Full Pixel Matching between Images for Non-linear Registration of Objects

Yaguchi, Yuichi; Iseki, Kenta; Oka, Ryuichi

doi:10.2197/ipsjtcva.2.1

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…The visible image is constructed by a visible light sensor by capturing different reflections of light from different object surfaces, while the thermal infrared image is constructed by an infrared sensor by capturing the difference in infrared thermal radiation intensity of different parts of natural objects [2], [3], [4], [5], [6]. The large differences between visible and thermal infrared images pose a very significant challenge for template matching schemes, whereas most existing template matching methods rely on linear, monotonic, or functionally constrained matching rules [29], [30], [31]. Special algorithms are required for template matching between heterogeneous images.…”

Section: Introductionmentioning

confidence: 99%

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

Mei

Wang²,

Wang

et al. 2022

IEEE Access

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A fast and robust template matching scheme, called Matching by Slice Transform Matrix Mapping (MSTMM), is proposed for the matching difficulty under occluded scenarios caused by nonlinear intensity differences and structure differences between visible and thermal infrared images. The first step in the MSTMM scheme was to extract information about the distribution of pixels with the same gray level through the developed Expanded Slice Transform with Adaptive Gray Level (EST-AGL). After completing the construction of the EST-AGL matrix for all image patches, different EST-AGL matrices were mapped to different integers or floats through the traditional special integer mapping mechanism or the neural network mapping mechanism. Finally, template matching between visible and thermal infrared images was achieved by evaluating the similarity of correlation mapping surface images through the Normalized Cross Correlation (NCC) algorithm. The proposed EST-AGL method can overcome the nonlinear intensity differences between visible and thermal infrared images by extracting the structural features of the image. The mapping mechanism of the MSTMM scheme can reduce the structural differences between the normal template image and the query image under an occluded scenario by increasing the similarity between the normal image patches and the image patches with occlusion. The proper mapping mechanism ensures the high performance of the MSTMM scheme by using only the simple NCC algorithm instead of other time-consuming anti-occlusion dense feature algorithms in the similarity evaluation stage. The three main experimental results of the MSTMM scheme are as follows: (1) the scheme of MSTMM can achieve template matching in only 0.015 seconds when a 64 × 64 template image slides on a 256 × 256 query image on a hardware platform with limited resources; (2) the matching success rate of the MSTMM scheme can reach up to 75% among 2107 experimental samples; and (3) the neural network training in the neural network mapping mechanism only takes at least 104.4 seconds on the CPU.INDEX TERMS Template matching, multimodal image, heterogeneous image, multisource image, visible and infrared image, image matching, neural network.

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Section: Introductionmentioning

confidence: 99%

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

Mei

Wang²,

Wang

et al. 2022

IEEE Access

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“…The main difference between these studies is the definition of the cost function; The 2D-DP focuses on finding the mapping between two images with a pre-defined cost function, while the likelihood of 2-D HMMs is defined between an input image and the distribution which is estimated from multiple training images. Although some efficient approximation algorithms have been proposed for the 2D-DP problem [13]- [16], they still need high complicated costs and prior knowledge to determine the cost function is required for representing an accurate elastic matching dependently on image variations.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition Based on Separable Lattice Trajectory 2-D HMMs

Tamamori

Nankaku

Tokuda

2014

IEICE Trans. Inf. & Syst.

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Akira TAMAMORI†a) , Nonmember, Yoshihiko NANKAKU †b) , and Keiichi TOKUDA †c) , Members SUMMARYIn this paper, a novel statistical model based on 2-D HMMs for image recognition is proposed. Recently, separable lattice 2-D HMMs (SL2D-HMMs) were proposed to model invariance to size and location deformation. However, their modeling accuracy is still insufficient because of the following two assumptions, which are inherited from 1-D HMMs: i) the stationary statistics within each state and ii) the conditional independent assumption of state output probabilities. To overcome these shortcomings in 1-D HMMs, trajectory HMMs were proposed and successfully applied to speech recognition and speech synthesis. This paper derives 2-D trajectory HMMs by reformulating the likelihood of SL2D-HMMs through the imposition of explicit relationships between static and dynamic features. The proposed model can efficiently capture dependencies between adjacent observations without increasing the number of model parameters. The effectiveness of the proposed model was evaluated in face recognition experiments on the XM2VTS database.

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