A landmark matching algorithm using the improved generalised Hough transform

A LANDMARK MATCHING ALGORITHM FOR THE GEOSTATIONARY SATELLITE IMAGES BASED ON MULTI-LEVEL GRIDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-569-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 569-574 ◽

Cited By ~ 1

Author(s):

S. Y. Hou ◽

Z. Y. Qin ◽

L. Niu ◽

W. G. Zhang ◽

W. T. Ai

Keyword(s):

Cloud Cover ◽

Satellite Image ◽

Geostationary Satellite ◽

Layer By Layer ◽

Matching Accuracy ◽

Data Set ◽

Matching Algorithm ◽

Fourth Level ◽

Multi Level ◽

Landmark Matching

Abstract. The resolution of geostationary satellite image is not high and the image is covered with clouds. At present, when the extracted feature points are unstable, there are some problems, such as low matching accuracy or even matching failure. In this paper, a landmark matching algorithm is proposed to directly establish the multi-level grids for the image coastline and the coastline template. Through the similarity measure of the multi-level grids, the landmark matching is realized layer by layer. First of all, we've finished cloud detection, establishment of landmark data set, and extraction of image coastline. Then we design and implement the landmark matching algorithm based on multi-level grids. Finally, through analysis from different levels of landmarks and different proportion of cloud cover, the advantages and applicable conditions of this algorithm are given. The experimental results show that: 1) with the increase of cloud cover, the correct rate of landmark matching decreases, but the decrease is small. It shows that the matching algorithm in this paper is stable. Correct matching rate could always be stable at about 75 percent in the fourth level. 2) when the proportion of cloud cover is less than 20 percent, the higher the matching level, the higher the matching accuracy. When the cloud cover is more than 20 percent, the matching accuracy in the fourth level is the highest. This algorithm provides a stable method for the landmark matching of geostationary satellite image.

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Robust Diffeomorphic Mapping via Geodesically Controlled Active Shapes

International Journal of Biomedical Imaging ◽

10.1155/2013/205494 ◽

2013 ◽

Vol 2013 ◽

pp. 1-19 ◽

Cited By ~ 11

Author(s):

Daniel J. Tward ◽

Jun Ma ◽

Michael I. Miller ◽

Laurent Younes

Keyword(s):

Numerical Optimization ◽

Gradient Descent ◽

Principal Component ◽

Surface Matching ◽

Imaging Data ◽

Matching Algorithm ◽

Training Samples ◽

Diffeomorphic Metric Mapping ◽

Surface Representations ◽

Landmark Matching

This paper presents recent advances in the use of diffeomorphic active shapes which incorporate the conservation laws of large deformation diffeomorphic metric mapping. The equations of evolution satisfying the conservation law are geodesics under the diffeomorphism metric and therefore termed geodesically controlled diffeomorphic active shapes (GDAS). Our principal application in this paper is on robust diffeomorphic mapping methods based on parameterized surface representations of subcortical template structures. Our parametrization of the GDAS evolution is via the initial momentum representation in the tangent space of the template surface. The dimension of this representation is constrained using principal component analysis generated from training samples. In this work, we seek to use template surfaces to generate segmentations of the hippocampus with three data attachment terms: surface matching, landmark matching, and inside-outside modeling from grayscale T1 MR imaging data. This is formulated as an energy minimization problem, where energy describes shape variability and data attachment accuracy, and we derive a variational solution. A gradient descent strategy is employed in the numerical optimization. For the landmark matching case, we demonstrate the robustness of this algorithm as applied to the workflow of a large neuroanatomical study by comparing to an existing diffeomorphic landmark matching algorithm.

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Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Webology ◽

10.14704/web/v18i2/web18369 ◽

2021 ◽

Vol 18 (2) ◽

pp. 999-1010

Author(s):

Hayder G.A. Altameemi ◽

Ahmed Abdul Azeez Ismael ◽

Raddam Sami Mehsen

Keyword(s):

Edge Detection ◽

Hough Transform ◽

Transformation Method ◽

Fingerprint Recognition ◽

Biometric Identification ◽

Fingerprint Matching ◽

Matching Algorithm ◽

Detection Techniques ◽

Matching Procedure ◽

Prewitt Edge Detection

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

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A NEW APPROACH FOR RECOGNIZING MULTIFONT CHINESE CHARACTERS USED IN A SPECIAL APPLICATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001496000165 ◽

1996 ◽

Vol 10 (03) ◽

pp. 203-222

Author(s):

LING-HWEI CHEN ◽

YEUAN-KUEN LEE

Keyword(s):

Hough Transform ◽

Recognition Rate ◽

Random Model ◽

Chinese Characters ◽

Learning Stage ◽

New Approach ◽

Error Measure ◽

Matching Algorithm ◽

Speed Up ◽

Thinning Process

Many approaches for recognizing all of the 5401 commonly used Chinese characters have been proposed, but they are complex. For some applications such as cheques, receipts, addresses, in Chinese, etc., only a limited set of printed characters is needed, but they are usually require a real time response and a high recognition rate. Based on this reason, here we provide a simpler approach to meet these two requirements. The approach takes horizontal strokes, vertical ones and crossings among these strokes as the character features. A fast modified Hough transform is provided to extract these features, it satisfies an important stroke property that a dotted line must not be a real stroke. In the learning stage, an error measure is defined and an optimal stroke matching algorithm is proposed to establish an accurate random model for each type of characters. In the recognition stage, another error measure is defined and a nearly-optimal matching algorithm is presented to speed up the recognition process. It is worth mentioning that the approach does not use any thinning process. Some experimental results are also provided to show the effectiveness of the proposed recognizer.

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