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2011 International Conference on Electric information and Control Engineering, ICEICE 2011

作者： Zhang, Jing Sang, Hongshi Shen, Xubang National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424480395

SIFT (Scale Invariant Feature Transform) is one of most popular approach for feature detection and matching. Many parallelized algorithms have been proposed to accelerate SIFT to apply into real-time systems. This paper divides the researches into three different categories, that is, optimizing parallel algorithms based on general purpose multi-core processors, designing customized multi-core processor dedicated for SIFT and implementing SIFT based FPGA (Field Programmable Gate Arrays). Overview of the three type researches and analysis of task-level parallelism are presented in this paper. © 2011 IEEE.

关键词： Real time systems

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Efficient systolic implementation of DFT using only the first-order moments

Efficient systolic implementation of DFT using only the firs...

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2010 International Conference on Intelligent Control and information processing, ICICIP 2010

作者： Liu, Jianguo Liu, Zhenbing Pan, Chao State Key Lab. for Multi-Spectral Information Processing Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424470488

The Discrete Fourier transform (DFT) is an important tool in digital signal processing. In this paper, a novel approach to DFT is proposed. The computation of DFT is transformed to the computation of the first-order moments by the simple mathematical deductions. It is well known that the first-order moments can be computed efficiently using only additions. An efficient systolic array is designed to implement DFT. The comparison with the existing methods shows the advantages of our method. The approach is also applicable to DFT inverses. © 2010 IEEE.

关键词： Discrete Fourier transforms

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Intensity-based correlation for heterogeneous images scene matching

Intensity-based correlation for heterogeneous images scene m...

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MIPPR 2007: Automatic Target Recognition and Image Analysis;and multispectral Image Acquisition

作者： Sheng, Zhong Huimin, Cao Tianxu, Zhang State Key Lab. for Multi-spectral Information Processing Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819469502

Downward looking scene matching is an important technique of the aircraft automation guidance. To solve the heterogeneous images scene matching problem, we present two effective methods based on intensity-based correlation in this paper. One is to search the real match position based on the feature of the peak on the correlation surface. We propose a criterion to search the proper matching. The other is to use a non-linear filter to pre-process the images, which reduces the influence of ambient lighting while keeping the necessary image details since the contour of the scene is the stable and unchanged feature. Also, we use a Fourier analysis to explain the contribution of different frequency spectrum in the correlation. By using this frequency information, we propose a simpler kernel filter method based on pre-process, which has the similar performance with non-linear filter pre-process but has less computation complexity. This simple kernel is more suitable for the embedded DSP real-time application.

关键词： Pattern matching

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High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks

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Plant Phenomics 2020年第1期2卷 255-268页

作者： Liang Liu Hao Lu Yanan Li Zhiguo Cao National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074 HubeiChina The University of Adelaide AdelaideSA 5005Australia School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan430205 HubeiChina

Rice density is closely related to yield estimation,growth diagnosis,cultivated area statistics,and management and damage ***,rice density estimation heavily relies on manual sampling and counting,which is inefficient and *** the prevalence of digital imagery,computer vision(CV)technology emerges as a promising alternative to automate this ***,challenges of an in-field environment,such as illumination,scale,and appearance variations,render gaps for deploying CV *** fill these gaps towards accurate rice density estimation,we propose a deep learningbased approach called the Scale-Fusion Counting Classification Network(SFC^(2)Net)that integrates several state-of-the-art computer vision *** particular,SFC^(2)Net addresses appearance and illumination changes by employing a multicolumn pretrained network and multilayer feature fusion to enhance feature *** ameliorate sample imbalance engendered by scale,SFC^(2)Net follows a recent blockwise classification *** validate SFC^(2)Net on a new rice plant counting(RPC)dataset collected from two field sites in China from 2010 to *** results show that SFC^(2)Net achieves highly accurate counting performance on the RPC dataset with a mean absolute error(MAE)of 25.51,a root mean square error(MSE)of 38.06,a relative MAE of 3.82%,and a R^(2) of 0.98,which exhibits a relative improvement of 48.2%*** over the conventional counting approach ***,SFC^(2)Net provides high-throughput processing capability,with 16.7 frames per second on 1024×1024 *** results suggest that manual rice counting can be safely replaced by SFC^(2)Net at early growth *** and models are available online at https://***/sfc2net.

关键词： illumination estimation render

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Density compensation to reference map of gravity navigation

Density compensation to reference map of gravity navigation

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2012 International Conference on Modelling, Identification and Control, ICMIC 2012

作者： Ma, Jie Yang, Fan Yan, Zu Xiong, Ling National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9780956715715

The current method of gravity gradient maps modeling is based on digital elevation model, the effects of isostasy in gravity gradient map modeling are researched and analyzed in this paper, and a novel method of modeling gravity gradient maps was presented. The current isostasy theory shows that the density of crust changes with elevation, so assigning a constant value to terrain's density is not accurate enough. At first, two kinds of isostasy model are analyzed, and on the basis of isostasy model, the gravity gradient map is modeled by using improved terrain density model. Then experimental results shows that Pratt model impose much more effects on gravity gradient map than Airy model. Consider the two models' effects in gravity gradient map modeling, Pratt Model has a great performance in the sea area, and Airy Model can be ignored the impact in the land area. © 2012 Huazhong Univ of Sci & Tec.

