Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification

Wanto, Anjar; Rizki, Syafrika Deni; Andini, Silfia; Surmayanti, S; Ginantra, Ni Luh Wiwik Sri Rahayu; Aspan, Henry

doi:10.1088/1742-6596/1933/1/012037

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Berdasarkan analisis dan pengujian, hasil penelitian menggunakan teknik deteksi tepi gabungan Roberts dan Canny menghasilkan akurasi identifikasi citra yang lebih baik dibandingkan dengan Sobel dan Prewitt. Akurasi rata-rata adalah 92,84% berbanding 68,75% [18]. P Vinista dan M M Joe (2019) Memodifikasi algoritma Sobel untuk deteksi tepi gambar yang lebih baik.…”

Section: Pendahuluanunclassified

Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan

Winanjaya

Anggraini

2022

bits

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Utilizing an edge detection algorithm in an image will produce the edges of the image object. The aim is to mark the part that becomes the image's detail and correct the point of blurring of vision that occurs due to errors or the effects of the image acquisition process. This study aims to see the ability of the combination of Sobel and Canny edge detection algorithms (SoCan) to detect the inverted image. The image dataset used is the image of the Laysan Albatross, which consists of 10 original images and ten images that have been inverted based on the standard image dataset. The Laysan albatross is a large species of seabird found in the North Pacific. 99.7% of the total population is found in the Northwest Hawaiian Islands. The research dataset was obtained from the Caltech Vision Lab website http://www.vision.caltech.edu/datasets/cub_200_2011/ with dimensions of 500 x 271 pixels. Based on the analysis of 10 experiments carried out, the combination of the Sobel and Canny algorithm (SoCan) is not good at performing edge detection because it only has an average accuracy of 47.79% with an average accuracy error rate of 52.21%. Thus, in this case, the combination of the Sobel and Canny algorithms (SoCan) is not able to identify the Inversion Image

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Section: Pendahuluanunclassified

Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan

Winanjaya

Anggraini

2022

bits

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“…There are many edge detection methods, such as differential operator, neural network, and wavelet detection. At present, edge detection based on differential operators is commonly used, mainly including Roberts, Sobel, Canny, Prewitt, and other detection operators [32][33][34]. Among them, the Prewitt operator and Sobel operator have a smoothing effect on noise.…”

Section: Image Edge Detectionmentioning

confidence: 99%

Object-Oriented Canopy Gap Extraction from UAV Images Based on Edge Enhancement

et al. 2022

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Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas.

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“…The discontinuities in grayscale intensity are the most salient features in the image and the first to be discovered. Subsequently, a variety of filters have been introduced, such as Roberts, Sobel, Laplacian, Log and Canny operators (Wanto et al , 2021). Roberts and Sobel belong to first-order differential operators.…”

Section: Introductionmentioning

confidence: 99%

A satellite component contour extraction method for lightweight space mobile platforms

Zhu

Liang

2023

AEAT

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Purpose Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms. Design/methodology/approach A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction. Findings Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes. Practical implications The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms. Originality/value A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

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Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification

Cited by 11 publications

References 10 publications

Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan

Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan

Object-Oriented Canopy Gap Extraction from UAV Images Based on Edge Enhancement

A satellite component contour extraction method for lightweight space mobile platforms

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