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Remote Multi-object detection based on bounding box field 11

Remote Multi-object detection based on bounding box field

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Liu, Jin Li, RongHao Gao, YongJian Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan Hubei Peoples R China

ISBN: (纸本)9781510636347

This paper proposes a new irregular remote sensing object detection algorithm that different from the ROI or rotating BOX obtained by traditional one. The architecture is designed to jointly learn four bounding box corner points and their association via two branches of the same sequential prediction process. The algorithm predicts four key points of the object and their associated connection, Bounding Box Fields(BBF) via convolutional neural network(CNN), and thus obtains the detail spatial distribution of the objects. In order to improve the positioning accuracy of the key points, network architecture reduced Receptive Field from large to small stage by stage. It has achieved ROI free finally. In this method, the object detection problem is framed as CNN convolution point detection and bounding box field detection, it achieved the one stage object detection with high precision and high speed. We verified the effectiveness and efficiency of the algorithm through experiments, which proved that the new data structure could locate the object attitude and spatial direction more accurately in real time with strong practicability.

关键词： Remote sensing Detection and tracking algorithms Neurons Data modeling Associative arrays Convolution image processing Network architectures Geodesy Lithium

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Marine biological monitoring and managing system based on Java technology 11

Marine biological monitoring and managing system based on Ja...

引用

SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Lei, Yu Cheng, Li Lei, Wei Hu, WenYa Wuhan Inst Technol Sch Elect & Informat Engn Hubei Engn Res Ctr Video Image & HD Project Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636422

In order to explore the rich Marine resources more efficiently and monitor the information of marine creatures in real time, this paper proposes to design a real-time Marine biological monitoring and managing system based on Java technology, which combines relevant Marine information collectors such as underwater camera. After the seabed image captured by the information collector is uploaded to the system server, the system can conduct relevant image processing to achieve a clearer image according to its fuzzy characteristics, and conduct unified classification, integration and storage to facilitate search later. Compared with the traditional method of image processing and image management segmentation, the system can save a lot of manpower and time.

关键词： java technology image processing marine resources motion blur image enhancement image pseudo-color

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Large-scale Book Page Retrieval by Deep Hashing Networks 11

Large-scale Book Page Retrieval by Deep Hashing Networks

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Huo, Jiao Zhao, Yi Liu, Leyuan Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Peoples R China Wuhan Zhimei Technol Co Ltd Wuhan Peoples R China

ISBN: (纸本)9781510636385

Nowadays, more and more printed books are accompanied by electronic resources including videos, audios, games, augmented reality and other mobile apps. However, it is not very convenient to access most of these electronic resources, as the association between printed books and electronic resources is not automatically available [2]. To build a bridge between a book page and the corresponding electronic resources, a large-scale book page retrieval method using deep hashing network is presented in this paper. There are mainly three contributions: First, a pipeline is proposed to make a Convolutional Neural Network (CNN) trained for another unrelated task available for book page retrieval. Second, the high-dimensional features extracted from the CNN is mapped to the low-dimensional binary hash code sequence in Hemming space by the deep hashing network, which not only increases the speed of retrieval but also saves the space of feature storage. third, a large-scale dataset which is consist of 1.55M book page images is collected. Experimental results on the 1.55M book page dataset show that the proposed deep hashing network achieves a Top-1 hit rate of 92.1% and the response time is less than 0.6 second on a desktop computer with a GeForce 1080Ti GPU.

关键词： image retrieval book page retrieval deep hashing network

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A small dim targets detection method with dark-spots interference based on infrared images 11

A small dim targets detection method with dark-spots interfe...

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Dong, Shuai Zhang, Tianxu Lv, Siman Wuhan Inst Technol Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9781510636361

When the small targets are dim and with dark-spots interference in infrared images, the targets can not be accurately detected by multi-level filter. In order to solve this problem, the dark-spots filtering algorithm based on contrast suppression is added to the original multi-level filter for detecting small dim targets. First, the result of difference image based on the low-pass filtered image and the original image preserves the information of the targets and dark-spots at the same time. Second, the difference image and the original image are superimposed to remove dark-spots, according to the inhibitory factor determined by the contrast between dark-spots and background. Finally, the targets are enhanced by using Gamma correction. The experimental results demonstrate that the algorithm enhances the targets, removes dark-spots, and suppresses the background of false detection, so the multi-level filter adding the proposed method can effectively detect small dim targets.

