Multiscale edge detection based on Gaussian smoothing and edge tracking

López-Molina, Carlos; Baets, Bernard De; Bustince, Humberto; Sanz, José Antonio; Barrenechea, Edurne

doi:10.1016/j.knosys.2013.01.026

Cited by 92 publications

(68 citation statements)

References 64 publications

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“…The results are usually examined either by visual inspection as a qualitative measure [1,26] or quantitatively by di erent indexes [27][28][29][30][31][32][33][34][35]. Some of these algorithms utilize a linking technique collecting pixels that belong to a set of edges [36][37][38].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, many papers have been published in the area of image edge detection [27][28][29][30][31][32][33][34][35] that tests its importance as follows: Lopez-Molina et al [27] presented a study that focuses on the improvements of edge detection by using Anisotropic Di usion (AD) instead of Gaussian Linear Filtering (GLF); in [28], the modi ed scheme is presented to improve the performance of traditional Canny edge detection through an adaptive lter based on bi-dimensional general auto-regression model; in [29], Lopez-Molina et al presented a novel edge detection framework based on the measurement of grey level changes using a new class of functions called relief functions; in [30], a new edge detection method that combines smoothing spline algorithm and gray- Figure 1. Schematic overview of the proposed algorithm where di erent steps can be observed.…”

Section: Related Workmentioning

confidence: 99%

“…The nal edge map (d) is obtained by applying the threshold values t1, t2, and t3 to the original image. moments operator is introduced; in [31], Ray presented a scheme for unsupervised edge detection that uses the highly e cient Absolute Di erence Mask (ADM) algorithm to generate the initial edge image and uses a subsequent modi ed non-maximal suppression scheme to optimize the edge output resulting in the nal edge map; in [32], Lopez-Molina et al presented a multiscale method for edge detection based on increasing Gaussian smoothing, the Sobel operators, and coarseto-ne edge tracking; in [33], a study focusing on the edge detection process by the generation of a fuzzy representation of the edges is shown; in [34], a new approach is presented for edge detection using a combination of Bacterial Foraging Algorithm (BFA) and a probabilistic derivative technique derived from Ant Colony Systems; in [35], a new PSO-based approach is presented to detect edges in noisy images and reduce the broken and jagged ones by means of a developed penalized tness function based on the possibility score of a curve tted on an edge and its curvature cost.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Elaraby

Moratal

2017

Scientia Iranica

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Abstract. Edge detection in medical imaging is a signi cant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an e cient approach based on generalized Hill entropy to nd a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: rstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The nal edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four di erent types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Elaraby

Moratal

2017

Scientia Iranica

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“…Gaussian low-pass filter has the properties of having no overshoot to a step function input while minimizing the rise and fall time [17]. The pixels of space distribution are used as a reference value in an image denoising process; the weight distribution is determined based on distance to the target pixel, according to the calculate principle, the Gaussian low-pass filter blur effect close to human natural vision [18,19]. Using a weight template the standard deviation for the convolution operation can realize the image smoothing process, with a standard deviation value becoming larger and the image smoothing effect becoming stronger.…”

Section: Introductionmentioning

confidence: 99%

Preservation of image edge feature based on snowfall model smoothing filter

Fan

Zhu

2018

J Image Video Proc.

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This paper proposed a snowfall model as a novel smoothing filter. The pixel composition of the image was similar to the geographic features, so it could be smooth because of snow accumulation. In the snowfall processing, luminance changes are linked to terrain and snowfall amount. Curvature and luminance gradient decided the amount of snowfall; the amount of snowfall became large on the parts where the curvature was large, and it became little on the parts where the gradient was steep. Snowfall algorithm simulates the natural snowfall process, which nonlinear diffusion and the target feature could be preserved well. Snowfall model has the same function as the Gaussian filter. The number of regions was reduced after Gaussian filter and snowfall model smoothing, respectively. The contrast experiment was carried out based on Watershed algorithm. The image area segmentation that pretreated through snowfall model was compared with Gaussian filter smoothing. The experimental result showed that the proposed snowfall model was a smoothing filter. It was able to realize edge preservation, which was the original purpose, and it was also possible to apply to region segmentation.

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“…Moreover, it maintains the spatial relations between adjacent scales, and provides a good way to imitate the human visual system (HVS) for perceiving objects ranging from whole to local details [28]. Generally, scale spaces can be constructed by wavelet transform [30], Gaussian smoothing [31], and mathematical morphology [32]. The scale space constructed by mathematical morphology is non-linear, and it is good for maintaining the shape of an object.…”

Section: Introductionmentioning

confidence: 99%

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

Wang

Huang

et al. 2016

Remote Sensing

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Abstract:Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user's requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points.

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Multiscale edge detection based on Gaussian smoothing and edge tracking

Cited by 92 publications

References 64 publications

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Preservation of image edge feature based on snowfall model smoothing filter

Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

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