scholarly journals Vehicle plate number localization using a modified GrabCut algorithm

2019 ◽  
Author(s):  
Ayodeji Olalekan Salau ◽  
Thomas Kokumo Yesufu ◽  
Babatunde Sunday Ogundare
Keyword(s):  
Plate Number ◽  
Grabcut Algorithm

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals A Multiobjective Piglet Image Segmentation Method Based on an Improved Noninteractive GrabCut Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Feilong Kang ◽  
Chunguang Wang ◽  
Jia Li ◽  
Zheying Zong
Keyword(s):  
Image Segmentation ◽  
Real Time ◽  
Monitoring System ◽  
Structural Similarity ◽  
Morphological Processing ◽  
Video Monitoring ◽  
Segmentation Method ◽  
Time Requirements ◽  
And Behavior ◽  
Grabcut Algorithm

In the video monitoring of piglets in pig farms, study of the precise segmentation of foreground objects is the work of advanced research on target tracking and behavior recognition. In view of the noninteractive and real-time requirements of such a video monitoring system, this paper proposes a method of image segmentation based on an improved noninteractive GrabCut algorithm. The functions of preserving edges and noise reduction are realized through bilateral filtering. An adaptive threshold segmentation method is used to calculate the local threshold and to complete the extraction of the foreground target. The image is simplified by morphological processing; the background interference pixels, such as details in the grille and wall, are filtered, and the foreground target marker matrix is established. The GrabCut algorithm is used to split the pixels of multiple foreground objects. By comparing the segmentation results of various algorithms, the results show that the segmentation algorithm proposed in this paper is efficient and accurate, and the mean range of structural similarity is [0.88, 1]. The average processing time is 1606 ms, and this method satisfies the real-time requirement of an agricultural video monitoring system. Feature vectors such as edges and central moments are calculated and the database is well established for feature extraction and behavior identification. This method provides reliable foreground segmentation data for the intelligent early warning of a video monitoring system.

scholarly journals UHF RFID Tag Antenna for Vehicle License Plate Number (e-Plate)

2013 ◽  
Vol 11 (2) ◽  
pp. 337 ◽  
Author(s):  
Evizal Evizal ◽  
Tharek Abdul Rahman ◽  
Sharul Kamal Abdul Rahim
Keyword(s):  
License Plate ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Plate Number

GrabCut Algorithm Fusion of Extreme Point Features

2021 ◽  
Author(s):  
Chao Li ◽  
Xiaozhen Zhao ◽  
Huiying Ru
Keyword(s):  
Extreme Point ◽  
Grabcut Algorithm

scholarly journals Key technologies of intelligent transportation based on image recognition

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091727
Author(s):  
Zeyou Chen ◽  
Yangyang Su ◽  
Yong Liu ◽  
Jiazhen Huang ◽  
Wuwen Cao
Keyword(s):  
Image Recognition ◽  
Template Matching ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Recognition System ◽  
Transportation System ◽  
Vertical Projection ◽  
Plate Number ◽  
Number Recognition ◽  
Key Technologies

With the development of economy, the research of urban intelligent transportation system is becoming more and more important. The research and development of plate number recognition system is an important factor to realize the intelligence and modernization of transportation system. It uses each car to have a unique plate number and recognizes the vehicle number through the vehicle image captured by the camera. On the basis of image recognition, this article takes plate number image as the research object and discusses the key technologies of plate number recognition system. First, this article uses image preprocessing technology to process images to improve image quality. Second, the plate number location algorithm based on the connected region search is analyzed. According to the characteristics of the plate number itself, the regional features of the plate number are extracted to locate the plate number accurately. Then, an improved vertical projection-based plate number character segmentation method is proposed to segment plate number characters. Finally, combined with character characteristics, the template matching method is used to recognize plate number characters. The simulation results show that, on the basis of image recognition, this article studies the key technologies of plate number recognition system, which effectively improves the performance of the system and makes the recognition of plate number more effective and accurate.

Sign in / Sign up

Export Citation Format

Share Document