关键词： multi-level filter dark-spots small dim targets difference image contrast suppression

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An image Encryption Method Based on Logistic Chaotic Mapping and DNA Coding 11

An Image Encryption Method Based on Logistic Chaotic Mapping...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Lin, Jiayu Zhao, Kangnan Cai, Xin Li, Danning Wang, Zuxi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781510636422

This paper proposes a new image encryption algorithm, which is based on Logistic chaotic mapping and DNA coding. A variable parameter logistic mapping coupled chaotic system is proposed to generate pseudo-random sequences with good pseudo-random properties. Before encrypting plain-text images, the distinct features of some special images are eliminated by superposing them with a pseudo-random image. Combining with DNA encoding operation, an encrypting algorithm of cross-operation of diffusion and peunutation is designed. The simulation results show that the proposed algorithm has large key space, high sensitivity to key and plain-text, and good robustness to resist exhaustive attack, statistic attack, differential attack, etc. It shows that the proposed method has good encryption effect and security.

关键词： Chaos DNA code Pseudo random number image encryption

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Air target tracking method based on spectral unmixing 11

Air target tracking method based on spectral unmixing

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Lv, Siman Zhang, Tianxu Dong, Shuai Wuhan Inst Technol Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9781510636361

Because the spatial resolution of remote sensing equipment is low, how to accurately obtain the true spectrum of the target to guide the stable tracking of the target is a complex problem. This paper proposes an air moving target tracking method based on spectral unmixing. Firstly the target abundance is obtained through the pretreatment of infrared image, secondly, accurate spectrum of target is acquired based on the linear combination relationship between the background spectrum and the measured spectrum, and then corrected by the atmosphere, that is, the object side is obtained. Accurate spectrum. Through the spectral feature comparison, the target tracking is guided according to the fingerprint information of the precise spectrum. A large number of field experiments prove that this method can accurately measure the weak targets in the remote sensing background, improve the stability and accuracy of tracking, and lay a foundation for the subsequent remote sensing target recognition.

关键词： small and weak target spectral unmixing atmospheric correction target tracking

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Location recognition based on image local feature matching 11

Location recognition based on image local feature matching

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11th international symposium on multispectral image processing and pattern recognition: Automatic Target recognition and Navigation, MIPPR 2019

作者： Gao, Zhuoyue Chai, Lin Jin, Lizuo Department of Automation Southeast University Nanjing city Jiangsu province China

ISBN: (纸本)9781510636354

Location identification is a research hot spot in computer vision. For a scene with the building, the location recognition method in this paper can accurately detect the building and identify the location. The specific method is to firstly determine the local feature extraction method to obtain more stable local features under different conditions. Secondly, encode image features effectively, build Scene codebook, establish image index, and compare image similarity. Fast and large-scale image retrieval can be achieved in this way. Then, in order to filter out the error matching results and choose the best matching result, a matching algorithm based on local spatial consistency is proposed. The shape model voting method with small calculation amount is proposed to obtain the position of the building in the scene picture. The experimental results show that the method can more accurately identify the location of the building, and the building images show good robustness and distinguishability when they are transformed. © 2020 SPIE.

关键词： Feature extraction

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A New Robust image Feature Point Detector 11

A New Robust Image Feature Point Detector

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Zhao, Yi HuBei Presch Teacher Coll Wuhan 430223 Peoples R China

ISBN: (纸本)9781510636361

A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noise efficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal and initial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, the experiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidian distance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in all regions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the new robust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that of the points without those limitations.

关键词： Feature Point Detection Computer Vision Noise Smooth Space Limitation Robustness

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A Real-time Location Algorithm for Critical Points Based on TMS320C6678 11

A Real-time Location Algorithm for Critical Points Based on ...

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Hong, Hanyu Chen, Huiyuan Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Res Ctr Video Image & High Denit Project Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636361

This paper analyzes the importance of real-time identification of key parts of non-cooperative targets in military applications, and proposes the shortcomings of current target detection algorithms, and a real-time localization algorithm for target points based on TMS320C6678 parallel processing. The algorithm firstly locates the approximate position of the target by downsampling and horizontal vertical projection of the original image, then locates the target key points by polar coordinate transformation and target edge curvature solution, and combines the multi-core of TMS320C6678 DSP to optimize the algorithm. Finally, the algorithm is parallel optimized on the TMS320C6678 multi-core DSP hardware platform, which can accurately locate the target parts within 5ms.

关键词： Target Detection Real-time positioning Parallel optimization TMS320C6678

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MIPPR 2019: pattern recognition and Computer Vision

MIPPR 2019: Pattern Recognition and Computer Vision

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11th international symposium on multispectral image processing and pattern recognition: pattern recognition and Computer Vision, MIPPR 2019

ISBN: (纸本)9781510636378

The proceedings contain 63 papers. The topics discussed include: rural settlements extraction based on deep learning from high spatial resolution remote sensing imagery;G-CNN type recognition of typical aircraft based on target characteristics;semantic segmentation network combined with edge detection for building extraction in remote sensing images;multi-scales feature integration single shot multi-box detector on small object detection;a novel neural network approach for error transfer analysis in electronic instrument transformer;large-scale book page retrieval by deep hashing networks;and object direction estimation by constrained convolutional neural network.

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