Contour detection in an infrared image using the modified snake algorithm

Infrared and thermal images have been used widely in the different forensics and security applications. Such images show the temperature difference between different objects and scene background. One of the drawbacks of such images is low contrast and noisy images which should be enhanced. This paper presents a new thermal image contour detection algorithm using the modified snake algorithm. The segmentation algorithm based on the image enhancement and the modified model of active contours based on regions, taking into account the calculation of the anisotropic gradient. Some presented experimental results illustrate the performance of the proposed cloud system on real thermal images in comparison with the traditional methods.

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A Novel Fuzzy Level Set Approach for Image Contour Detection

Mathematical Problems in Engineering ◽

10.1155/2016/2602647 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yingjie Zhang ◽

Jianxing Xu ◽

H. D. Cheng

Keyword(s):

Differential Equation ◽

Level Set ◽

Energy Function ◽

Level Set Methods ◽

Contour Detection ◽

Low Contrast ◽

Probability Densities ◽

Entropy Principle ◽

Image Contour ◽

Level Set Approach

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

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Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform

Entropy ◽

10.3390/e21030244 ◽

2019 ◽

Vol 21 (3) ◽

pp. 244 ◽

Cited By ~ 9

Author(s):

Julio Mello Román ◽

José Vázquez Noguera ◽

Horacio Legal-Ayala ◽

Diego Pinto-Roa ◽

Santiago Gomez-Guerrero ◽

...

Keyword(s):

Multiple Scales ◽

Infrared Image ◽

Infrared Images ◽

Public Database ◽

Human Visual Perception ◽

Multiple Features ◽

Thermal Images ◽

Low Contrast ◽

Database Evaluation ◽

Infrared Thermal Images

Discrete entropy is used to measure the content of an image, where a higher value indicates an image with richer details. Infrared images are capable of revealing important hidden targets. The disadvantage of this type of image is that their low contrast and level of detail are not consistent with human visual perception. These problems can be caused by variations of the environment or by limitations of the cameras that capture the images. In this work we propose a method that improves the details of infrared images, increasing their entropy, preserving their natural appearance, and enhancing contrast. The proposed method extracts multiple features of brightness and darkness from the infrared image. This is done by means of the multiscale top-hat transform. To improve the infrared image, multiple scales are added to the bright areas and multiple areas of darkness are subtracted. The method was tested with 450 infrared thermal images from a public database. Evaluation of the experimental results shows that the proposed method improves the details of the image by increasing entropy, also preserving natural appearance and enhancing the contrast of infrared thermal images.

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A Multi-Target Detection Algorithm for Infrared Image based on Retinex and LeNet5 Neural Network

10.23940/ijpe.18.11.p16.27022710 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lijun Yun

Keyword(s):

Neural Network ◽

Target Detection ◽

Infrared Image ◽

Detection Algorithm

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A Temperature Monitoring Method for Power Electronic Converter Based on Infrared Image and Object Detection Algorithm

10.1109/cieec50170.2021.9510854 ◽

2021 ◽

Author(s):

Hongcheng Yang ◽

Yu Chen ◽

Yong Kang

Keyword(s):

Object Detection ◽

Infrared Image ◽

Detection Algorithm ◽

Temperature Monitoring ◽

Power Electronic ◽

Monitoring Method ◽

Power Electronic Converter

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Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

Security and Communication Networks ◽

10.1155/2021/1549772 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Zhaoli Wu ◽

Xin Wang ◽

Chao Chen

Keyword(s):

Real Time ◽

Target Detection ◽

Pedestrian Detection ◽

Infrared Image ◽

Far Infrared ◽

Detection Algorithm ◽

Model Parameters ◽

Detection Accuracy ◽

Detection Model ◽

Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

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Identification of Bone Fragmentation in X-Ray Images using Contour Detection Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7851.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 2121-2124

Keyword(s):

Edge Detection ◽

Bone Fracture ◽

Detection Algorithm ◽

Contour Detection ◽

Crack Identification ◽

Fracture Detection ◽

Canny Edge Detection ◽

X Ray ◽

Base Paper ◽

Canny Edge

The crack can occur in any bone ofour body. Broken bone is a bone condition that endured a breakdown of bone trustworthiness. The Fracture can't recognize effortlessly by the bare eye, so it is found in the x-beam images. The motivation behind this task is to find the precise territory where the bone fracture happens utilizing X-Ray Bone Fracture Detection by Canny Edge Detection Method. Shrewd Edge Detection technique is an ideal edge identification calculation on deciding the finish of a line with alterable limit and less error rate. The reproduction results have indicated how canny edge detection can help decide area of breaks in x-beam images. In the base paper, the cracked bit is chosen physically to defeat this downside, the proposed technique identify the bone fracture consequently and furthermore it quantifies the parameter like length of the crack, profundity of the fracture and the situation of the crack as for even and vertical pivot. The outcome demonstrates that the proposed technique for crack identification is better. The outcomes demonstrate that calculation is 91% exact and effective

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