Template matching between visible light and infrared images

In the field of computer vision, template matching technology is an important research direction. This technique compares the template image with the sample image to find out the position of the template image in the sample image. It has the characteristics of simple algorithm, small amount of calculation and high recognition rate, so it is usually used in other computer vision fields such as object detection and target tracking. In addition, with the popularity of infrared sensors, and infrared images can obtain additional information that is not included in visible light images, the integrated processing of visible light and infrared information has always been a research hotspot. The traditional template matching algorithm mainly focuses on the matching between visible light images. For the information difference between visible light and infrared images, the traditional template matching algorithms are difficult to achieve accurate matching between the two types of images, and the amount of calculation is large. In response to this problem, a template matching algorithm based on feature extraction of convolutional neural networks is proposed in this paper. Our method draws on the robust template matching using scale-adaptive deep convolutional features. We use a scaleadaptive method to extract the deep features of visible light and infrared images, and then uses the traditional NCC matching algorithm to obtain the matching position of the template on the feature map. Then the regression and optimization of the template position are performed to obtain the position of the template image on the sample image. The research results show that our method can achieve the matching of the infrared template on the visible light image, and the position error is not large.

[1]  Heng ZHANG,et al.  Guided Attentive Feature Fusion for Multispectral Pedestrian Detection , 2021, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[2]  Stefan Oehmcke,et al.  Attentional Feature Fusion , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[3]  Humberto Loaiza-Correa,et al.  Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion , 2020, Data in brief.

[4]  Ailong Ma,et al.  A Novel Robust Feature Descriptor for Multi-Source Remote Sensing Image Registration , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Wael Abd-Almageed,et al.  QATM: Quality-Aware Template Matching for Deep Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Shuang Wang,et al.  A deep learning framework for remote sensing image registration , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[7]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

[8]  Wei Wu,et al.  High Performance Visual Tracking with Siamese Region Proposal Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Changick Kim,et al.  Robust template matching using scale-adaptive deep convolutional features , 2017, 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[10]  A. Smeulders,et al.  Siamese Instance Search for Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  S. Avidan.  Best-Buddies Similarity for robust template matching , 2015, CVPR.

[12]  Nikos Komodakis,et al.  Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  J. Sarvaiya,et al.  Image Registration by Template Matching Using Normalized Cross-Correlation , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[14]  Fan Fan,et al.  Visible/Infrared Combined 3D Reconstruction Scheme Based on Nonrigid Registration of Multi-Modality Images With Mixed Features , 2019, IEEE Access.

[15]  Federico Tombari,et al.  Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Min-Seok Choi,et al.  A novel two stage template matching method for rotation and illumination invariance , 2002, Pattern Recognit..