scholarly journals An Image Processing Based Fungus Detection System for Mangoes

2019 ◽  
Vol 9 (1) ◽  
pp. 3493-3497
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Health Problems ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
High Yield ◽  
Data Set ◽  
Different Color ◽  
The Given

Fruits which grow with high yield in many states of India are rich in proteins. But due to addition of excess pesticides and chemicals intake of these fruits lead to serious health problems. It is necessary to identify the presence of chemical in the fruits before consuming it. In this project we have planned to develop an image processing technique to analyze whether the fruit is free from chemicals and fungus. In our paper, we have implemented MATLAB used as well as fungus present in the fruit. We capture the images of the fruit or we use datasets and train the database with different color-based changes that happen after adding chemicals to the fruit. The enhancement process is carried out in the captured image. Then image is segmented to hit the regions with affected spots in the fruit. K-means method is used to carry out the segmentation process. The input image is compared with the given data set for training to identify the images. In this way unhealthy fruits can be identified and the affected spots in the fruit can be detected.

scholarly journals Image processing technique in the study of cell structures of biomedical data

2019 ◽  
pp. 63-66
Author(s):  
В.В. Ляшенко ◽  
O.A. Кобилін ◽  
O.I. Рязанцев ◽  
I.O. Рязанцев
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Cellular Structures ◽  
Biomedical Data ◽  
Processing Methods ◽  
Additional Information ◽  
Cell Structures ◽  
Different Color

Image processing methods are used in all areas of research. These methods provide additional information, a better understanding of the object that is being studied. Among the areas of using image processing methods, medicine occupies a special place. Biomedical data allow us to assess human health, to identify diseases in the early stages. Images of cellular structures of cytological preparations are one of the examples of biomedical data. Based on image analysis methods, we can isolate various components of cellular structures of cytological preparations. To do this, we apply the methods of wavelet analysis for different color components of the input image. Applying morphological analysis, we can identify individual cellular structures. The results are shown on the example of images of cellular structures of cytological preparations.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals Automatic Foreground Extraction Based on Difference of Gaussian

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yubo Yuan ◽  
Yun Liu ◽  
Guanghui Dai ◽  
Jing Zhang ◽  
Zhihua Chen
Keyword(s):  
Image Data ◽  
Input Image ◽  
Normalized Cut ◽  
Data Set ◽  
Foreground Extraction ◽  
Difference Of Gaussian ◽  
Different Color ◽  
The Given ◽  
Novel Algorithm

A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

Segmentation of White Blood Cell Nucleus Using Active Contour

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Nurhanis Izzati Che Marzuki ◽  
Nasrul Humaimi Mahmood ◽  
Mohd Azhar Abdul Razak
Keyword(s):  
Image Processing ◽  
Blood Cell ◽  
White Blood Cell ◽  
Active Contour ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Contour Method ◽  
Automated Diagnosis ◽  
Parameter Values

Image processing comes with various techniques. It uses a series of framework to transform an input image into an output image. In recent times, image processing technique has been extensively used in medical area. In order to overcome the problems of manual diagnosis in identifying the morphology of blood cells, the automated diagnosis is often used. Manual diagnosis required the observation of blood sample by expert hematologist and pathologist. This method may suffer from the presence of non-standard precision of human visual inspection. Due to this problem, this paper focused on semi-automated diagnosis that used image processing technique to perform the segmentation of the nucleus in white blood cell (WBC). Several image processing techniques are used including the active contour method. The results obtained are based on the parameter values obtained from segmentation process. The parameter value is calculated from the roundness equation. The value of 0.80 can be used to describe as a single leukocyte. 

scholarly journals Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

2019 ◽  
Vol 9 (2) ◽  
pp. 1033-1037
Keyword(s):  
Image Processing ◽  
Urban Areas ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights

Traffic light detection is crucial to decrease the traffic light accidents at intersections and to realize autonomous driving. There are so many existing methods to detect traffic light. However, these approaches have several limitations, such as not function well in complex driving environments. Hence, to overcome such constraints, the traffic light detection for the autonomous vehicle using image processing technique is proposed. The experiments are carried out using 114 scene images that consist of 209 traffic lights with different angles, weather conditions, and distance. An image processing technique, Hough Circle Transform is used in this traffic light detection system with the help of Gaussian blurring and Sobel filter. So, the overall accuracy rate for the proposed algorithm is 75.59%. This system is possible to be used in urban areas or complex environments, whether it is at night or day, and it able to detect the traffic light regardless of the colour changes.

scholarly journals A New Dataset method for Biomechanical Training Model of the Free Throws Shots in Basketball Using Image Processing Technique

2019 ◽  
Vol 13 (2) ◽  
pp. 132
Author(s):  
Sumaia Saraireh ◽  
Ahmad Hassanat ◽  
Mohammad Abu Al-Taieb ◽  
Hashem A Kilani
Keyword(s):  
Image Processing ◽  
Video Processing ◽  
Sagittal Plane ◽  
Training Model ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Data Set ◽  
Free Throw ◽  
Basketball Game ◽  
Processing Techniques

This work provides a new dataset method intended to build a biomechanical training model for the free-throws shots in basketball. Eight youth players from Jordanian secondary public school were video recorded from the sagittal plane executing free throw shots in basketball. Collectively (480) video clips were recorded and analyzed using image processing techniques to identify the ball track. Video processing involves extracting (11) different parameters that may affect the free throw in basketball game after detecting the ball trajectory. Creation of this dataset and its subsequent use for extracting free-throws information yielded several insights. First, a set of most important features were identified as those affecting the free-throws score in basketball. Second, our data set can be trained and tested using machine learning classifiers for building a new biomechanical training model based on set of rules that can be useful for both trainers and trainee to rehearse on successful free-throws in basketball. The dataset is being made publicly available at www.ju.edu.jo.

Sign in / Sign up

Export Citation Format

Share Document