Nonparametric Bayesian Line Detection - Towards Proper Priors for Robotic Computer Vision

2016 ◽  
Author(s):  
Anne C. van Rossum ◽  
Hai Xiang Lin ◽  
Johan Dubbeldam ◽  
H. Jaap van den Herik
Keyword(s):  
Computer Vision ◽  
Line Detection ◽  
Nonparametric Bayesian ◽  
Proper Priors

MEASURING CHARACTERISTIC PARAMETERS OF FORM GRINDING WHEELS USED FOR MICRODRILL FLUTING BY COMPUTER VISION

2011 ◽  
Vol 35 (3) ◽  
pp. 383-401 ◽  
Author(s):  
Wen-Tung Chang ◽  
Ting-Hsuan Chen ◽  
Yeong-Shin Tarng
Keyword(s):  
Computer Vision ◽  
Line Detection ◽  
Measuring Apparatus ◽  
Characteristic Parameters ◽  
Grinding Wheels ◽  
Measuring Process ◽  
Circular Arc ◽  
Form Grinding ◽  
Straight Line ◽  
Straight Line Detection

This study aims at measuring the characteristic parameters of form grinding wheels used for microdrill fluting, whose wheel contours are specially made up of combinations of multiple curves. With the aid of the indirect duplication of wheel contours and by using computer vision, this paper presents a systematic process for the wheel contour measurement. The measuring process includes five sequential steps: the edge detection, the straight line detection, the contour separation, the circular arc fitting, and the circular arc angle evaluation. To test the proposed measuring process, a measuring apparatus was built, and experiments measuring the characteristic parameters of diamond grinding wheels used for microdrill fluting were conducted. It showed that the proposed measuring process was feasible to measure the characteristic parameters of certain form grinding wheels used for microdrill fluting.

Angle Measurement Based on Computer Vision

2013 ◽  
Vol 456 ◽  
pp. 115-119
Author(s):  
Jun Wang ◽  
Xiao Hua Ni
Keyword(s):  
Computer Vision ◽  
Traditional Method ◽  
High Accuracy ◽  
Angle Measurement ◽  
Line Detection ◽  
Simulation Experiments ◽  
Image Preprocessing ◽  
Testing Technology ◽  
Canny Algorithm ◽  
Angle Calculation

In order to improve the precision and the speed of angle measurement,A new method for measuring the angle of the workpiece is presented in this paper, which is based on the computer vision testing technology. The image of workpiece is obtained, the first step is image preprocessing, then the measured worpiece image is processed by edge detection through Canny algorithm, specific features of workpieces edge is fully extracted, Then one can accomplish line detection by using Hough transform, Finally, the angle value is obtained through the means of Angle Calculation. By employing practical examples in engineering and simulation experiments, the experimental results proved the method has more strong anti-interference ability, more high accuracy and speed than traditional method.

Fundamentals of Nonparametric Bayesian Line Detection

2017 ◽  
pp. 175-193
Author(s):  
Anne C. van Rossum ◽  
Hai Xiang Lin ◽  
Johan Dubbeldam ◽  
H. Jaap van den Herik
Keyword(s):  
Line Detection ◽  
Nonparametric Bayesian

An Improved Hough Transform Algorithm Based on Pyramid Method

2014 ◽  
Vol 543-547 ◽  
pp. 1917-1921
Author(s):  
Long Ren ◽  
Jia Wen Liao ◽  
Jian Zhong Cao ◽  
Hua Wang ◽  
Xiao Dong Zhao ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Standard Method ◽  
Hough Transform ◽  
Computational Time ◽  
Line Detection ◽  
Computation Complexity ◽  
Transform Method ◽  
Straight Line ◽  
Algorithm Base

Hough Transform[has become a common method in the usage of line detection because of its robustness. It is important in computer vision and image analysis. Usually, the standard Hough transform method (SHT) transform the points in image space into parameter space and vote for all the possible patterns passing through that point. But, there are two serious problems in the standard method of line detection. The first is the high computation complexity and the second is the large storage requirements .In order to solve the two problems, this paper raise a fast-Hough transform algorithm base on pyramid algorithm. First of all we need to desample the primitive binary image with n times; and execute the Hough transform in the nth level image to get the parameter of straight line in this image, which is used in the n-1 level image. Finally we can get the parameter of lines in the primitive image. Experiments show that this method can extremely reduces the computational time.

scholarly journals Detection of nearby UAVs using a multi-microphone array on board a UAV

2020 ◽  
Vol 12 ◽  
pp. 175682932092574
Author(s):  
Aldrich A Cabrera-Ponce ◽  
J Martinez-Carranza ◽  
Caleb Rascon
Keyword(s):  
Computer Vision ◽  
Visual Processing ◽  
Microphone Array ◽  
False Positives ◽  
Image Resolution ◽  
Line Detection ◽  
Light Conditions ◽  
Audio Signals ◽  
On Line

In this work, we address the problem of UAV detection flying nearby another UAV. Usually, computer vision could be used to face this problem by placing cameras onboard the patrolling UAV. However, visual processing is prone to false positives, sensible to light conditions and potentially slow if the image resolution is high. Thus, we propose to carry out the detection by using an array of microphones mounted with a special array onboard the patrolling UAV. To achieve our goal, we convert audio signals into spectrograms and used them in combination with a CNN architecture that has been trained to learn when a UAV is flying nearby, and when it is not. Clearly, the first challenge is the presence of ego-noise derived from the patrolling UAV itself through its propellers and motor’s noise. Our proposed CNN is based on Google’s Inception v.3 network. The Inception model is trained with a dataset created by us, which includes examples of when an intruder UAV flies nearby and when it does not. We conducted experiments for off-line and on-line detection. For the latter, we manage to generate spectrograms from the audio stream and process it with the Nvidia Jetson TX2 mounted onboard the patrolling UAV.

Sign in / Sign up

Export Citation Format

Share Document