Fuzzy Index to Evaluate Edge Detection in Digital Images

Perez-Ornelas, Felicitas; Mendoza, Olivia; Melín, Patricia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Castillo, Oscar

doi:10.1371/journal.pone.0131161

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…The experimental results are shown in Tables 1 and 2. To assess the performance of the four edge detection methods, there are many estimation indicators, such as reference [42][43][44], in the paper, in order to accurately evaluate the performance of improved edge detection method, the evaluation indices used in a previous study [45] were used for comparison, and the experimental results are shown in Tables 3-6. For the sake of convenience, the following tables use one-hundredth of a second as the time unit.…”

Section: Objective Comparison Of the Experimental Results Obtainedmentioning

confidence: 99%

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Liu

2023

International Journal of Aerospace Engineering

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Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection.

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Section: Objective Comparison Of the Experimental Results Obtainedmentioning

confidence: 99%

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Liu

2023

International Journal of Aerospace Engineering

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“…For the first two images, an FD = 0.2 was assigned for each gradient, while for the last three images an FD = 0.6 was used. To evaluate the efficiency of the proposed edge detection method, the metric based on the "Figure of merit" of Pratt's (FOM) [37][38][39] was used. FOM values are between 0 and 1 (If the result of the FOM is 1 or very close to 1, this means that the detected edge is the same or very similar to the reference image.…”

Section: Simulation Resultsmentioning

confidence: 99%

General Type-2 Fuzzy Sugeno Integral for Edge Detection

et al. 2019

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A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.

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“…This method however is noted as the first performance evaluation without ground truth that analyses the structure of edges. Ornelas et al (2015) proposed fuzzy index for performance evaluation. They argued that current methods of edge performance evaluation discard critical information contained in pixel level since they normally operate in edge map which is known as a binary image.…”

Section: Related Workmentioning

confidence: 99%

Performance measurement without ground truth to achieve optimal edge

Crysdian

2017

International Journal of Image and Data Fusion

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This paper presents an effort to develop performance measurement of edge detector without ground truth. The motivation is to drive edge detector to catch up with human vision that is capable of always generating closed-loop edge. Two axioms of edge are presented, i.e. the presentation of edge as a closed-loop line and its characteristic as being thin. A set of features and approaches are generated in this research in order to enable the measurement of edge map. Experiment shows that current edge detector is still difficult to catch up with human vision for recognising edge. Experiment also proves that statistical approach based on object size delivers the best performance measurement among other approaches. And, the use of region of interest is proved useful to optimise edge measurement.

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Fuzzy Index to Evaluate Edge Detection in Digital Images

Cited by 19 publications

References 34 publications

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

General Type-2 Fuzzy Sugeno Integral for Edge Detection

Performance measurement without ground truth to achieve optimal edge

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