Overview on Edge Detection Methods

Waghule, Diplaxmi R.; Ochawar, Rohini S.

doi:10.1109/icesc.2014.31

Cited by 19 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the real normal vector map does not contain reflective regions, and most of the images obtained by the camera contain reflective regions, such regions not only cause the estimated normal vectors to deviate from the true normal vectors, but also cause the six polarized surface normal vectors obtained by Equations (2) and (3) to contain reflective regions as well. Traditional reflective region localization requires manual setting of thresholds and is less well achieved [ 18 ]. In this paper, we propose to use the spectral properties of specular reflection model to obtain the reflective region map

, as in the following equation.…”

Section: Fundamentalsmentioning

confidence: 99%

Polarized Object Surface Reconstruction Algorithm Based on RU-GAN Network

Yang

Cheng

Duan

et al. 2023

Sensors

View full text Add to dashboard Cite

There are six possible solutions for the surface normal vectors obtained from polarization information during 3D reconstruction. To resolve the ambiguity of surface normal vectors, scholars have introduced additional information, such as shading information. However, this makes the 3D reconstruction task too burdensome. Therefore, in order to make the 3D reconstruction more generally applicable, this paper proposes a complete framework to reconstruct the surface of an object using only polarized images. To solve the ambiguity problem of surface normal vectors, a jump-compensated U-shaped generative adversarial network (RU-Gan) based on jump compensation is designed for fusing six surface normal vectors. Among them, jump compensation is proposed in the encoder and decoder parts, and the content loss function is reconstructed, among other approaches. For the problem that the reflective region of the original image will cause the estimated normal vector to deviate from the true normal vector, a specular reflection model is proposed to optimize the dataset, thus reducing the reflective region. Experiments show that the estimated normal vector obtained in this paper improves the accuracy by about 20° compared with the previous conventional work, and improves the accuracy by about 1.5° compared with the recent neural network model, which means the neural network model proposed in this paper is more suitable for the normal vector estimation task. Furthermore, the object surface reconstruction framework proposed in this paper has the characteristics of simple implementation conditions and high accuracy of reconstructed texture.

show abstract

, as in the following equation.…”

Section: Fundamentalsmentioning

confidence: 99%

Polarized Object Surface Reconstruction Algorithm Based on RU-GAN Network

Yang

Cheng

Duan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A number of second order differential methods [16,[22][23][24][25] exist for the detection of edges. Second order differential methods detect the crossing of the zero gradient axis of ∇ 2 I(x, y) to define the location of edges within an image.…”

Section: Second Ordermentioning

confidence: 99%

A Low Redundancy Wavelet Entropy Edge Detection Algorithm

et al. 2021

View full text Add to dashboard Cite

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon Entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

show abstract

“…As one of the most important parts of image processing, edge detection is of great significance to image high-order feature extraction, target recognition, image segmentation and many other fields. The image edge refers to the area where the grey value of adjacent pixels changes dramatically [1]. Various edge detection methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing

Kong¹,

Chen²,

Song³

et al. 2023

IJACSA

View full text Add to dashboard Cite

In this paper, a novel adaptive threshold Sobel edge detection algorithm based on the improved genetic algorithm is proposed to detect edges. Because of the influence of external factors in actual detection process, the result of detection is often not accurate enough when the configured threshold of the target image is far away from the real threshold. Different thresholds of images are calculated by improved genetic algorithm for different images. The calculated threshold is used in edge detection. The experimental results show that the image processed by the improved algorithm has stronger edge continuity. It is shown that proposed algorithm has a better detection effect and applicability than the traditional Sobel algorithm.

show abstract

Overview on Edge Detection Methods

Cited by 19 publications

References 12 publications

Polarized Object Surface Reconstruction Algorithm Based on RU-GAN Network

Polarized Object Surface Reconstruction Algorithm Based on RU-GAN Network

A Low Redundancy Wavelet Entropy Edge Detection Algorithm

Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing

Contact Info

Product

Resources

About