An Improved Randomized Hough Transform Method for the Cotton Recognition

2012 ◽  
Vol 189 ◽  
pp. 383-387
Author(s):  
Kun Liu ◽  
Shu Min Fei ◽  
Mu Lan Wang
Keyword(s):  
Hough Transform ◽  
Mathematic Model ◽  
Detection Technique ◽  
Transform Method ◽  
Model Based ◽  
Novel Approach ◽  
Randomized Hough Transform ◽  
Boundary Trace ◽  
Contour Information ◽  
Harvesting Robot

The cotton recognition would become rather difficult during the application of the cotton harvesting robot (CHR) in the case where the cotton is sheltered or covered by other objects. In this paper, a novel approach of cotton detection based on the improved randomized Hough transform (IRHT) for this case is proposed. Based on the contour information from the boundary trace, the mathematic model-based IRHT is derived based on a modified circular detection technique. It yields a well agreement with the requirements of the precision and rating of CHR.

scholarly journals Eyelids, eyelashes detection algorithm and houghtransform method for noise removal in iris recognition

2020 ◽  
Vol 18 (2) ◽  
pp. 731
Author(s):  
Bounegta Nadia ◽  
Bassou Abdessalam ◽  
Beladgham Mohamed
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Iris Recognition ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Detection Technique ◽  
Biometric System ◽  
Transform Method ◽  
Iris Image ◽  
Identical Feature

<p><span>The biometric system is based on human’s behavioral and physical characteristics. Among all of these, iris has unique structure, higher accuracy and it can remain stable over a person’s life. Iris recognition is the method by which system recognize a person by their unique identical feature found in the iris. Iris recognition technology includes four subsections as, capturing of the iris image, segmentation, extraction of the needed features and matching. This paper is a detail description of eyelids; eyelashes detection technique and Hough transform method applied on iris image. </span></p>

scholarly journals Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 885
Author(s):  
Yoanda Alim Syahbana ◽  
Yokota Yasunari ◽  
Morita Hiroyuki ◽  
Aoki Mitsuhiro ◽  
Suzuki Kanade ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Hough Transform ◽  
University Hospital ◽  
Transform Method ◽  
Data Set ◽  
Medical Specialists ◽  
Detection And Tracking ◽  
Mexican Hat ◽  
Pupil Detection ◽  
In The Wild

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach

A Mathematic Model Based on Unstructured Mesh for Simulating Ice Accretion on Aircraft

2019 ◽  
Author(s):  
Ningli Chen ◽  
Yaping Hu ◽  
Honghu Ji ◽  
Yongqing Yuan ◽  
Guangzhou Cao
Keyword(s):  
Unstructured Mesh ◽  
Mathematic Model ◽  
Ice Accretion ◽  
Model Based

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

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