An adaptive edge-detection method based on histogram

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840088
Author(s):  
Hongyang Zhao ◽  
Miaoyi Shang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Area ◽  
Adaptive Method ◽  
Good Effect ◽  
High Threshold ◽  
Edge Pixel ◽  
Edge Points ◽  
Current Threshold ◽  
Edge Detection Method

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

Image Edge Detection Based on Direction Fuzzy Entropy

2011 ◽  
Vol 268-270 ◽  
pp. 1234-1238
Author(s):  
Xian Qing Ling ◽  
Jun Lu ◽  
Lei Wang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Feature Space ◽  
Fuzzy Entropy ◽  
Noise Performance ◽  
Edge Pixel ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Image Edges

To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.

scholarly journals Implementasi Penggabungan Prewitt dan Canny Edge Detection untuk Identifikasi Ikan Air Tawar

KREA-TIF ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 120
Author(s):  
Gibtha Fitri Laxmi ◽  
Puspa Eosina ◽  
Fety Fatimah
Keyword(s):  
Edge Detection ◽  
Image Retrieval ◽  
Freshwater Fish ◽  
Detection Method ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
A Value ◽  
Edge Points ◽  
Canny Edge ◽  
Edge Detection Method

<p align="center"><strong>Abstrak</strong></p><p class="IsiAbstrak">Indonesia merupakan negara yang memiliki keanekaragaman hayati yang besar, salah satunya jenisnya ialah keanekaragaman ikan air tawar. Ikan air tawar yang layak konsumsi saat ini pun banyak jenisnya, sehingga bagi masyarakat yang kurang pengetahuan untuk mengenali jenis ikan sangatlah sulit. Teknologi identifikasi pengenalan citra dengan berbasis konten citra (Content Based Image Retrieval) dengan fitur bentuk berdasarkan titik tepi yang dihasilkan dapat membantu mengenali jenis ikan yang ada. Citra ikan yang digunakan diubah dari RGB menjadi grayscale yang diproses dengan metode deteksi tepi menjadi matriks nilai biner sehingga membentuk titik tepi dari ikan. Data citra ikan air tawar dalam penelitian berjumlah sepuluh jenis ikan, yang akan diproses untuk mendapatkan ekstraksi fitur deteksi tepinya. Deteksi tepi yang digunakan ialah penggabungan metode prewitt dan canny. Penelitian ini tidak memiliki hasil yang akurat dengan nilai 25%. Dimana penggabungan fitur lain akan sangat membantu dalam identifikasi.</p><p align="center"><strong>Abstract</strong></p><p><em>Indonesia is a country that has a great biodiversity, one of which is the diversity of freshwater fish. Freshwater fish that are suitable for consumption today are of many kinds, so that people who lack knowledge to recognize fish species are very difficult. Image recognition identification technology with Content Based Image Retrieval with shape features based on the resulting edge points can help identify the types of fish that exist. The fish image used is converted from RGB to grayscale which is processed by edge detection method into a binary value matrix so that it forms the edge points of the fish. Image data of freshwater fish in the study amounted to ten types of fish, which will be processed to obtain extraction of the edge detection features. The edge detection used is the merging of the prewitt and canny methods. This study did not have accurate results with a value of 25%. Where combining other features will be very helpful in identification.</em></p>

scholarly journals Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Changzhi Yu ◽  
Fang Ji ◽  
Xingjiu Jing ◽  
Mi Liu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Structured Light ◽  
Experimental Tests ◽  
Adaptive Method ◽  
Matrix Space ◽  
The Hierarchical Structure ◽  
Canny Edge ◽  
Edge Detection Method ◽  
Two Phases

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

Research and Improvement on Fast Location Algorithm in License Plate

2014 ◽  
Vol 543-547 ◽  
pp. 2792-2795
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Edge Detection ◽  
Partial Derivative ◽  
High Frequency ◽  
Detection Method ◽  
Recognition System ◽  
License Plate ◽  
The Third ◽  
Edge Points ◽  
Image Edge ◽  
Edge Detection Method

In the recognition system of license plate, the detection effect is often influenced by the speed of vehicle, the weather and illumination condition. However, the image edge is less influenced by the above conditions, so it gets more and more attention by using edge detection method to detect license plate. In this paper, three kinds of edge detection method based on partial derivative are compared. Firstly, using the first derivative to get the point set of gray step is discussed and thus the edge is obtained. However, this methods' result is largely influenced by noise. Secondly, adopting denosing theory and second partial derivative to acquire the image edge is represented, but the result shows that this method would filter out some high frequency edges and lead to the edge loss. Finally, the improved algorithm that is the fusion of three aspects: denosing theory, the second partial derivative and linking isolated edge points, is put forward. The result shows that the third algorithm has strong ability to restrain noise. However, at the same time it would smooth some high frequency edges out and lead to the edge loss. However, the third method finally makes isolated points link together, which ensure the integrity of the edge. Therefore, the result obtained by the second partial algorithm is better than the results by the two previous algorithms.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

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