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

Automated Number Plate Recognition Using Template Matching

2018 ◽  
Vol 6 (12) ◽  
pp. 298-304
Author(s):  
Nancy Aggarwal ◽  
Shilpa Sethi
Keyword(s):  
Template Matching

Rapid speech keyword spotting method based on template matching

2013 ◽  
Vol 33 (11) ◽  
pp. 3138-3140
Author(s):  
Guoteng ZHU ◽  
Wei SUN
Keyword(s):  
Template Matching ◽  
Keyword Spotting

Method based on feature template matching to identify special icons in map

2013 ◽  
Vol 26 (7) ◽  
pp. 605-609 ◽  
Author(s):  
Yang Li ◽  
Xiaodong Zhang ◽  
Yuanlv Bao
Keyword(s):  
Template Matching ◽  
Feature Template

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.

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.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

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