Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels

Shui, Peng‐Lang; Zhang, Weichuan

doi:10.1016/j.patcog.2011.07.020

Cited by 96 publications

(122 citation statements)

References 29 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…Di Zenzo initially used the Sobel operator as channel-wise gradient technique [15], which is sensitive to noise. To improve the anti-noise anility, we prefer to use the ANDD--based gradient [46] considering its superiority in noise robustness. It is worth noting that the implementation codes of the comparative methods are directly taken from the authors for fair and consistent comparison.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Examples of these tiny structures can be found in the two ellipses in Figure 11 Figure 12 illustrates the edge results for Figure 11 (b) produced by the four methods. According to the criteria of reference [46], a good detection should keep good edge resolution and connectivity. The superiority of the proposed method on these criteria is clearly visible as shown in Figure 12.…”

Section: A Experiments On the Farmland Hsismentioning

confidence: 99%

See 1 more Smart Citation

Gravitation-Based Edge Detection for Hyperspectral Images

Sun¹,

Zhang²,

Ren³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract:Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method.

show abstract

Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: A Experiments On the Farmland Hsismentioning

confidence: 99%

Gravitation-Based Edge Detection for Hyperspectral Images

Sun¹,

Zhang²,

Ren³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The attention received by each of the three steps is very different. While regularization, either context-aware or context-unaware, has been common subject of study [19,20], differentiation has been seldom analyzed [21,22], and edge binarization is barely absent from the literature. Canny studied all three steps in the boundary detection process.…”

Section: The Canny Methodsmentioning

confidence: 99%

Gradient extraction operators for discrete interval-valued data

López-Molina

Marco-Detchart

Cerrón

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

View full text Add to dashboard Cite

Digital images are generally created as discrete measurements of light, as performed by dedicated sensors. Consequently, each pixel contains a discrete approximation of the light inciding in a sensor element. The nature of this measurement implies certain uncertainty due to discretization matters. In this work we propose to model such uncertainty using intervals, further leading to the generation of so-called interval-valued images. Then, we study the partial differentiation of such images, putting a spotlight on antisymmetric convolution operators for such task. Finally, we illustrate the utility of the interval-valued images by studying the behaviour of an extended version of the well-known Canny edges detection method.

show abstract

“…Noise-robust edge detector is proposed in [15], which combines a small-scaled isotropic Gaussian kernel and large-scaled anisotropic Gaussian kernels to obtain edge maps of images. Its main advantage is that noise reduction is attained while maintaining high edge resolution.…”

Section: Gaussian Based Image Edge Detectionmentioning

confidence: 99%

“…Anisotropic directional derivatives are derived by using anisotropic Gaussian kernals to capture the locally directional variation of an image. The performance of this method is proved using the aggregate test receiver-operating-characteristic (ROC) curves and the Pratt's Figure of Merit (FOM) [15].…”

Section: Gaussian Based Image Edge Detectionmentioning

confidence: 99%

An Optimal Solution for Image Edge Detection Problem using Simplified Gabor Wavelet

Sujatha¹,

Selvathi²

2012

IJCSEIT

View full text Add to dashboard Cite

Edge detection plays a vital role in computer vision and image processing. Edge of the image is one of the most significant features which are mainly used for image analyzing process. An efficient algorithm for extracting the edge features of images using simplified version of Gabor Wavelet is proposed in this paper. Conventional Gabor Wavelet is widely used for edge detection applications. Due do the high computational complexity of conventional Gabor Wavelet, this may not be used for real time application. Simplified Gabor wavelet based approach is highly effective at detecting both the location and orientation of edges. The results proved that the performance of proposed Simplified version of Gabor wavelet is superior to conventional Gabor

show abstract

Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels

Cited by 96 publications

References 29 publications

Gravitation-Based Edge Detection for Hyperspectral Images

Gravitation-Based Edge Detection for Hyperspectral Images

Gradient extraction operators for discrete interval-valued data

An Optimal Solution for Image Edge Detection Problem using Simplified Gabor Wavelet

Contact Info

Product

Resources

About