scholarly journals A System Model for Analyzing and Accumulating Construction Work Crew′s Productivity Data Using Image Processing Technologies

2014 ◽  
Vol 13 (3) ◽  
pp. 547-554 ◽  
Author(s):  
Chankyu Lee ◽  
Jaeho Son ◽  
Seunghyun Lee
Keyword(s):  
Image Processing ◽  
System Model ◽  
Construction Work ◽  
Processing Technologies ◽  
Productivity Data

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

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 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.

AUTOMATED CLASSIFICATION OF BREAST CARCINOMA CELL BASED ON IMAGE PROCESSING AND SUPPORT VECTOR MACHINE

2010 ◽  
Vol 22 (02) ◽  
pp. 127-135 ◽  
Author(s):  
Yung-Lung Kuo ◽  
Chien-Chuan Ko ◽  
Yueh-Min Lin ◽  
Yong-Min Chen
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Automated Classification ◽  
Processing Technologies ◽  
Major Health ◽  
Suitable Treatment ◽  
Breast Tissues

As breast cancer is a substantial threat to the lives of women, it has become a major health issue in the world over the past 50 years, and its incidence has increased in the recent years. Early diagnosis and suitable treatment is relatively important. In the process of breast screening, tissue biopsy is an important operation in determining the presence of breast cancer. It not only provides an accurate diagnosis of the disease but also determines the prognosis for breast cancer. The main goal of this study is to develop a breast cancer diagnosis system based on histopathology and a sequence of image-processing technologies to analyze H&E stained images of breast tissues. The proposed system can automatically detect the mitosis of nuclei and analyze the size and the shape of nuclei to evaluate the duct structure of the breast tissue. Moreover, it provides physicians quantitative prognosis and classification of tissue malignancy, which will improve the diagnostic accuracy and efficiency of the cancer.

scholarly journals Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

2020 ◽  
Vol 37 (4) ◽  
pp. 619-626
Author(s):  
Shizhen Bai ◽  
Fuli Han
Keyword(s):  
Image Processing ◽  
Image Retrieval ◽  
Visual Information ◽  
Feature Matching ◽  
Scale Invariant ◽  
Behavior Recognition ◽  
Processing Technologies ◽  
Tourist Behavior ◽  
Scenic Spot ◽  
Spot Image

The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.

scholarly journals Imaging technologies to study the composition of live pigs: A review

2016 ◽  
Vol 14 (3) ◽  
pp. e06R01 ◽  
Author(s):  
Anna Carabús ◽  
Marina Gispert ◽  
Maria Font-i-Furnols
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Image Processing ◽  
Magnetic Resonance ◽  
Visual Image ◽  
Dual Energy ◽  
Resonance Imaging ◽  
Processing Technologies ◽  
X Ray ◽  
Advantages And Disadvantages

Image techniques are increasingly being applied to livestock animals. This paper overviews recent advances in image processing analysis for live pigs, including ultrasound, visual image analysis by monitoring, dual-energy X-ray absorptiometry, magnetic resonance imaging and computed tomography. The methodology for live pigs evaluation, advantages and disadvantages of different devices, the variables and measurements analysed, the predictions obtained using these measurements and their accuracy are discussed in the present paper. Utilities of these technologies for livestock purposes are also reviewed. Computed tomography and magnetic resonance imaging yield useful results for the estimation of the amount of fat and lean mass either in live pigs or in carcasses. Ultrasound is not sufficiently accurate when high precision in estimating pig body composition is necessary but can provide useful information in agriculture to classify pigs for breeding purposes or before slaughter. Improvements in factors, such as the speed of scanning, cost and image accuracy and processing, would advance the application of image processing technologies in livestock animals.

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