The Research of Edge Detection in RMB Paper Currency Value Identification

2013 ◽  
Vol 756-759 ◽  
pp. 3492-3496
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu
Keyword(s):  
Edge Detection ◽  
Color Pattern ◽  
Edge Image ◽  
Gradient Operator ◽  
Paper Currency ◽  
Canny Algorithm

When using international popular algorithms deal with RMB paper currency, there exist three main aspects. First, the size of RMB paper currency is different with other countries. Second, the color of RMB paper currency is different from other countries, and the different face values have different major color, pattern and texture. Third, the location of face value in paper currency of our country is different from other countries. For RMB paper currency image, using edge detection is to separate the paper currency from the image according to boundary. In other words, the background is removed and only paper currency is left. This paper firstly introduces the principles of removing background by gradient operator, LOG algorithm and Canny algorithm. Then it analyzes the edge image results obtained by these three kind algorithms. At last, these three algorithms are evaluated. Results show that the Canny algorithm can satisfy the requirement of removing background and can improve a good foundation for the subsequent of paper currency extraction.

Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition

2012 ◽  
Vol 220-223 ◽  
pp. 1279-1283 ◽  
Author(s):  
Li Hong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Rock Interface ◽  
Coal Rock ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

An Improved Edge Detection Algorithm Based on Canny Operator

2013 ◽  
Vol 347-350 ◽  
pp. 3541-3545 ◽  
Author(s):  
Dan Dan Zhang ◽  
Shuang Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Image Simulation ◽  
Edge Image ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Edge Detection Algorithm

The traditional Canny edge detection algorithm is analyzed in this paper. To overcome the difficulty of threshold selecting in Canny algorithm, an improved method based on Otsu algorithm and mathematical morphology is proposed to choose the threshold adaptively and simultaneously. This method applies the improved Canny operator and the image morphology separately to image edge detection, and then performs image fusion of the two results using the wavelet fusion technology to obtain the final edge-image. Simulation results show that the proposed algorithm has better anti-noise ability and effectively enhances the accuracy of image edge detection.

scholarly journals SURVEY ON DISTRIBUTED CANNY EDGE DETECTOR WITH FPGA

2015 ◽  
Vol 3 (3) ◽  
pp. 51-61
Author(s):  
Poonam S. Deokar ◽  
Anagha P. Khedkar
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Large Scale ◽  
Attractive Alternative ◽  
Edge Detector ◽  
Canny Edge Detection ◽  
Canny Edge Detector ◽  
Canny Edge ◽  
Canny Algorithm ◽  
Real Time Applications

The Edge can be defined as discontinuities in image intensity from one pixel to another. Modem image processing applications demonstrate an increasing demand for computational power and memories space. Typically, edge detection algorithms are implemented using software. With advances in Very Large Scale Integration (VLSI) technology, their hardware implementation has become an attractive alternative, especially for real-time applications. The Canny algorithm computes the higher and lower thresholds for edge detection based on the entire image statistics, which prevents the processing of blocks independent of each other. Direct implementation of the canny algorithm has high latency and cannot be employed in real-time applications. To overcome these, an adaptive threshold selection algorithm may be used, which computes the high and low threshold for each block based on the type of block and the local distribution of pixel gradients in the block. Distributed Canny Edge Detection using FPGA reduces the latency significantly; also this allows the canny edge detector to be pipelined very easily. The canny edge detection technique is discussed in this paper.

scholarly journals A Proposed Approach to Determine the Edges in SAR images

2020 ◽  
pp. 185-192
Author(s):  
Rabab Farhan Abbas
Keyword(s):  
Edge Detection ◽  
Electromagnetic Waves ◽  
Operator Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Edge Image ◽  
Sar Images ◽  
Wavelet Method ◽  
Radar Images ◽  
Sobel Operator

Radar is the most eminent device in the prolonged scattering era The mechanisms involve using electromagnetic waves to take Synthetic Aperture Radar (SAR) images for long reaching. The process of setting edges is one of the important processes used in many fields, including radar images, which assists in showing objects such as mobile vehicles, ships, aircraft, and meteorological and terrain forms. In order to accurately identify these objects, their edges must be detected. Many old-style methods are used to isolate the edges but they do not give good results in the  determination process. Conservative methods use an operator to detect the edges, such as the Sobel operator which is used to perform edge detection where the edge does not appear well.      The proposed method which combines Ridgelet transform, Bezier curve and Sobel operator is used to detect edges very efficiently. Ridghelet transform resolves the harms in the wavelet transform and it can well detect the edges in images. Bezier curve can profit gradual variation of the data and their mutability. Hence, the efficiency of the edged image is improved and, when used with Sobel operator, the quality of the edge image become very good. The data show that the advocated method has superior fallouts over the Sobel edge detection and the wavelet method in both subjective and impartial experiments. While the Peak Signal to Noise Ratio(PSNR) values were equal to 9.3812, 9.8918, 9.6521 and 9.0743using the Sobel operator method and to10.2564, 10.7927, 10.5612and 10.8633 using the wavelet method, they were increased in the proposed method to 12.6542, 12.9514, 12.8574 and 12.3013 respectively.

scholarly journals Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Febri Liantoni ◽  
Luky Agus Hermanto
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Ant Colony ◽  
K Nearest Neighbor ◽  
Edge Image ◽  
Image Detection ◽  
Mango Leaves ◽  
Leaf Midrib

Abstract. Leaf is one important part of a plant normally used to classify the types of plants. The introduction process of mango leaves of mangung and manalagi mango is done based on the leaf edge image detection. In this research the conventional edge detection process was replaced by ant colony optimization method. It is aimed to optimize the result of edge detection of mango leaf midrib and veins image. The application of ant colony optimization method successfully optimizes the result of edge detection of a mango leaf midrib and veins structure. This is demonstrated by the detection of bony edges of the leaf structure which is thicker and more detailed than using a conventional edge detection. Classification testing using k-nearest neighbor method obtained 66.67% accuracy. Keywords: edge detection, ant colony optimization, classification, k-nearest neighbor. Abstrak. Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor. Daun merupakan salah satu bagian penting dari tanaman yang biasanya digunakan untuk proses klasifikasi jenis tanaman. Proses pengenalan daun mangga gadung dan mangga manalagi dilakukan berdasarkan deteksi tepi citra struktur tulang daun. Pada penelitian ini proses deteksi tepi konvensional digantikan dengan metode ant colony optimization. Hal ini bertujuan untuk optimasi hasil deteksi tepi citra tulang daun mangga. Penerapan metode ant colony optimization berhasil mengoptimalkan hasil deteksi tepi struktur tulang daun mangga. Hal ini ditunjukkan berdasarkan dari hasil deteksi tepi citra struktur tulang daun yang lebih tebal dan lebih detail dibandingkan menggunakan deteksi tepi konvensional. Pengujian klasifikasi dengan metode k-nearest neighbor didapatkan nilai akurasi sebesar 66,67%.Kata Kunci: deteksi tepi, ant colony optimization, klasifiaksi, k-nearest neighbor.

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