Topic
Kirsch operator
About: Kirsch operator is a research topic. Over the lifetime, 40 publications have been published within this topic receiving 171 citations.
Papers
TL;DR: An algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle to efficiently and accurately analyze various forms of module defects.
Abstract: Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.
83 citations
TL;DR: Novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator, which can perform real-time image processing with high accuracy.
Abstract: In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.
50 citations
1 Dec 1981
TL;DR: This thesis presents an algorithm for detecting man-made objects embedded in low resolution imagery using a modified Kirsch edge operator for initial image enhancing and a normal Kirsch operator for edge detection.
Abstract: : This thesis presents an algorithm for detecting man-made objects embedded in low resolution imagery. A modified Kirsch edge operator is used for initial image enhancing. A normal Kirsch operator is then used for edge detection. A two-dimensional threshold for edges and the original intensity detects the pixels on the edges of the objects only. These pixels are then subjected to connectedness and size tests to detect the blobs which most probable represent man-made objects. The algorithm was tried on 325 pictures and a detection probability of 83.3% was achieved. False alarm probability was less than 10%. (Author)
39 citations
10 Jun 2001
TL;DR: The template-matching approach generalizes the well-known Kirsch operator for 2D images and can detect change of intensity in every direction, and has the property of rotation invariance in 18-neighborhood.
Abstract: This paper proposes a template-matching approach to the edge detection of volume data. Twenty-six templates of an ideal step-like edge in the 3/spl times/3/spl times/3 neighborhood of volume data are given, and the step-like edge of volume data is detected by matching such patterns in various orientations. The approach is a simple and straightforward one for edge detection of volume data. It generalizes the well-known Kirsch operator for 2D images. It can detect change of intensity in every direction, and has the property of rotation invariance in 18-neighborhood. Implementation of proposed approach is given for biological and medical volume data, including MRI and CT volume data.
14 citations
13 Aug 2015
TL;DR: A new method based on background-estimation for hard exudates detection is presented and the proposed method has been demonstrated on the public databases of DIARETDB1 and HEI-MED.
Abstract: Hard exudates (HEs) are one kind of the most important symptoms of Diabetic Retinopathy (DR). A new method based on background-estimation for hard exudates detection is presented. Firstly, through background-estimation, foreground map containing all bright objects is acquired. We use the edge information based on Kirsch operator to obtain HE candidates, and then we remove the optic disc. Finally, the shape features, histogram statistic features and phase features of the HE candidates are extracted. We use the SVM classifier to acquire the accurate extraction of HEs. The proposed method has been demonstrated on the public databases of DIARETDB1 and HEI-MED. The experiment results show that the method’s sensitivity is 97.3 % and the specificity is 90 % at the image level, and the mean sensitivity is 84.6 % and the mean predictive value is 94.4 % at the lesion level.
11 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 2 |
| 2020 | 4 |
| 2019 | 6 |
| 2018 | 3 |
| 2017 | 2 |
| 2015 | 4 |