Comparison of SUSAN and Sobel Edge Detection in MRI Images for Feature Extraction

2006 ◽  
Author(s):  
G. Rezai-Rad ◽  
M. Aghababaie
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Sobel Edge Detection

The Use of Hybrid Technique: Thresholding and Edge Detection for Identifying River from Aerial Photo

2012 ◽  
Author(s):  
Mohd. Shafry Mohd. Rahim ◽  
Nik Isrozaidi Nik Ismail ◽  
Mohd. Azuan Shah Idris
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Edge Detection ◽  
Key Words ◽  
Detection Technique ◽  
Hybrid Technique ◽  
Aerial Photo ◽  
Hybrid Techniques ◽  
Grey Scale ◽  
Sobel Edge Detection

Bidang pemprosesan imej merupakan satu bidang yang luas dengan pelbagai aplikasi terutama dalam bidang sains dan industri. Pemprosesan imej digunakan dalam manipulasi dan penambahbaikan imej untuk memudahkan proses seterusnya. Penyelidikan ini melibatkan penggunaan teknik hybrid yang menggabungkan teknik threshold dan teknik pengesanan sisi Sobel, untuk mengenal pasti sungai daripada imej berskala kelabu. Teknik thresholding digunakan untuk mengurangkan piksel sisi yang tak maksima, piksel sisi yang lemah dan mengurangkan kesan hingar, manakala edge detection digunakan untuk mengesan kehadiran piksel sisi. Hasil yang diperolehi daripada penggunaan teknik hybrid dibandingkan dengan teknik–teknik yang sedia ada seperti Sobel, Prewitt, Laplacian dan Robert Cross. Kata kunci: Pemprosesan imej, mengenal pasti ciri-ciri, pengesanan garis, foto udara The field of image processing is a broad field with many applications in science and industry. Image processing is used to manipulate and enhance an image, which ease the next process. This research involves the use of a hybrid techniques, which is a combination of thresholding and Sobel edge detection technique, to recognize a river from a grey scale image. Thresholding technique is used to reduce non-maxima pixels, weak edges and noise, whilst the edge detection technique is used to detect location of the edges. The output from this hybrid technique is compared to the existing techniques such as Sobel, Prewitt, Laplacian, and Robert Cross technique. Key words: Image processing, feature extraction, edge detection, aerial photo

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

scholarly journals Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 265
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Fei Teng ◽  
Yunhao Lin ◽  
Mengjie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Hunan Province ◽  
Building Roof ◽  
Rural Buildings ◽  
High Resolution Images ◽  
Rgb Images ◽  
Sobel Edge Detection ◽  
Model Recognition

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Automatic Object Classification and Image Retrieval by Sobel Edge Detection

2014 ◽  
pp. 196-201
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Object Classification ◽  
Similarity Coefficient ◽  
Cosine Similarity ◽  
Retrieval Algorithms ◽  
Sobel Edge Detection ◽  
Classification Tool

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

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