Comparison of Discrete Curvature Estimators and Application to Corner Detection

On the Stability of Refined L-Curvature under Rotation Transformations

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.401 ◽

2010 ◽

Vol 20-23 ◽

pp. 401-406

Author(s):

Bao Jiang Zhong

Keyword(s):

Image Processing ◽

Computer Vision ◽

Critical Role ◽

Corner Detection ◽

Curve Matching ◽

Discrete Curvature ◽

Efficient Measure ◽

Curvature Measures ◽

The Stability

Estimation of curvature on digital curves plays a critical role for various applications in the field of image processing and computer vision. The refined L-curvature has proven to be an efficient measure of discrete curvature for corner detection and curve matching. In this paper its stability under rotation transformations is evaluated. Experiments with comparisons to other existing curvature measures show that the refined L-curvature performs best.

Download Full-text

Discrete Curvature Representations for Noise Robust Image Corner Detection

IEEE Transactions on Image Processing ◽

10.1109/tip.2019.2910655 ◽

2019 ◽

Vol 28 (9) ◽

pp. 4444-4459 ◽

Cited By ~ 8

Author(s):

Weichuan Zhang ◽

Changming Sun ◽

Toby Breckon ◽

Naif Alshammari

Keyword(s):

Corner Detection ◽

Discrete Curvature ◽

Robust Image ◽

Noise Robust

Download Full-text

Corner detection using multi-scale representation of contour orientation rate based on B-spline

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.00725 ◽

2009 ◽

Vol 29 (3) ◽

pp. 725-728

Author(s):

Ming-jian HONG ◽

Xiao-hong ZHANG ◽

Dan YANG

Keyword(s):

Corner Detection ◽

B Spline ◽

Multi Scale

Download Full-text

Harris Corner Detection Algorithm Optimization Based on OTSU

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180212100932 ◽

2018 ◽

Vol 11 (2) ◽

pp. 91-96

Author(s):

Wang Lifang ◽

Zhao Yanan ◽

Qin Pinle ◽

Gao Yuan

Keyword(s):

Detection Algorithm ◽

Corner Detection ◽

Algorithm Optimization ◽

Harris Corner ◽

Harris Corner Detection

Download Full-text

Infrared small target detection based on region proposal and CNN classifier

Signal Image and Video Processing ◽

10.1007/s11760-021-01936-z ◽

2021 ◽

Author(s):

Mingming Fan ◽

Shaoqing Tian ◽

Kai Liu ◽

Jiaxin Zhao ◽

Yunsong Li

Keyword(s):

Neural Network ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Detection Rate ◽

Corner Detection ◽

Small Target ◽

Small Target Detection ◽

Potential Target ◽

Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

Download Full-text

Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

Applied Sciences ◽

10.3390/app11093863 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3863

Author(s):

Ali Emre Öztürk ◽

Ergun Erçelebi

Keyword(s):

Deep Learning ◽

Image Classification ◽

Synthetic Data ◽

Real Data ◽

Corner Detection ◽

Batch Size ◽

Test Accuracy ◽

Classification Problems ◽

Auc Value ◽

Classification Test

A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

Download Full-text