Application of thresholding algorithms for brown bread porosity evaluation

2019 ◽  
Vol 2 (2) ◽  
pp. 99
Author(s):  
Angel Danev ◽  
Atanaska Bosakova-Ardenska ◽  
Miroslav Apostolov
Keyword(s):  
Image Processing ◽  
Threshold Value ◽  
Physicochemical Parameter ◽  
Fast Evaluation ◽  
Real Time Processing ◽  
Global Threshold ◽  
Java Program ◽  
Computer Based ◽  
Brown Bread ◽  
Processing Techniques

The bread is one of the most popular foods in Bulgaria. It’s quality is regulated by approved standards. This paper presents a computer based approach for evaluation of bread porosity which is one of physicochemical parameters of bread quality. The proposed approach includes image processing techniques. A Java program is developed to binarize images of bread and calculate ratio of white pixels to all (coefficient of diversity). This coefficient corresponds with physicochemical parameter- bread porosity. It is used an open-source plugin Auto_Threshold for image binarization. This plugin implements seventeen different algorithms to find global threshold value of a grayscale image. The results show that global thresholding is appropriate for evaluation of bread porosity. The correlation analysis shows that algorithm HisAnalysis could be used for fast and effective evaluation of bread porosity using image processing. Practical applicationsThe use of image processing accelerate the process of bread porosity evaluation. Presented research proves practical benefit to apply image processing for evaluation of physicochemical parameter- bread porosity. The results show that seven algorithms which are included in Auto_Threshold plugin and HisAnalysis algorithm are suitable for bread porosity evaluation. The fastest algorithm is HisAnalysis and it could be used in practice for fast evaluation (in real-time processing) of physicochemical parameter- bread porosity.

scholarly journals Development of Conventional Controller Based on Image Processing for Monitoring and Controlling Burning Zone Temperature in a Cement Plant in Rotary Kiln Process Through IOT

2021 ◽  
Vol 20 (4) ◽  
pp. 209-214
Author(s):  
Polaiah Bojja ◽  
N. Merrin Prasanna ◽  
Pamula Raja Kumari ◽  
T. Bhuvanendhiran ◽  
Panuganti Jayanth Kumar
Keyword(s):  
Image Processing ◽  
Rotary Kiln ◽  
Continuous Process ◽  
Color Space ◽  
Threshold Value ◽  
Cement Industry ◽  
Raspberry Pi ◽  
Python Language ◽  
Burning Zone ◽  
Processing Techniques

In the cement factories, a rotary kiln is a pyro-processing device that is used to raise the temperature of the materials in a continuous process. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker and it has been implemented using various image processing techniques. In this paper we are measuring and controlling the temperature of rotational kiln in cement industry to get proper clinker ouput. Burning zone flame images are captured using CCD(Charge Coupled Device) camera and are processed using image processing with PID(Proportion Integration and Derivative) controller and which are programmed on raspberry pi card with the help of python language, also the captured images and attributes are transferred to authorized mobile/pc through Raspberry PI by selecting the IP address of mobile or PC. All the attributes received in the mobile in the form of web page the according to the object following data temperature controlled and object is ceaselessly followed to get the proper clinker output. Picture handling calculation with Open cv, as indicated by the calculation the edge estimation of the camera is settled. The frame value of the camera is set. Conversion from RGB color space to HSV color space is achieved and the reference color threshold value is determined. The range esteem estimated by the camera is contrasted and the reference esteem. In this study temp of rotational kiln is measured effectively using PID controller, this controller continuously control the temperature of revolving kiln by varying the i/p images of burning zone at finally fix one flame which is giving 1400degc.

scholarly journals De-Noising of Uterus Fibroid Ultrasound Image Using Gaussismooth Convolution Filter (GSCF)

