

Real Time ◽

Image Recognition ◽

Error Detection ◽

Convolution Neural Network ◽

Data Sets ◽

Small Target ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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Image Completion Using Randomized Correspondence

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.271-273.229 ◽

2011 ◽

Vol 271-273 ◽

pp. 229-234

Author(s):

Yun Ling ◽

Hai Tao Sun ◽

Jian Wei Han ◽

Xun Wang

Keyword(s):

Real Time ◽

Random Sampling ◽

Nearest Neighbor ◽

Structural Constraint ◽

Image Completion ◽

Image Patches ◽

Real Time Applications ◽

Surrounding Areas ◽

Straight Lines ◽

Better Than

Image completion techniques can be used to repair unknown image regions. However, existing techniques are too slow for real-time applications. In this paper, an image completion technique based on randomized correspondence is presented to accelerate the completing process. Some good patch matches are found via random sampling and propagated to surrounding areas. Approximate nearest neighbor matches between image patches can be found in real-time. For images with strong structure, straight lines or curves across unknown regions can be manually specified to preserve the important structures. In such case, search is only performed on specified lines or curves. Finally, the remaining unknown regions can be filled using randomized correspondence with structural constraint. The experiments show that the quality and speed of presented technique are much better than that of existing methods.

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Real-Time Image Recognition with the Parallel Directed Enumeration Method

Lecture Notes in Computer Science - Computer Vision Systems ◽

10.1007/978-3-642-39402-7_13 ◽

2013 ◽

pp. 123-132 ◽

Cited By ~ 3

Author(s):

Andrey V. Savchenko

Keyword(s):

Real Time ◽

Image Recognition ◽

Real Time Image ◽

Time Image

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Quantitative evaluation of image recognition performance of fiducial markers in real-time tumor-tracking radiation therapy

Physica Medica ◽

10.1016/j.ejmp.2019.08.004 ◽

2019 ◽

Vol 65 ◽

pp. 33-39 ◽

Cited By ~ 1

Author(s):

Naoki Miyamoto ◽

Kenichiro Maeda ◽

Daisuke Abo ◽

Ryo Morita ◽

Seishin Takao ◽

...

Keyword(s):

Radiation Therapy ◽

Real Time ◽

Quantitative Evaluation ◽

Image Recognition ◽

Recognition Performance ◽

Fiducial Markers ◽

Tumor Tracking

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The Correction Algorithm Research of Inclination Paper Currency Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.1464 ◽

2013 ◽

Vol 756-759 ◽

pp. 1464-1468

Author(s):

Hong Hai Liu ◽

Xiang Hua Hou

Keyword(s):

Image Recognition ◽

Inclination Angle ◽

Experimental Results ◽

Correction Algorithm ◽

Recognition Algorithms ◽

Paper Currency ◽

Rotation Transformation ◽

Straight Lines

There exist low recognition speed and non-ideal recognition effect in some recognition algorithms of paper currency value. One of very important reasons is that the shooting angle makes the image inclined. This paper firstly analyses the binarization processing of RMB 100-Yuan image and then the method of acquiring straight lines in image is discussed. Thus, the inclination angle of image is calculated by using the obtained straight lines. Finally, through rotation transformation, the inclination image is corrected. The experimental results show that this algorithm has a good corrected effect to the inclination image of paper currency and improves the image recognition effect.

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