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مقاله

A FUZZY DIFFERENCE BASED EDGE DETECTOR

M. A. Nikouei Mahani, Mohamad Koohi Moghadam, Hosein Nezamabadi-pour

چکیده

In this paper, a new algorithm for edge detection based on fuzzyconcept is suggested. The proposed approach defines dynamic membershipfunctions for different groups of pixels in a 3 by 3 neighborhood of the centralpixel. Then, fuzzy distance and -cut theory are applied to detect the edgemap by following a simple heuristic thresholding rule to produce a thin edgeimage. A large number of experiments are employed to confirm the robustnessof the proposed algorithm. In the experiments different cases such as normalimages, images corrupted by Gaussian noise, and uneven lightening imagesare involved. The results obtained are compared with some famous algorithmssuch as Canny and Sobel operators, a competitive fuzzy edge detector, and astatistical based edge detector. The visual and quantitative comparisons showthe effectiveness of the proposed algorithm even for those images that werecorrupted by strong noise.

شکل‌ها (۱۷)

  • Figure 1. Two Typical Fuzzy Numbers A and B. Crossing Cut α and Fuzzy Numbers A and B Produce the Values a1(α), b1(α), and a2(α), b2(α), Respectively
    Figure 1. Two Typical Fuzzy Numbers A and B. Crossing Cut α and Fuzzy Numbers A and B Produce the Values a1(α), b1(α), and a2(α), b2(α), Respectivelyصفحه‌ی ۴
  • Figure 2. The 8-neighbors of the Central Pixel P0 in a 3*3 Mask
    Figure 2. The 8-neighbors of the Central Pixel P0 in a 3*3 Maskصفحه‌ی ۵
  • Figure 3. The Proposed Six Tis, Black Stands for Class Ai and Gray for Class Bi a) T1 = P artition1 = {A1 = (P1, P2, P3), B1 = (P6, P7, P8)}
    Figure 3. The Proposed Six Tis, Black Stands for Class Ai and Gray for Class Bi a) T1 = P artition1 = {A1 = (P1, P2, P3), B1 = (P6, P7, P8)}صفحه‌ی ۵
  • Figure 4. The Pseudo Code of the Proposed FDBED Algorithm
    Figure 4. The Pseudo Code of the Proposed FDBED Algorithmصفحه‌ی ۸
  • Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Images
    Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Imagesصفحه‌ی ۹
  • Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Images
    Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Imagesصفحه‌ی ۹
  • Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Images
    Figure 5. The Edge Image Provided by the Proposed FDBED Using Different Type of Membership Functions with Image 430*580 Pixels Imagesصفحه‌ی ۹
  • Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.
    Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.صفحه‌ی ۱۰
  • Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.
    Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.صفحه‌ی ۱۰
  • Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.
    Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.صفحه‌ی ۱۰
  • Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.
    Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.صفحه‌ی ۱۰
  • Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.
    Figure 7. a) The Noisy Image Corrupted by Gaussian Noise with Zero Mean and Variance 25, b) The Edge Map Obtained by FBDED Using Gaussian Membership Function After Smoothing.صفحه‌ی ۱۰

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