2020 ◽  
Vol 5 (7) ◽  
pp. 1033-1037
Author(s):  
Dr. M. Renuka Devi ◽  
V. Sindhu
Keyword(s):  
Image Processing ◽  
Imaging Techniques ◽  
Ultrasound Image ◽  
Cost Effective ◽  
Initial Step ◽  
Threshold Value ◽  
Input Image ◽  
Local Maxima ◽  
Convolution Filter ◽  
Processing Techniques

This paper discusses the methods to detect the presence of uterus fibroid in woman by implementing various image processing techniques. The input image is an ultrasound image as it is cost effective when compared to other imaging techniques like CT, MRI. The initial step in image processing is to remove noise by applying filters. Application of filters smoothen the image without blurring the image. Gradient of the processed image is calculated and the image is enhanced by sharpening the edges of the image are achieved by calculating the local maxima of the gradient. Then, the edges are decided by calculating the threshold value of the processed image. The proposed Gaussismooth Convolution Filter gives better results when compared with other existing filter with PSNR value of 94%.

scholarly journals Automatic recognition of pulse crops using image processing

2015 ◽  
Vol 2 (2) ◽  
pp. 215-220
Author(s):  
Muhammad Mustagis Billah ◽  
Muhammad Ashik E Rabbani ◽  
Bu Tayeb Muhammad Alimuzzaman
Keyword(s):  
Image Processing ◽  
Automatic Recognition ◽  
Success Rates ◽  
Chick Pea ◽  
Pulse Crops ◽  
Pulse Crop ◽  
Texture Characteristics ◽  
Computer Based ◽  
Intensity Images ◽  
Processing Techniques

The present study was explored the feasibility of implementing fast and reliable computer-based systems for the automatic recognition of pulse crops from color and gray intensity images. Pulse crop’s size, shape, color and texture characteristics are obtained by standard image-processing techniques and their discriminating power as classification features was assessed. These investigations were performed on a database containing 102 images of most common four pulse crop’s that were Lentil, Ground Nut, Chick-pea and Split-pea. Each image contains approximately 15-20 pulses of same and mix varieties together and considers the implementation of a simple RGB and gray color model for recognition. The results indicate that classifier based on an adequately selected set of classification features has an excellent performance. The success rates of Lentil, Ground Nut, Chick-pea and Split-pea were 90.02%, 90.33%, 91.96% and 83.58%, respectively. In addition, the recognition gave highest percentages using distinct characteristics as classification features.Res. Agric., Livest. Fish.2(2): 215-220, August 2015

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Specific Labeling of Protein Domains with Antibody Fragments

1981 ◽  
Vol 39 ◽  
pp. 416-417
Author(s):  
U. Aebi ◽  
L.E. Buhle ◽  
W.E. Fowler
Keyword(s):  
Image Processing ◽  
Specific Protein ◽  
Antibody Fragments ◽  
Supramolecular Organization ◽  
Two Dimensional ◽  
Ordered Structures ◽  
Virus Capsids ◽  
Processing Techniques

Many important supramolecular structures such as filaments, microtubules, virus capsids and certain membrane proteins and bacterial cell walls exist as ordered polymers or two-dimensional crystalline arrays in vivo. In several instances it has been possible to induce soluble proteins to form ordered polymers or two-dimensional crystalline arrays in vitro. In both cases a combination of electron microscopy of negatively stained specimens with analog or digital image processing techniques has proven extremely useful for elucidating the molecular and supramolecular organization of the constituent proteins. However from the reconstructed stain exclusion patterns it is often difficult to identify distinct stain excluding regions with specific protein subunits. To this end it has been demonstrated that in some cases this ambiguity can be resolved by a combination of stoichiometric labeling of the ordered structures with subunit-specific antibody fragments (e.g. Fab) and image processing of the electron micrographs recorded from labeled and unlabeled structures.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques

Identification of Cucumber Leaf Disease using Image Processing Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 1493-1499
Author(s):  
Shrutika.C.Rampure . ◽  
Dr. Vindhya .P. Malagi ◽  
Dr. Ramesh Babu D.R
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Leaf Disease ◽  
Processing Techniques
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