Geometric mesh corner detection using triangle principle

2017 ◽  
Vol 53 (20) ◽  
pp. 1354-1356 ◽  
Author(s):  
Xinyu Lin ◽  
Ce Zhu ◽  
Qian Zhang ◽  
Yipeng Liu
Keyword(s):  
Corner Detection ◽  
Geometric Mesh

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

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

scholarly journals Infrared small target detection based on region proposal and CNN classifier

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.

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

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.

scholarly journals Dynamic Resource-Aware Corner Detection for Bio-Inspired Vision Sensors

2021 ◽  
Author(s):  
Sherif A.S. Mohamed ◽  
Jawad N. Yasin ◽  
Mohammad-Hashem Haghbayan ◽  
Antonio Miele ◽  
Jukka Heikkonen ◽  
...  
Keyword(s):  
Corner Detection ◽  
Vision Sensors ◽  
Dynamic Resource ◽  
Resource Aware

scholarly journals Simulation and Analysis of Floodlighting Based on 3D Computer Graphics

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1042
Author(s):  
Rafał Krupiński
Keyword(s):  
Computer Graphics ◽  
External Surface ◽  
Geometric Model ◽  
Three Dimensional ◽  
Fold Increase ◽  
Luminance Level ◽  
Object Model ◽  
Geometric Mesh ◽  
Average Luminance ◽  
To Receive

The paper presents the opportunities to apply computer graphics in an object floodlighting design process and in an analysis of object illumination. The course of object floodlighting design has been defined based on a virtual three-dimensional geometric model. The problems related to carrying out the analysis of lighting, calculating the average illuminance, luminance levels and determining the illuminated object surface area are also described. These parameters are directly tied with the calculations of the Floodlighting Utilisation Factor, and therefore, with the energy efficiency of the design as well as the aspects of light pollution of the natural environment. The paper shows how high an impact of the geometric model of the object has on the accuracy of photometric calculations. Very often the model contains the components that should not be taken into account in the photometric calculations. The research on what influence the purity of the geometric mesh of the illuminated object has on the obtained results is presented. It shows that the errors can be significant, but it is possible to optimise the 3D object model appropriately in order to receive the precise results. For the example object presented in this paper, removing the planes that do not constitute its external surface has caused a two-fold increase in the average illuminance and average luminance. This is dangerous because a designer who wants to achieve a specific average luminance level in their design without optimizing the model will obtain the luminance values that will actually be much higher.

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