Infrared and visible images registration with adaptable local-global feature integration for rail inspection

Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

doi:10.1016/j.infrared.2017.09.013

Cited by 43 publications

(16 citation statements)

References 51 publications

(47 reference statements)

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“…These methods use infrared images to accomplish the tasks that are impossible for visible images. The infrared and visible image match has been widely applied in an unmanned aerial vehicle [3,4], remote sensing satellite [5,6], and security monitoring platform [7,8].…”

Section: Introductionmentioning

confidence: 99%

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

et al. 2020

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Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and visible images. Second, common feature points of infrared and visible images are extracted based on the consistent edge structures. Third, feature descriptions are established according to the edge structure attributes including edge length and edge orientation. Lastly, feature correspondences are calculated according to the distance of feature descriptions. Due to the utilization of consistent edge structures of infrared and visible images, the proposed DDCE method can improve the detection repeatability and the matching accuracy. DDCE is evaluated on two public datasets and are compared with several state-of-the-art methods. Experimental results demonstrate that DDCE can achieve superior performance against other methods for infrared and visible image match.

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Section: Introductionmentioning

confidence: 99%

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

et al. 2020

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“…Telke et al [29] improved signal processing for video-based measurement methods. Tang et al [30] added new information of the infrared spectrum to the point clouds, Qiu et al [21] added the information of the digital terrain models (DTM) to the point clouds.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

et al. 2019

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The precision of railway map is becoming a significant issue for autonomous train scheduling, monitoring and maintenance, related location-based service (LBS), and further ensuring travel safety. Mobile 3D laser scanning is an efficient method for making relative high-precision railway track maps, particularly during the night period of railway maintenance, for light detection and ranging (LiDAR) can work without ambient light. In this paper, we propose an efficient and accurate railway track vectorization method based on the LiDAR point clouds from the self-built train Mobile Laser Scanning (MLS) system. Our method takes full use of railway track geometry and reflection intensity feature of LiDAR, without any trajectory prior information. Firstly, clear track points are filtered by intensity; then, a K-means clustering fused Region-Grow Fitting algorithm is applied. It can not only extract the line vector of railway track, but also can tell the track branches apart, especially on bends and turnout. Experiments were carried on using point clouds with an average density of 490 points per square meter. The experimental results show that the method not only can quickly extract linear objects such as railway track and catenary, but also can detect the railways even in complex real-world topologies such as at bends and turnouts. The precision of the detection area in bends and turnouts are 90.32% and 81.31% respectively, the sensitivity is 83.27% and 83.33%, respectively. Moreover, it can identify the track networks.

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“…In general, 3D information can be acquired by binocular cameras [17], Kinect sensor [18][19][20], Leap Motion [21], and other devices. Those devices can be usually utilized to obtain depth information by spatial relationship of different direction [17] or infrared reflection [22], which can conveniently acquire noncontact image for recognition and classification instead of wearing the complicated equipment. For example, Ťupa et al [23,24] presented the detection of selected gait attributes by Microsoft Kinect image and depth sensors to track movements in three-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

Peng

2018

Journal of Sensors

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Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.

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Infrared and visible images registration with adaptable local-global feature integration for rail inspection

Cited by 43 publications

References 51 publications

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

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