关键词： Digital instruments

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A new method of ship detection for SAR images

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International Journal of Advancements in Computing technology 2011年第9期3卷 64-71页

作者： Hu, Huozhen Tian, Jinwen Dai, Guangming Wang, Maocai Peng, Yi National key laboratory of science and technology on multi-spectral information processing Huazhong university of Science and technology Wuhan Hubei China Department of Computer Science China University of Geosciences Wuhan Hubei 430074 China Laboratory Equipment Division China University of Geosciences Wuhan Hubei 430074 China

The ship is an important kind of military target. The ship detection is the study focus of the SAR image processing currently. But most of the traditional ship detection methods can be affected by the SAR speckle, and many false alarms may exist in the detection result. To improve the ship detection capability, a new method of ship detection for SAR images is put forward by this paper. It uses mean filtering to smooth the original image and the minimum bias-normal distribution index method to realize automatic segmentation. It detects candidate targets with Mathematical Morphology and removing tiny area connected component. In order to remove false alarm targets and retain geometric features of images, it utilizes mathematical morphology filtering. Finally, it further removes the false alarm targets based on the characteristic of the target histogram. Experimental result indicates that the method can effectively and rightly detect ship of SAR images. Moreover, it has significant practical value.

关键词： Synthetic aperture radar

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Real time peaks extraction

Real time peaks extraction

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MIPPR 2007: Automatic Target Recognition and Image Analysis;and multispectral Image Acquisition

作者： Xiao, Zhou Ruolan, Hu Guilin, Zhang State Key Lab for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819469502

Peaks extraction is a kind of post-process in many image application or vision tasks that can be used for finding the optimum solution in the solution space. In this paper a real time method is proposed. A candidate queue is first build for containing highest peaks in the image in ascending order. Then the image is scanned in sequence. At each scanning position every candidate in the queue is updated respectively by some criterions given in this paper. After the image is scanned over, the highest peaks in the image is achieved in the queue. All the process can be accomplished by logic circuit, so the method is very suitable for hardware system such as FPGA and so on.

关键词： Feature extraction

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Second order morphology algorithm for IR small target enhancement in sea clutter background

Second order morphology algorithm for IR small target enhanc...

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2010 International Conference on Optics, Photonics and Energy Engineering, OPEE 2010

作者： Xiao, Zhou Xingang, Mou Guilin, Zhang Natl. Key Laboratory of Science and Technology on Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424452354

The framework of a second order morphology algorithm is proposed to enhance the dim small infrared target in sea clutter background with strong detector noise. First, a morphological filters bank is given. Each filter in the bank is designed to suppress its corresponding type texture. The second order process can be accomplished in two steps. The original infrared image is first filtered using each filter separately, and then the first order result is obtained by the combination of the filtered result images. The other step is essentially the same as the first one except the combination manner of the filtered result images. Second, the traditional SNR gain based evaluation of different small target enhancement algorithms is examined and a more reasonable method based on the distance between the target and the maximal background (DTMB) is described in this paper. Third, using the new evaluation method, the comparison is given by experiment through a sequence of infrared images with a dim small target. Experimental results demonstrate that the new proposed algorithm performs better in sea clutter background with strong detector noise than the traditional ones. © 2010 IEEE.

关键词： Morphology

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Submarine navigation based on gravity gradient-terrain matching

Submarine navigation based on gravity gradient-terrain match...

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MIPPR 2007: Remote Sensing and GIS Data processing and Applications;and Innovative multispectral technology and Applications

作者： Xiong, Ling Ma, Jie Zhang, Li Tian, Jin-Wen Liu, Jian State Key Laboratory for Multi-spectral Information Processing Technologies Education Ministry for Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China

ISBN: (纸本)9780819469540

Based on the geophysics technology, a gravity gradient-terrain matching submarine navigation approach is proposed in this paper. The submarine's current position obtained by matching the measured gravity gradient data to the prepared gravity gradient reference map is used to correct the inertial navigation system's accumulated error. Although the precision gradiometer is in use, there is no world-wide gravity gradient map. The ocean's gravity gradient data is even scarce. Therefore, a gravity gradient matching navigation system directly utilizing the gravity gradient reference map can't be realized. With the sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain elevation data and the preparing approach of the gravity gradient map is proposed in detail in the paper. Since the seabed terrain elevation map, especially highly accurate offing terrain elevation map has been presurveyed, the location can be actualized through matching the real-time measured gravity gradient data to the prepared gravity gradient reference map. Simulations show that the submarine navigation approach on gravity gradient -terrain matching is feasible and can be put into practice.

关键词： Navigation

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Repeatable adaptive keypoint detection via self-supervised learning

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Science China(information Sciences) 2022年第11期65卷 123-147页

作者： Pei YAN Yihua TAN Yuan TAI National Key Laboratory of Science & Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology

keypoint-based matching is a fundamental technology for different computer vision tasks, in which keypoint detection is a crucial step and directly affects the entire performance. Based on deep learning approaches, the learning-based keypoint detectors have been significantly developed. To further improve the accuracy of high-level matching tasks, the extracted keypoints should provide more accurate point-to-point correspondences and maintain a uniform spatial distribution. Based on this idea, a self-supervised learning method of keypoint detection named repeatable adaptive point is proposed. This method consists of a selfsupervised objective and an optimization algorithm. The objective maximizes the repeatability measure with the sparsity constraint of keypoints. This sparsity constraint is formulated by combining the non-maximum suppression operation and the penalty function of keypoint number, which generally makes keypoints have a uniform spatial distribution. A novel approximate alternate optimization algorithm is proposed to maximize the above objective, whose convergence is proved in theory. The proposed detector is “adaptive” because the combinations of it and some existing descriptors can adapt to high-level matching tasks with a fast convergence speed. Specifically, the combinations of it and SuperPoint/HardNet descriptors achieve stateof-the-art accuracy on three high-level tasks based on image matching, namely homography estimation,camera pose estimation, and three-dimensional reconstruction. Furthermore, the proposed method converges faster on new scenes compared with the state-of-the-art method that jointly optimizes the detector and the descriptor.

关键词： keypoint detection convolutional neural network image matching self-supervised learning repeatable

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