Optimized approach of sobel edge detection technique using Xilinx system generator

2015 ◽  
Author(s):  
Sumant Kumar Mohapatra ◽  
Biswa Ranjan Swain ◽  
Sushil Kumar Mahapatra
Keyword(s):  
Edge Detection ◽  
Detection Technique ◽  
System Generator ◽  
Xilinx System Generator ◽  
Sobel Edge Detection

Study on Edge Detection Technique in Material Bag Image

2010 ◽  
Vol 97-101 ◽  
pp. 4408-4411
Author(s):  
Tian Hou Zhang ◽  
Chang Chun Li ◽  
Shi Feng Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Theoretical Basis ◽  
Automatic Recognition ◽  
Detection Technique ◽  
Segmentation Method ◽  
Edge Information ◽  
Sobel Edge Detection ◽  
Iterative Threshold

According to the features of material bag image, the paper compares an analyzes the detection effects of different edge detection operators detecting material bag image. A new image segmentation method is proposed to combine Sobel edge detection operator and iterative threshold. The method can extract edge information of material bag image efficiently and provide a theoretical basis for the robot automatic recognition of material bags technique.

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

Using Neural Natural Network for Image Recognition in Bioinformatics

2021 ◽  
pp. 830-836
Author(s):  
Dina Kharicheva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Image Recognition ◽  
Detection Technique ◽  
Connected Component ◽  
Novel Technique ◽  
Automatic Image Recognition ◽  
Sobel Edge Detection ◽  
Artificial Neural Network Ann

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.

scholarly journals Image Edge Detection Using FPGA

2013 ◽  
Vol 10 (1) ◽  
pp. 1192-1200
Author(s):  
Cms Amrutha Kumari ◽  
Syed Jahangir Badashah
Keyword(s):  
Edge Detection ◽  
Automatic Segmentation ◽  
Detection Methods ◽  
X Ray ◽  
System Generator ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Image Edge ◽  
Xilinx System Generator

Medical imaging often involves the injection of contrast agents and subsequent analysis of tissue enhancement patterns. X-ray angiograms are projections of 3D reality into 2D representations, there is a fair amount of self occlusion among the vessels, hence one cannot extract the vessels directly using the image intensities or gradients (edge) alone. Vessels extraction from angiogram images is useful for blood vessels measurement and computer visualizations of the coronary artery. This project describes the algorithm for automatic segmentation of coronary arteries in digital X-ray projections here an improved k-means algorithm is proposed. The performance of the proposed algorithm is compared with other techniques. A methodology for implementing real-time DSP applications on a field programmable gate arrays (FPGA) using Xilinx System Generator (XSG) for Mat lab is presented in this paper. It presents the architecture for Edge Detection using Sobel Filter for image processing using Xilinx System Generator. The design was implemented targeting a Spartan3 a DSP 3400 device (XC3SD3400A-4FGG676C) then a vertex 5 (xc5vlx50-1ff676) .the edge detection methods has been verified successfully with no visually perceptual errors in the resulted images.

Using Neural Natural Network for Image Recognition in Bioinformatics

2019 ◽  
Vol 9 (2) ◽  
pp. 35-41
Author(s):  
Dina Kharicheva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Image Recognition ◽  
Detection Technique ◽  
Connected Component ◽  
Novel Technique ◽  
Automatic Image Recognition ◽  
Sobel Edge Detection ◽  
Artificial Neural Network Ann

Automatic image recognition is very useful in bioinformatics. This article presents a novel technique to recognize the characters in the number plate automatically by using connected component analysis (CCA), artificial neural network (ANN) and neural natural network (Triple N). The preprocessing steps, Sobel edge detection technique and CCA are applied to the captured image of the vehicle to obtain character images. ANN technique can be used over these images to recognize the characters of the image in bioinformatics. The preprocessing steps are used to remove the noise and to enhance the image for recognizing the characters effectively. After performing the preprocessing steps, the edge detection technique and CCA are carried out to separate the character images from the whole image which can be recognized using ANN. These text characters can be compared with database to find authentication of vehicle, identifying the owner of the vehicle, penalty bill generation, etc.

scholarly journals Video Steganography using Sobel Edge Detection Technique

2020 ◽  
Vol 9 (5) ◽  
pp. 1735-1738
Keyword(s):  
Edge Detection ◽  
Research Work ◽  
Detection Technique ◽  
Video Steganography ◽  
Sobel Edge Detection

Video Steganography is a procedure of hiding the message into video in such a way that no one can detect its presence. In proposed research work used two algorithms namely random scan and edge detection for hiding the data into video. Five edge operators are used for edge detection of selected frames namely Sobel, Canny, Prewitt, Log, Robert.

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.

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