Comparison between Edge Detection Techniques

Kaur, Satbir; Singh, Ishpreet

doi:10.5120/ijca2016910867

Cited by 44 publications

(17 citation statements)

References 7 publications

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“…Another popular two colorless traffic sign detection methods are edge detection features and Haar-like features. Edge detection refers to the process of identifying and locating sharp discontinuities in an image [62]. By using this method, image data is simplified for the purpose of minimizing the amount of data to be processed.…”

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

121

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The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

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Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

121

View full text Add to dashboard Cite

show abstract

“…Therefore, numerous classificationsof pulp disease had only identified by a limited number of clinical diagnostic testsbefore effective dental treatment is performed [11]. Therefore, our research interest was aimed to identify the regionsof interest which is the affected pulp necrotic tooth itself, from the radiographsby applying edges as basis image features, the texture description and the modifiedactive contour model for feature extraction [12, 13].…”

Section: Proposed Methodologymentioning

confidence: 99%

Quantitative growth analysis of pulp necrotic tooth (post‐op) using modified region growing active contour model

Shanmugam¹,

Gunasekaran²,

Natarajan³

et al. 2017

IET Image Processing

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“…The researchers in [35] propose a theoretically based study which aims to explain prevailing edge detection techniques. From the research work it has been deduced that canny edge detection performs better than all other detection techniques.…”

Section: Cnn Architectures Have Been Implemented In This Papermentioning

confidence: 99%

Automated Detection of Diabetic Retinopathy using Deep Residual Learning

Rahman¹,

Rahman²,

Noshin³

2020

IJCA

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Significant amount of people suffer from Diabetic Retinopathy (DR), which is one of the major causes of vision loss. The incidence of this disease is even higher due to not being diagnosed at the right time. On numerous occasions, due to neglect and poor care, diabetic retinopathy can lead to significant damage to the eyes. That is why, early diagnosis of eye diseases, proper treatment and care for the disease can prevent vision loss. Referral of eyes with diabetic retinopathy for advanced assessment and treatment would aid in reducing the chances of vision loss, allowing proper diagnoses. The purpose of this study is to develop resilient and flexible diagnostic techniques for the detection of DR and to identify dynamic DR grading using residual networks to facilitate the network training that are significantly intense than previously used networks. Even though lots of research has been done on DR, its identifications remains challenging due to time and space complexity along with higher accuracy specificity. Here, a residual learning framework has been proposed that overcomes the challenges while efficiently detecting DR. Hence, using a high-end Graphics Processor Unit (GPU) the model has been trained on the publicly available Kaggle dataset and empirical evidence has been provided in order to support the results with a sensitivity of 95.6% and an accuracy of 93.20%.

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Comparison between Edge Detection Techniques

Cited by 44 publications

References 7 publications

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Quantitative growth analysis of pulp necrotic tooth (post‐op) using modified region growing active contour model

Automated Detection of Diabetic Retinopathy using Deep Residual Learning

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