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Coupled discriminant mappings for heterogeneous face recognition 11

Coupled discriminant mappings for heterogeneous face recogni...

引用

11th International Symposium on Multispectral image Processing and Pattern Recognition: Pattern Recognition and Computer Vision, MIPPR 2019

作者： Cao, Xinli Wen, Ke Huang, Likun Tang, Bing Zhang, Wei Hubei Engineering Research Center of Video Image and HD Projection School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510636378

Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method. © SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Mapping

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Accurate extraction of line-structured optical stripe centerlines under low exposure 12

Accurate extraction of line-structured optical stripe center...

引用

SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Zhu, Cheng Tian, Keyun Gao, Yao Ding, Zhiqiang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The line-structure light bar images acquired in industrial metal parts inspection may suffer from inaccurate extraction of the centerline of the line structure light bar due to the interference of ambient lighting and strong diffuse reflection and weak reflection of the part surface material. For this reason, this study proposes a method based on G-HL (Gauss-Hyper-Laplace Distribution) curve fitting to improve the center extraction accuracy of the line structure light bar in complex environments. First, the method uses Gaussian filtering to denoise the line-structured light image. Then, the laser line target pixel region is localized by finding the upper and lower boundaries of the line-structured light bar using the neighborhood gradient for each column of the line-structured light image, and the width of the line-structured light bar is calculated by the difference of the boundary coordinates, and the fitting interval is determined. Finally, within the fitting interval, the G-HL curve is used to fit the gray scale of the line-structured light cross-section to obtain the G-HL parameter, and the point of the maximum value of the G-HL curve is taken to be the coordinates of the center of the line-structured light. Experiments prove that the proposed method can accurately extract the center of the line structure light in low exposure. Compared with the methods such as the grayscale center of gravity method and the extreme value method, the proposed method has high accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Curve fitting

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Photovoltaic panel string detection based on prior knowledge and feature learning 12

Photovoltaic panel string detection based on prior knowledge...

引用

SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Li, Bolin Cheng, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Inspection

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An improved image segmentation algorithm based on MRF and Sobel operator 12

An improved image segmentation algorithm based on MRF and So...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Gao, Yao Tian, Keyun Zhu, Cheng Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In response to the issues of over-segmentation, excessive noise, and suboptimal segmentation results commonly encountered in existing image segmentation algorithms based on Markov Random Fields (MRF), this paper proposes an enhanced image segmentation algorithm that integrates MRF with the Sobel operator. The algorithm begins by performing an initial segmentation of the image using a Markov Random Field (MRF)-based method. Subsequently, an enhanced Sobel operator is employed to eliminate noise points and extract fine edge details from the image. Finally, the segmentation result is refined through pixel-wise operations with the edge detection result, resulting in the ultimate segmentation output. The evaluation of segmentation performance is conducted using the Dice coefficient and Mean Hausdorff Distance as assessment metrics. Through experimental analysis, the method in this paper can improve the segmentation effect of the traditional MRF segmentation algorithm, and has better performance and higher adaptivity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

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Defect detection of photovoltaic panel based on morphological segmentation 12

Defect detection of photovoltaic panel based on morphologica...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Cheng, Bolin Li, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

The automatic inspection of photovoltaic panels based on infrared images is one of the important tasks in the daily maintenance of photovoltaic panels in photovoltaic power plants. In this paper, a defect detection method of infrared thermal image photovoltaic panel based on morphological segmentation is proposed. First of all, according to the infrared characteristics of the photovoltaic plant station and the morphological characteristics of the photovoltaic panel, the accurate region of each photovoltaic panel is determined in the inspection image. Then, statistical method is used for feature analysis to determine whether there are defects in photovoltaic panels, and then the defective photovoltaic panels are segmented and different defects are classified according to morphological characteristics, and finally the defect detection and classification of photovoltaic panels are realized. Through the experiment in the actual inspection image of photovoltaic plant station, it is verified that the proposed method has a high accuracy of defect detection and recognition. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Morphology

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An Improved Watershed Foreign Object Detection of Road Surface Based on Adaptive Gaussian Threshold 12

