Implementasi Aplikasi Hijab Berbasis Android dengan Metode Canny Operator dan Template Matching Correlation

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Astri Oktavianti ◽  
Winarno Sugeng ◽  
Aldrian Agusta
Keyword(s):  
Template Matching ◽  
Canny Operator

Research on Straight-Line Extraction for Aerial Images

2010 ◽  
Vol 439-440 ◽  
pp. 1203-1208 ◽  
Author(s):  
Chang Li ◽  
Min Hu ◽  
Peng Cheng Liu
Keyword(s):  
Template Matching ◽  
Development Trend ◽  
Image Understanding ◽  
Least Square ◽  
Aerial Images ◽  
Canny Operator ◽  
Line Extraction ◽  
Straight Line ◽  
High Level ◽  
Gaussian Operator

Line and plane feature can provide more information than point feature, thus 3D reconstruction based on high-level features, such as line and plane, is an important development trend in Digital Photogrammetry and Computer Vision. Several methods for extracting straight line are researched, and the main procedures can be introduced as follows. Firstly, image is preprocessed by Wallis filtering that is used to enhance the image contrast and reduce the noise, so it is easy to extract more lines. Secondly, Laplacian of Gaussian operator (LoG) and Canny operator algorithms are compared to locate the edge by detecting discontinuity variation in image. Thirdly, Hough transform or perceptual organization based on hypothesis testing are compared and tested for combining and fitting fractured short line segments into a whole line. Lastly, the least square template matching algorithm (LSTM) is done to get higher precise (sub-pixel) located lines. In the experiment, different algorithms for straight line extraction of aerial images are realized and compared, in order to faster achieve richer and higher accurate straight line information, which can pave the way of image understanding and image matching.

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

Cognition System of Bolt Hole Using Template Matching

2011 ◽  
Author(s):  
Gwan Ryong Baek ◽  
Yung Hak Mo ◽  
Jae Sik Jeong ◽  
Jung Min Park ◽  
Myo Taeg Lim
Keyword(s):  
Template Matching

scholarly journals Spinal vertebrae localization and analysis on disproportionality in curvature using radiography—a comprehensive review

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Joddat Fatima ◽  
Muhammad Usman Akram ◽  
Amina Jameel ◽  
Adeel Muzaffar Syed
Keyword(s):  
Spinal Cord ◽  
Template Matching ◽  
Human Anatomy ◽  
Comprehensive Overview ◽  
Low Contrast ◽  
Active Shape Models ◽  
Harris Corner ◽  
Illumination Changes ◽  
The Central Nervous System ◽  
The Brain

AbstractIn human anatomy, the central nervous system (CNS) acts as a significant processing hub. CNS is clinically divided into two major parts: the brain and the spinal cord. The spinal cord assists the overall communication network of the human anatomy through the brain. The mobility of body and the structure of the whole skeleton is also balanced with the help of the spinal bone, along with reflex control. According to the Global Burden of Disease 2010, worldwide, back pain issues are the leading cause of disability. The clinical specialists in the field estimate almost 80% of the population with experience of back issues. The segmentation of the vertebrae is considered a difficult procedure through imaging. The problem has been catered by different researchers using diverse hand-crafted features like Harris corner, template matching, active shape models, and Hough transform. Existing methods do not handle the illumination changes and shape-based variations. The low-contrast and unclear view of the vertebrae also makes it difficult to get good results. In recent times, convolutional nnural Network (CNN) has taken the research to the next level, producing high-accuracy results. Different architectures of CNN such as UNet, FCN, and ResNet have been used for segmentation and deformity analysis. The aim of this review article is to give a comprehensive overview of how different authors in different times have addressed these issues and proposed different mythologies for the localization and analysis of curvature deformity of the vertebrae in the spinal cord.

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