scholarly journals Image Processing Technologies for Security. Face and Feature Detection Using Skin Color and Motion.

1998 ◽  
Vol 52 (12) ◽  
pp. 1840-1847 ◽  
Author(s):  
Hitoshi Hongo ◽  
Kazuhiko Yamamoto
Keyword(s):  
Image Processing ◽  
Skin Color ◽  
Feature Detection ◽  
Processing Technologies

scholarly journals Development and Application of a Human–Machine Interface Using Head Control and Flexible Numeric Tables for the Severely Disabled

2020 ◽  
Vol 10 (19) ◽  
pp. 7005
Author(s):  
Che-Ming Chang ◽  
Chern-Sheng Lin ◽  
Wei-Cheng Chen ◽  
Chung-Ting Chen ◽  
Yu-Liang Hsu
Keyword(s):  
Image Processing ◽  
Skin Color ◽  
Digital Camera ◽  
Human Machine Interface ◽  
Head Motion ◽  
Processing Technologies ◽  
Color Recognition ◽  
Machine Interface ◽  
Head Control ◽  
Head Gestures

The human–machine interface with head control can be applied in many domains. This technology has the valuable application of helping people who cannot use their hands, enabling them to use a computer or speak. This study combines several image processing and computer vision technologies, a digital camera, and software to develop the following system: image processing technologies are adopted to capture the features of head motion; the recognized head gestures include forward, upward, downward, leftward, rightward, right-upper, right-lower, left-upper, and left-lower; corresponding sound modules are used so that patients can communicate with others through a phonetic system and numeric tables. Innovative skin color recognition technology can obtain head features in images. The barycenter of pixels in the feature area is then quickly calculated, and the offset of the barycenter is observed to judge the direction of head motion. This architecture can substantially reduce the distraction of non-targeted objects and enhance the accuracy of systematic judgment.

A Hair Volume Analysis System Using Deep Learning and Image Processing Technologies

2021 ◽  
Author(s):  
Ming-Che Chen ◽  
Wan-Jung Chang ◽  
Yu-Xiang Xiao ◽  
Zi-Xuan Zhang ◽  
Yi-Chan Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Processing Technologies ◽  
Volume Analysis ◽  
Analysis System

Classification of Fresh N36 Pineapple Crop Using Image Processing Technique

2011 ◽  
Vol 418-420 ◽  
pp. 1739-1743 ◽  
Author(s):  
Shuhairie Mohammad ◽  
Kamarul Hawari Ghazali ◽  
Nazriyah Che Zan ◽  
Siti Sofiah Mohd Radzi ◽  
Rohana Abdul Karim
Keyword(s):  
Image Processing ◽  
Skin Color ◽  
Promising Result ◽  
Linear Method ◽  
Processing Technique ◽  
Maturity Index ◽  
Image Processing Technique ◽  
Classification Rate ◽  
Testing Stage ◽  
The Government

Malaysia is one of the world pineapple producers besides Thailand, Philippine, Indonesia, Brazil and South Africa. The government encourage farmers to have more production to meet increasing demand for export. Most of the pineapple production activities is still in manual process and rely on labor workers. In this paper, we proposed a system that can be used in production house to automatically detect the maturity index of pineapple. We implement image processing method to determine the maturity of a pineapple based on yellowish skin color. Binary ellipse mask has been used for extracting region of interest (ROI) as well as morphology normalized RGB to filter out the background and unwanted pixel image. Finally, linear method using threshold values has been selected to classify the maturity index. 910 pineapple images has been used at the development and testing stage and we obtained promising result with 94.29% good classification rate.

Facial Image Processing in Computer Vision

2013 ◽  
pp. 1111-1123
Author(s):  
Moi Hoon Yap ◽  
Hassan Ugail
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Animation ◽  
Feature Detection ◽  
Research Field ◽  
Future Research ◽  
Expression Recognition ◽  
Facial Image ◽  
Detection Techniques ◽  
Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

Study on Soil Particle Size Distribution by Digital Image Processing

2013 ◽  
Vol 788 ◽  
pp. 627-630
Author(s):  
Jian Shu Hou
Keyword(s):  
Mechanical Property ◽  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Digital Image ◽  
Traditional Method ◽  
Processing Method ◽  
Distribution Analysis ◽  
Processing Technologies

The particle size distribution of soil is very importantto its physical and mechanical property. The ordinary method of the particlesize distribution analysis is based on shaking the soil through a set of sieves.But it will be difficult to use the method while there have particles largerthan the biggest aperture of the size sieves. Then the digital image processingwas used to solve the problem here. The processing technologies, such as imageanalysis and enhancement, deblurring, edge detection were studied to analyzethe image of soil particles. Then the image processing method was used to getthe particle size distribution accurately. Though some promotions need to becarried out in the further study, it is can be found that the image processingmethod is more efficiently than the traditional method.

scholarly journals Feature detection of mitral valve based on image processing technique: A review

2019 ◽  
Vol 1372 ◽  
pp. 012071
Author(s):  
Lina Farhana Mahadi ◽  
Nabilah Ibrahim ◽  
Mohd Thariq Zaluwi ◽  
Muhammad Haniff S.M. Johan
Keyword(s):  
Image Processing ◽  
Mitral Valve ◽  
Feature Detection ◽  
Processing Technique ◽  
Image Processing Technique

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

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