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Accurate Registration of Remote Sensing Images Based on Local Optimal Transformation

Journal of Beijing Institute of Technology 2019年第2期28卷 371-382页

作者： Bo Wang Changqing Li Shi Tang Zhiqiang Zhou Hong Zhao School of Automation Beijing Institute of TechnologyBeijing 100081China Beijing Urban Construction Design&Development Group Co. Ltd.Beijing 100071China Beijing Navigation Control Technology Co. Ltd.Beijing 102200China

As the basic work of image stitching and object recognition,image registration played an important part in the image processing *** previous work in registration accuracy and realtime performance progressed very slowly,especially in registrating images with line *** innovative method for image registration based on lines is proposed,it can effectively improve the accuracy and real-time performance of image *** line feature can deal with some registration problems where point feature does not *** registration process is divided into two *** first part determines the rough registration transformation relation between reference image and test *** the similarity degree among different transformation and modified nonmaximum suppression(MNMS)algorithms are obtained,which produce local optimal solution to optimize the rough registration *** final optimal registration relation can be obtained from two registration parts according to the match *** experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.

关键词： initial registration relationship accurate registration relationship similarity degree local optimal transformation modified non-maximum suppression(MNMS)algorithm

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Restricted Hysteresis Reduce Redundancy in Edge Detection

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Journal of Signal and Information Processing 2013年第3期4卷 158-163页

作者： Bo Li Ulrik Soderstrom Shafiq Ur Réhman Haibo Li Department of Applied Physics and Electronics Umea UniversityUmeaSweden Department of Media Technology and Interac-tion Design KTH Royal Institute of TechnologyStockholmSweden

In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully.

关键词： Edge Detection Hysteresis non-maximum Suppression Redundancy

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Research on Pedestrian Detection Technology Based on MSR and Faster R-CNN

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Journal of Computer and Communications 2018年第7期6卷 54-63页

作者： Xueyun Zhao Chaoju Hu College of Control and Computer Engineering North China Electric Power University Baoding China

In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.

关键词： Deep Learning Pedestrian Detection Region-Based Convolutional Neural Network Image Enhancement non-maximum Suppression

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