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Design of polarimetric camera based on the single-layer twisted nematic liquid crystal device 12

Design of polarimetric camera based on the single-layer twis...

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SPIE 12th international symposium on multispectral image processing and pattern recognition, mippr 2023

作者： Ye, Mao Zhu, Wenjie Liu, Kewei Zhang, Xinyu Institude of Artificial Intelligence Huazhong University of Science and Technology Hubei Wuhan430074 China National Key Laboratory of Science & Technology on Multispectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510674929

ISBN: (纸本)9781510674912

Polarization is the asymmetry of a light wave's electric field vibration direction with respect to its propagation direction, as a crucial clue for describing the light, which is one of the most apparent signs that distinguishes transverse waves from longitudinal waves. the current mainstream polarimetric cameras typically adopt a division of focal plane imaging architecture, in which a metal wire-grid structure is coupled with a detector to acquire the polarization state of the target. However, the structure of the division of focal plane reduces effective spatial resolution of the detector and requires complex registration algorithms to achieve pixel-level polarization acquisition, which leads to high equipment costs and impedes the development and application of polarimetric cameras in industrial production. In this paper, a single-layer twisted nematic liquid crystal (TNLC) is inserted to the imaging optical path of traditional camera to realize the polarization acquisition, allowing for a cost-effective transition from traditional devices to polarization imaging systems. the imaging detector coupled with the TNLC device can acquire polarization states ranging from 10 to 90-degree by adjusting the external voltage applied on the LC device. Moreover, a polarization angle calibration method based on a single-layer TNLC is proposed in this paper, providing a reliable reference for electrically controlled polarization angle acquisition. the experimental results acquired by presented imaging system demonstrate the feasibility and effectiveness of the proposed method. the TNLC based imaging scheme presented in this paper provides a solid foundation for developing simple, low-cost, and efficient polarimetric cameras. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cameras

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Turnover and Shape Filter Based Feature Matching for image Stitching 11

Turnover and Shape Filter Based Feature Matching for Image S...

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

作者： Song, Shuang He, Xinguo He, Lin South China Univ Technol Sch Automat Sci & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781510636385

this work intends to deal with the problem of misalignment in image stitching caused by small overlap area. To reduce mismatches between matched features pairs in two connected images, random sample consensus (RANSAC) [1] is usually adopted, which works under the assumption that the sampling of matched feature points with the largest number of inliers should be utilized to compute geometric matrix. However, this assumption does not hold in the case of small overlap area between the connected images, as compressing or turning over the image may result in better spatial consistency of matched feature points. therefore, we propose a turnover and shape filter based feature matching method for image stitching. In the method, a turnover and shape filter is firstly used to filter out the samplings resulted from turnover and compression, which is then connected to RANSAC to yield final inliers. Experimental results from real-world datasets validate the effectiveness of our method.

关键词： Feature matching turnover and shape filter RANSAC image stitching

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A novel self-relearning approach for Landsat image change detection 11

A novel self-relearning approach for Landsat image change de...

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

作者： Chang, Xiaoyu Huang, Xin Li, Jiayi Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Survey Mapping & Remo Wuhan 430079 Peoples R China

ISBN: (纸本)9781510636347

Land cover composition and change are important aspects for many scientific research and socioeconomic assessments. the multi-date land cover change detection is generally more difficult and time-consuming to select enough training samples when considering multi-date image labels at the same location. To improve the accuracy for multi-date change detection, this study proposed a new algorithm framework, combining self-learning and relearning algorithm. Wuhan was selected as the experimental area, and Landsat images in 2005 and 2016 were used to extract six main types of change classes. Firstly, PCM (primitive co-occurrence matrix) and the minimum class certainty are used to ensure the high confidence of selected candidate set samples, and then the most informative samples are identified for classification from the candidate samples. To save computing costs, we adopt clustering method to reduce the self-relearning samples. Based on our experimental results, the self-relearning algorithm increases the final classification accuracy by approximately 2.5% (from 92.64% to 95.09%) in the case of using few initial training samples, providing a feasible solution for the multi-date change detection.

关键词： land cover change detection Landsat image relearning self-learning primitive co-occurrence matrix

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Graph Fusion Based Hyperspectral image Classification 11

Graph Fusion Based Hyperspectral Image Classification

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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： Luo, Haokun He, Lin Yu, Long South China Univ Technol Sch Automat Sci & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781510636422

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relieve the dilemma Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph-fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

关键词： hyperspectral image classification semi-supervised learning graph-fusion Gabor feature

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Adaptive Locality Preserving Projection for Hyperspectral image Classification 11

Adaptive Locality Preserving Projection for Hyperspectral Im...

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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： He, Lin Chen, Xianjun Xie, Xiaofeng Luo, Haokun South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China

ISBN: (纸本)9781510636422

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relief the dilemma Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

关键词： Hyperspectral image local preserving projection superpixel segmentation covariance feature Riemannian manifold

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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 Fast Color Cast Detection and Correction Method for Large-field Video-image in Nature Scene 10

A Fast Color Cast Detection and Correction Method for Large-...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - multispectral image Acquisition, processing, and Analysis (mippr)

作者： Hong, Ke Shi, Yu Hua, Xia Wuhan Inst Technol Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

Color cast is a major problem of target detection and recognition base on image feature analysis. A color cast detection and correction algorithm for video-image in nature scene is proposed in this paper. To overcome the inaccurate detection in existed color cast detection method based on RGB space,the analysis method based on the color distribution characteristics of the equivalent circle in Lab space is proposed. In order to achieve better color correction results, the gray balance and perfect reflection method is combined to reproduce the actual color of the objects, especially a modified method for images with severe color deviation. Experimental results show that the proposed methods can detect the degree of color cast correctly, achieve better correction results, and meet the real-time requirements.

关键词： color cast detection color correction image enhancement real-time

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image Compression Based on Orthogonal Balanced Multiwavelets with Symmetry/antisymmetry 10

Image Compression Based on Orthogonal Balanced Multiwavelets...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Zhang Liping Zhao Yi Changjiang Polytech Wuhan Hubei Peoples R China HuBei Presch Teacher Coll Wuhan Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Multiwavelets have orthogonality, compacted support and symmetry simultaneously, these properties are very important for signal processing. However, most of Multiwavelets require related prefilters. An approach to construction of symmetry/antisymmetry orthogonal filter is proposed and its corresponding balanced filter is constructed, no any prefilter is necessary. Experimental results prove its performance is superior to DGHM and CL multiwavelets, higher than Bi9/7.

关键词： image Compression Multiwavelet Balancing Coefficients Rearrangement

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Infrared vehicle recognition using unsupervised feature learning based on K-Feature 10

Infrared vehicle recognition using unsupervised feature lear...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - Automatic Target recognition and Navigation

作者： Lin, Jin Tan, Yihua Xia, Haijiao Tian, Jinwen Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Sch Automat Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. the infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. then, the unsupervised feature learning based on K-feature, which is generated by K-means clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

关键词： vehicle recognition K-Feature infrared image unsupervised feature learning

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Dim target detection method based on salient graph fusion 10

Dim target detection method based on salient graph fusion

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10th international symposium on multispectral image processing and pattern recognition (mippr) - Automatic Target recognition and Navigation

作者： Hu Ruo-lan Shen Yi-yan Jiang Jun Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

关键词： dim target detection salient graph image fusion

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