An Improved Watershed Foreign Object Detection of Road Surfa...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Liang, Yongjie Tian, Keyun Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To a certain extent, foreign objects on road surfaces can cause damage to vehicles and increase the risk of accidents, which is even more immeasurably harmful in such special scenarios as airports. Effective detection of foreign objects on road surfaces is of great significance to airport flight safety and road safety maintenance. Due to the traditional watershed segmentation method in the recognition of foreign objects, there are obvious over-segmentation, noise complexity and so on. Therefore, this paper proposes an improved watershed segmentation method based on Lab-color space using image filtering techniques and morphological operations to initially separate the image foreground and background, combined with Adaptive Gaussian thresholding, labeling method for detection. This effectively solves the problem of over-segmentation, can better suppress image noise, and accurately detects foreign objects on road surfaces. © 2024 SPIE.

关键词： Object detection

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A Robust Algorithm for Reading Recognition of Pointer Instrument with Local Matching 12

A Robust Algorithm for Reading Recognition of Pointer Instru...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Niu, Tun Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The inspection and recognition of readings on pointer instruments are important tasks in power inspection. Due to the diversity and the complex dial information of pointer instruments, the adaptability and low accuracy of existing methods are not enough. In this paper, we proposed a pointer instrument reading recognition algorithm based on local fast matching. Firstly, YOLOv8 is used to locate and segment the instrument in the inspection image. The positions and values of key scale are marked in the template image. Based on the key local information, a mapping relationship is established between the instrument location region in the inspection image and the template image. Then, the Hough transform is used to detect the position of the pointer, map it to the template image, and finally calculate the instrument reading based on the relative positions of the key graduations on the template image and the mapped pointer position. Experimental results demonstrate that the proposed method achieves a relative error within 5% for pointer instrument reading recognition in indoor substations. © 2024 SPIE.

关键词： Inspection

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Accurate Laser Line Extraction Algorithm Based on Morphological Features under Strong Interference 12

Accurate Laser Line Extraction Algorithm Based on Morphologi...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Wu, Yuanzhe Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In outdoor complex lighting environments, laser stripe extraction becomes challenging due to the possibility of strong exposures in the external environment as well as reflective interference on the road surface. In order to overcome these problems, this paper proposes a multi-feature structured light extraction algorithm based on seed neighborhoods, which utilizes multiple seed points and comprehensively considers the laser stripe features, especially the morphological features of the optical stripes in their surrounding neighborhoods, so as to obtain the approximate distribution location of the real laser stripe. Finally, the laser streak extraction is performed by the gradient difference of the optical streak boundary as well as its own brightness. The experimental results show that the method is still effective in extracting laser lines even in high exposure environments where there are interferences such as reflections on the road surface. © 2024 SPIE.

关键词： Luminance

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Dense center Prediction for centerline Extraction Based on BP Network 12

Dense Center Prediction for Centerline Extraction Based on B...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Gui, Chuanqi Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In the line structured light three-dimensional measurement system, high-precision laser stripe centerline extraction is the key to improving measurement accuracy. The laser line extraction technology based on neural network can automatically detect and extract laser lines with high accuracy and robustness. In this paper, a BP neural network containing one hidden layers is constructed, and the centerline obtained through feature extraction of the network can effectively overcome the influence of noise interference. Compared with the gray-gravity method and the Steger algorithm, the method in this paper has the advantage of high accuracy and can meet the requirements for extracting the center of complex light strips. © 2024 SPIE.

关键词： Extraction

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An Improved Bilateral Filtering Based on Curvature Feature for Point Cloud Denoising in Road Surface Detection 12

An Improved Bilateral Filtering Based on Curvature Feature f...

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SPIE 12th International Symposium on Multispectral image Processing and Pattern Recognition, MIPPR 2023

作者： Chen, Si Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Technology Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To effectively remove noise from the point clouds obtained for road surface defect detection while preserving the feature information, a bilateral filtering algorithm based on curvature features is proposed for denoising road surface defect point clouds. First, divide the noise points in the point cloud into several regions based on their distribution characteristics and curvature features. Then use statistical filtering to remove noise points in the obvious noise area and the flat area. Finally, an improved bilateral filtering algorithm is applied to smooth the noise points in the complex detail area. © 2024 SPIE.

关键词： Surface defects

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