scholarly journals Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana

2020 ◽  
Vol 12 (2) ◽  
pp. 72-79
Author(s):  
Ismawan Noor Ikhsan ◽  
Son Ali Akbar
Keyword(s):  
Machine Vision ◽  
Color Space ◽  
Region Of Interest ◽  
Contour Detection ◽  
Segmentation Method ◽  
Smart System ◽  
Hsv Color Space ◽  
Aerial Vehicle ◽  
Detection Evaluation ◽  
Flying Robots

Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.

Both Hands’ Fingers’ Angle Calculation from Live Video

2012 ◽  
Vol 2 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Ankit Chaudhary ◽  
Jagdish L. Raheja ◽  
Karen Das ◽  
Shekhar Raheja
Keyword(s):  
Gesture Recognition ◽  
Color Space ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Natural Computing ◽  
Robotic Hand ◽  
Hsv Color Space ◽  
Binary Format ◽  
Novel Method ◽  
Angle Calculation

In the last few years gesture recognition and gesture-based human computer interaction has gained a significant amount of popularity amongst researchers all over the world. It has a number of applications ranging from security to entertainment. Gesture recognition is a form of biometric identification that relies on the data acquired from the gesture depicted by an individual. This data, which can be either two-dimensional or three-dimensional, is compared against a database of individuals or is compared with respective thresholds based on the way of solving the riddle. In this paper, a novel method for angle calculation of both hands’ bended fingers is discussed and its application to a robotic hand control is presented. For the first time, such a study has been conducted in the area of natural computing for calculating angles without using any wired equipment, colors, marker or any device. The system deploys a simple camera and captures images. The pre-processing and segmentation of the region of interest is performed in a HSV color space and a binary format respectively. The technique presented in this paper requires no training for the user to perform the task.

scholarly journals Unmanned Aerial Patrol Technology Based on Tracking Algorithm of Target Tracking

2018 ◽  
Vol 14 (11) ◽  
pp. 160
Author(s):  
Yao Yao ◽  
Qing-le Quan ◽  
Hong-hui Zhang ◽  
Qiong Li
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Target Tracking ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Region Of Interest ◽  
Power Line ◽  
Tracking Algorithm ◽  
Inspection Method ◽  
Aerial Vehicle

<p class="0abstract"><span lang="EN-US">In order to study the power patrol technology of unmanned aerial vehicle, the tracking algorithm was applied. The automatic patrolling of power lines was discussed in terms of algorithms. An unmanned aerial vehicle transmission line inspection method based on machine vision was proposed. The image and video of the unmanned aerial vehicle inspection of the power line had a complex background. By Wiener filtering de-noising and probability density functions, the image clarity was improved. According to the existing tracking techniques and algorithms, a Camshaft target tracking algorithm based on lossless Kalman filter was proposed. The method of non-destructive Kalman filter was adopted to predict the region of interest of power line identification. Using the Camshaft algorithm, the prediction of the window was searched and the size of the window was adjusted. Transmission lines were tracked in real time. The results showed that the restoration effect of the algorithm was obvious. The clarity of the image was improved. It prepared for the extraction and tracking of the future transmission lines. Therefore, the proposed method provides a feasible way for the UAV power line inspection technology based on machine vision.</span></p>

scholarly journals Fingers' Angle Calculation Using Level-Set Method

2014 ◽  
pp. 191-202 ◽  
Author(s):  
Ankit Chaudhary ◽  
Jagdish Lal Raheja ◽  
Karen Das ◽  
Shekhar Raheja
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Color Space ◽  
Region Of Interest ◽  
Natural Computing ◽  
Hsv Color Space ◽  
Binary Format ◽  
The World ◽  
Novel Method ◽  
Angle Calculation

In the current age, use of natural communication in human-computer interaction is a known and well-installed thought. Hand gesture recognition and gesture-based applications have gained a significant amount of popularity amongst people all over the world. They have a number of applications ranging from security to entertainment. These applications generally are real time applications and need fast, accurate communication with machines. On the other end, gesture-based communications have few limitations, but bent finger information is not provided in vision-based techniques. In this chapter, a novel method for fingertip detection and for angle calculation of both hands' bent fingers is discussed. Angle calculation has been done before with sensor-based gloves/devices. This study has been conducted in the context of natural computing for calculating angles without using any wired equipment, colors, marker, or any device. The pre-processing and segmentation of the region of interest is performed in a HSV color space and a binary format, respectively. Fingertips are detected using level-set method and angles are calculated using geometrical analysis. This technique requires no training for the system to perform the task.

HSV color space based pupil position locating method

2014 ◽  
Author(s):  
Peng Cao ◽  
Qijie Zhao ◽  
Dawei Tu ◽  
Hui Shao
Keyword(s):  
Color Space ◽  
Hsv Color Space

FOREGROUND IMAGE EXTRCTION IN THE HSV COLOR SPACE

2010 ◽  
Vol 7 (7) ◽  
pp. 1-4
Author(s):  
Jyh-Yeong Chang ◽  
Jia-Jye Shyu ◽  
Yi-Cheng Luo
Keyword(s):  
Color Space ◽  
Hsv Color Space

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

scholarly journals 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Structured Light ◽  
Color Space ◽  
Reconstruction Technique ◽  
Single Shot ◽  
Line Pattern ◽  
Hsv Color Space ◽  
Phase Map ◽  
Line Segmentation

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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