scholarly journals Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 30-42
Author(s):  
Oladimeji Adeyemi ◽  
Martins Irhebhude ◽  
Adeola Kolawole
Keyword(s):  
Image Processing ◽  
Image Processing Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Stage ◽  
Image Processing Techniques ◽  
Stop Sign ◽  
Processing Techniques ◽  
Road Marking ◽  
Detection And Recognition

This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

scholarly journals A new approach for breast abnormality detection based on thermography

2018 ◽  
Vol 2 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Chebbah Nabil Karim ◽  
Ouslim Mohamed ◽  
Temmar Ryad
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormality Detection ◽  
Public Database ◽  
New Approach ◽  
Image Processing Techniques ◽  
Breast Abnormality ◽  
Processing Techniques

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method  showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.

scholarly journals Specifics of Using Image Processing Techniques for Blood Smear Analysis

2020 ◽  
Vol 1 (6) ◽  
pp. 1-6
Author(s):  
Vyacheslav Lyashenko ◽  
Tetiana Sinelnikova ◽  
Oleksandr Zeleniy ◽  
Asaad Mohammed Ahmed Babker
Keyword(s):  
Image Processing ◽  
Human Health ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Important Stage ◽  
Blood Smears ◽  
Processing Techniques

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

scholarly journals Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

2020 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Dr. Dhaya R.
Keyword(s):  
Image Processing ◽  
Virtual Reality ◽  
Processing Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Processing Techniques

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

2021 ◽  
pp. 103-115
Author(s):  
Eimad Abdu Abusham
Keyword(s):  
Image Processing ◽  
Traditional Method ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Economic Losses ◽  
Image Processing Techniques ◽  
Appropriate Treatment ◽  
Processing Techniques

Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.

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