List of Articles Edge Detection

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  1 - Accurate Fire Detection System for Various Environments using Gaussian Mixture Model and HSV Space
  Khosro Rezaee Seyed Jalaleddin Mousavirad Mohammad Rasegh Ghezelbash Javad Haddadnia
  10.7508/jist.2013.01.007
  20.1001.1.23221437.2013.1.1.5.0
  Smart and timely detection of fire can be very useful in coping with this phenomenon and its inhibition. Enhancing some image analysis methods such as converting RGB image to HSV image, smart selecting the threshold in fire separation, Gaussian mixture model, forming po More

  Smart and timely detection of fire can be very useful in coping with this phenomenon and its inhibition. Enhancing some image analysis methods such as converting RGB image to HSV image, smart selecting the threshold in fire separation, Gaussian mixture model, forming polygon the enclosed area resulted from edge detection and its combination with original image, this papers addresses fire detection. Accuracy and precision in performance and rapid detection of fire are among the features that distinguish this proposed system from similar fire detection systems such as Markov model, GM, DBFIR and other algorithms introduced in valid articles. The average accuracy (95%) resulted from testing 35000 frames in different fire environments and the high sensitivity (96%) was quite significant. This system be regarded as a reliable suitable alternative for the sensory set used in residential areas, but also the high speed image processing and accurate detection of fire in wide areas makes it low cost, reliable and appropriate. Manuscript profile
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  2 - A Robust Statistical Color Edge Detection for Noisy Images
  Mina Alibeigi Niloofar Mozafari Zohre Azimifar Mahnaz Mahmoodian
  10.7508/jist.2015.02.003
  Edge detection is a fundamental tool that plays a significant role in image processing, and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. Therefore, edge detection is one of the well-studied areas in image p More

  Edge detection is a fundamental tool that plays a significant role in image processing, and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. Therefore, edge detection is one of the well-studied areas in image processing and computer vision. However, it is clear that accurate edge map generation is more difficult when images are corrupted with noise. Moreover, most of edge detection methods have parameters which must be set manually. In recent years different approaches has been used to address these problems. Here we propose a new color edge detector based on a statistical test, which is robust to noise. Also, the parameters of this method will be set automatically based on image content. To show the effectiveness of the proposed method, four state-of-the-art edge detectors are implemented and the results are compared. Experimental results on five of the most well-known edge detection benchmarks show that the proposed method is robust to noise. The performance of our method for lower levels of noise is very comparable to the existing approaches, whose performances highly depend on their parameter tuning stage. However, for higher levels of noise, the observed results significantly highlight the superiority of the proposed method over the existing edge detection methods, both quantitatively and qualitatively. Manuscript profile
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  3 - An Efficient Noise Removal Edge Detection Algorithm Based on Wavelet Transform
  Ehsan Ehsaeian
  10.7508/jist.2017.17.005
  In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two st More

  In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two stage. The first idea comes from the response multiplication across the derivation and the second one is pruning algorithm which improves fake edges. Our method is applied to the binary and the natural grayscale image in the noise-free and the noisy condition with the different power density. The results are compared with the traditional wavelet edge detection method in the visual and the statistical data in the relevant tables. With the proper selection of the wavelet basis function, an admissible edge response to the significant inhibited noise without the smoothing technique is obtained, and some of the edge detection criteria are improved. The experimental visual and statistical results of studying images show that our method is feasibly strong and has good edge detection performances, in particular, in the high noise contaminated condition. Moreover, to have a better result and improve edge detection criteria, a pruning algorithm as a post processing stage is introduced and applied to the binary and grayscale images. The obtained results, verify that the proposed scheme can detect reasonable edge features and dilute the noise effect properly. Manuscript profile
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  4 - An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images
  Hamed Agahi Kimia Rezaei
  10.52547/jist.9.36.285
  20.1001.1.23221437.2021.9.36.5.6
  This paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images. The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions. To this aim, an optimal thres More

  This paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images. The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions. To this aim, an optimal threshold is automatically found, according to which the proposed method dichotomizes the pixels to the edges and non-edges. First, some pre-processing operations are applied to the image. Then, the vector sum of the gravitational forces applied to each pixel by its neighbors is computed according to the universal law of gravitation. Afterwards, the force magnitude is mapped to a new characteristic called the force feature. Following this, the histogram representation of this feature is determined, for which an optimal threshold is aimed to be discovered. Three thresholding techniques are proposed, two of which contain iterative processes. The parameters of the formulation used in these techniques are adjusted by means of the metaheuristic grasshopper optimization algorithm. To evaluate the proposed system, two standard databases were used and multiple qualitative and quantitative measures were utilized. The results confirmed that the methodology of our work outperformed some conventional and recent detectors, achieving the average precision of 0.894 on the BSDS500 dataset. Moreover, the outputs had high similarity to the ideal edge maps. Manuscript profile
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  5 - Edge Detection and Identification using Deep Learning to Identify Vehicles
  Zohreh Dorrani Hassan Farsi Sajad Mohammadzadeh
  10.52547/jist.16385.10.39.201
  20.1001.1.23221437.2022.10.39.2.6
  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract informati More

  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods. Manuscript profile