scholarly journals Quantum-Assisted Retinal Drusen Detection Algorithm using Entropy-Based Image Processing Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 2251-2256
Keyword(s):  
Image Processing ◽  
Composite System ◽  
Detection Method ◽  
Research Work ◽  
Detection Algorithm ◽  
Image Processing Techniques ◽  
Sensitivity Specificity ◽  
Processing Techniques ◽  
Retinal Fundus ◽  
Retinal Drusen

Drusen identification is the fundamental operation in the automated diagnosis of eye diseases. Manual and automatic detection of the drusen in the retinal fundus images has been developed recently in the classical manner only. This work provides the quantum-based retinal drusen detection method using entropy-based image processing techniques. This algorithm is the composite system of two channels, classical and quantum channels for the preprocessing and drusen detection respectively. This research work has been evaluated with the databases of DRIVE, STARE, MESSIDOR, E-Optha-Ex and ONH-Hunter. This quantum-based approach will be analyzed with the results of the existing classical methods and proves its efficiency from the calculations of sensitivity, specificity, accuracy and execution time.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

HUMAN FACE IMAGE PROCESSING TECHNIQUES

2001 ◽  
Vol 01 (02) ◽  
pp. 197-215 ◽  
Author(s):  
HONG YAN
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Facial Feature ◽  
Face Image ◽  
Computer Algorithms ◽  
Human Face ◽  
Feature Location ◽  
Image Processing Techniques ◽  
Face Image Processing ◽  
Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

scholarly journals Analysis of Buffer Zones of Limestone Mines Areas using Digital Image Processing Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5431-5437
Keyword(s):  
Image Processing ◽  
Land Use ◽  
Digital Image Processing ◽  
Digital Image ◽  
Land Use Changes ◽  
Research Work ◽  
Mining Activities ◽  
Buffer Zones ◽  
Image Processing Techniques ◽  
Processing Techniques

The economic growth of any country crucially depends on the mining activity of that country. The mining activities require huge land for the extraction of mineral from the earth. The recent government policy imposing the systematic mapping of the land use and land cover in and around the mines. In the present study, work, the analysis of land used and land covered was carried out at Malkapur limestone mines. This study discussed the brief mapping of the buffer zones buffer zones areas in by using digital image processing techniques. This research work demonstrated the changes happened in and around mines for the buffer radius of 1 km, 5 km and 10 km. In this study it was found that there were no significant changes observed in land use which intern implies that mining activities are not having any impact in land use changes. Further, in this study, not much variation was reported against the forest land and water bodies situated in and around the mines

scholarly journals Detecção de Vagas em Estacionamento Usando Visão Computacional e Redes Neurais Convolucionais

2020 ◽  
Author(s):  
Caroline Mazetto Mendes ◽  
Willian Marrion Cavenagli
Keyword(s):  
Image Processing ◽  
Detection Algorithm ◽  
Parking Space ◽  
Urban Mobility ◽  
Parking Lots ◽  
Image Processing Techniques ◽  
Mobility Problem ◽  
Processing Techniques

Parking lots are no longer practical solutions but become anothertopic of urban mobility problem due to the difficulty in finding availableparking spaces. This work proposes a parking space detectionsystem to assist drivers. The system detects unoccupied vacanciesby image processing techniques and convolutional neural networks.Vacancies are detected through horizontal markings and by recognizingspaces with or without vehicles. Finally, a mobile applicationmakes available to the user the occupancy status of vacancies. Initialresults showed that the system detects vacancies with visiblemarkings during the daytime. To improve detection in adversesituations, the vacancy detection algorithm is being improved.

scholarly journals Detection and Classification of Rice Diseases: An Automated Approach Using Textural Features

2019 ◽  
Vol 38 (1) ◽  
pp. 239-250 ◽  
Author(s):  
Komal Bashir ◽  
Mariam Rehman ◽  
Mehwish Bari
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Recall Rate ◽  
Misclassification Rate ◽  
Maximum Efficiency ◽  
Support Vector ◽  
Image Processing Techniques ◽  
Effective Decision Making ◽  
Processing Techniques

Image processing techniques are widely used for the detection and classification of diseases for various plants. The structure of the plant and appearance of the disease on the plant pose a challenge for image processing. This research implements SVM (Support Vector Machine) based image-processing approach to analyze and classify three of the rice crop diseases. The process consists of two phases, i.e. training phase and disease prediction phase. The approach identifies disease on the leaf using trained classifier. The proposed research work optimizes SVM parameters (gamma, nu) for maximum efficiency. The results show that the proposed approach achieved 94.16% accuracy with 5.83% misclassification rate, 91.6% recall rate and 90.9% precision. These findings were compared with image processing techniques discussed in review of literature. The results of comparison conclude that the proposed methodology yields high accuracy percentage as compared to the other techniques. The results obtained can help the development of an effective software solution by incorporating image processing and collaboration features. This may facilitate the farmers and other bodies in effective decision making to efficiently protect the rice crops from substantial damage. While considering the findings of this research, the presented technique may be considered as a potential solution for adding image processing techniques to KM (Knowledge Management) systems.

scholarly journals Auto_Bot for Fire Detection and Extinguishion

2019 ◽  
Vol 8 (4) ◽  
pp. 5224-5226
Keyword(s):  
Image Processing ◽  
Mobile Robot ◽  
Temperature Sensor ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Smoke Detector ◽  
Set Point ◽  
Image Processing Techniques ◽  
Processing Techniques

In this paper, we proposed a fire detection algorithm to detect fire based on image processing techniques. This is compatible in surveillance device like CCTV, wireless camera. Video - Based Fire Detection are not mobilised and autonomous. The camera is turnedON only when the sensors reach a particular set point from temperature sensor and smoke detector. The captured video is converted to frames and image processing is done to identify the fire by means of its unique characteristics like Color, Motion and Flickering of flamesas these features are powerful discriminants. Suitable image processing techniques are applied to detect the fire.If the fire is identified, a mobile robot consists of water hoses is actuated to put off the fire

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