scholarly journals Cut surface of Bulgarian white brined cheese evaluation by image analysis in HSI color space

2021 ◽  
Vol 1031 (1) ◽  
pp. 012114
Author(s):  
A Bosakova-Ardenska ◽  
H Andreeva ◽  
A Danev ◽  
P Panayotov ◽  
P Boyanova
Keyword(s):  
Image Analysis ◽  
Color Space ◽  
Hsi Color Space ◽  
Cut Surface

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

A method for measuring the residence time distribution of particles in a fluidized bed based on digital image analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jiamin Li ◽  
Xiaoping Chen ◽  
Jiliang Ma ◽  
Cai Liang
Keyword(s):  
Image Analysis ◽  
Fluidized Bed ◽  
Residence Time ◽  
Digital Image ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Digital Image Analysis ◽  
Color Space ◽  
The Difference ◽  
Accuracy And Repeatability

AbstractTraditional methods for measuring the residence time distribution (RTD) of particles in a fluidized bed are complex and time-consuming. To this regard, the present work proposes a new measurement method with remarkable efficiency based on digital image analysis. The dyed tracers are recognized in the images of the samples due to the difference of colors from bed materials. The HSV and the well-known RGB color space were employed to distinguish the tracers. By enhancing the Saturation and the Value in HSV and adjusting the gray range of images, the recognition error is effectively reduced. Then the pixels representing the tracers are distinguished, based on which the concentration of the tracers and RTD are measured. The efficiency, accuracy and repeatability of the method were validated by RTD measurements experiments. The method is also fit for distinguishing the target particles from multi-component systems consisting of particles of different colors.

scholarly journals Multichannel Correlation Clustering Target Detection

2020 ◽  
Vol 49 (3) ◽  
pp. 335-345
Author(s):  
Yan Xu ◽  
Jiangtao Dong ◽  
Zishuo Han ◽  
Peiguang Wang
Keyword(s):  
Color Space ◽  
Training Model ◽  
Gaussian Mixture ◽  
Time Correlation ◽  
Model Parameters ◽  
Time Dimension ◽  
Dynamic Background ◽  
Correlation Clustering ◽  
Hsi Color Space ◽  
Real Time Computing

During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.

scholarly journals Dehazing in HSI Color Space with Color Correction

2013 ◽  
Vol 18 (2) ◽  
pp. 140-148 ◽  
Author(s):  
Taeha Um ◽  
Wonha Kim
Keyword(s):  
Color Space ◽  
Color Correction ◽  
Hsi Color Space

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

scholarly journals Variational Histogram Equalization for Single Color Image Defogging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Li Zhou ◽  
Du Yan Bi ◽  
Lin Yuan He
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Histogram Equalization ◽  
Degradation Model ◽  
Image Defogging ◽  
Hsi Color Space ◽  
General Variation ◽  
Bad Weather ◽  
Physical Degradation

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

Video Vehicle Detection Method Based on Multiple Color Space Information Fusion

2012 ◽  
Vol 546-547 ◽  
pp. 721-726
Author(s):  
Hong Xiang Shao ◽  
Xiao Ming Duan
Keyword(s):  
Information Fusion ◽  
Traditional Method ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Data Accuracy ◽  
Traffic Information ◽  
Detection Accuracy ◽  
Relative Independence ◽  
Hsi Color Space

A detection method which selective fuses the nine detection results of RGB, YCbCr and HSI color space according to the image color space relative independence of each component and complementarities is approached in order to improve vehicle video detection accuracy. The method fuses three different detection results in nine components by the value of H when the value of both S and I are higher and does another three detection results when the value of both S and I are smaller. Experiments show that the method compared to the traditional method using only the detection results of the brightness component improved substantial, reduced empty of the detected vehicle a large extent and increased traffic information data accuracy depending on the detection result.

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