{"id":15831,"date":"2022-10-07T00:22:58","date_gmt":"2022-10-06T17:22:58","guid":{"rendered":"https:\/\/blink-drive.com\/?p=15831"},"modified":"2022-10-07T04:09:31","modified_gmt":"2022-10-06T21:09:31","slug":"matlabroad-detection-project-unt","status":"publish","type":"post","link":"https:\/\/blink-drive.com\/index.php\/2022\/10\/07\/matlabroad-detection-project-unt\/","title":{"rendered":"[MATLAB]Road Detection Project UNT"},"content":{"rendered":"\n

\u0e2b\u0e21\u0e32\u0e22\u0e40\u0e2b\u0e15\u0e38 : \u0e19\u0e35\u0e48\u0e44\u0e21\u0e48\u0e43\u0e0a\u0e48\u0e42\u0e1e\u0e2a\u0e23\u0e16\u0e22\u0e19\u0e15\u0e4c\u0e44\u0e1f\u0e1f\u0e49\u0e32\u0e04\u0e23\u0e31\u0e1a \u0e1c\u0e21\u0e40\u0e2d\u0e32\u0e40\u0e27\u0e47\u0e1a Blink Drive \u0e40\u0e02\u0e35\u0e22\u0e19\u0e2a\u0e48\u0e07\u0e07\u0e32\u0e19\u0e2d\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c\u0e04\u0e23\u0e31\u0e1a skip \u0e44\u0e14\u0e49 skip \u0e40\u0e25\u0e22\u0e04\u0e23\u0e31\u0e1a \u0e41\u0e15\u0e48\u0e16\u0e49\u0e32\u0e43\u0e04\u0e23\u0e2d\u0e22\u0e32\u0e01\u0e28\u0e36\u0e01\u0e29\u0e32\u0e07\u0e32\u0e19\u0e17\u0e35\u0e48\u0e1c\u0e21\u0e17\u0e33\u0e01\u0e47\u0e40\u0e0a\u0e34\u0e0d\u0e40\u0e02\u0e49\u0e32\u0e21\u0e32\u0e2d\u0e48\u0e32\u0e19\u0e44\u0e14\u0e49\u0e15\u0e32\u0e21\u0e2d\u0e31\u0e18\u0e22\u0e32\u0e28\u0e31\u0e22\u0e04\u0e23\u0e31\u0e1a \u0e19\u0e35\u0e48\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e1b\u0e23\u0e40\u0e08\u0e04\u0e01\u0e25\u0e38\u0e48\u0e21\u0e43\u0e19\u0e01\u0e32\u0e23\u0e40\u0e2d\u0e32\u0e20\u0e32\u0e1e\u0e16\u0e48\u0e32\u0e22\u0e17\u0e32\u0e07\u0e14\u0e32\u0e27\u0e40\u0e17\u0e35\u0e22\u0e21\u0e21\u0e32\u0e19\u0e31\u0e48\u0e07\u0e41\u0e01\u0e30\u0e2b\u0e32\u0e40\u0e2a\u0e49\u0e19\u0e15\u0e48\u0e32\u0e07\u0e46 \u0e41\u0e25\u0e30 generate \u0e16\u0e19\u0e19\u0e2d\u0e2d\u0e01\u0e21\u0e32\u0e08\u0e32\u0e01\u0e20\u0e32\u0e1e\u0e04\u0e23\u0e31\u0e1a \u0e01\u0e33\u0e25\u0e31\u0e07\u0e1b\u0e31\u0e48\u0e19\u0e2d\u0e22\u0e39\u0e48\u0e04\u0e23\u0e31\u0e1a \u0e22\u0e31\u0e07\u0e44\u0e21\u0e48\u0e40\u0e2a\u0e23\u0e47\u0e08<\/p>\n\n\n\n

This post is built to keep the history of the road detection experiment via MATLAB tools, CSCE 5222 [MATLAB] at Artificial Intelligence, UNT. <\/p>\n\n\n\n

Here is the code for this project : Github<\/a><\/p>\n\n\n\n

1. Dataset : Ground Truth Image<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

2. Convert to Grayscale Image<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

3. Contrast Enhancement<\/h2>\n\n\n\n

This feature is apply the contrast enhancement into grayscale image.<\/p>\n\n\n\n

Enhanced01 : First Enhanced : 10\/6\/22<\/h4>\n\n\n\n
\n
\"\"
Enhanced Images using imadjust<\/figcaption><\/figure>\n\n\n\n
\"\"
Enhanced Images using adapthisteq<\/figcaption><\/figure>\n\n\n\n
\"\"
Enhanced Images using histeq<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

4. Apply Filters<\/h2>\n\n\n\n

Filter Detail :<\/p>\n\n\n\n

  • Sobel : Default<\/li>
  • Canny : Threshold 0.25<\/li>
  • Log (Laplacian of Gaussian)<\/li><\/ul>\n\n\n\n

    We experiment by applying the filters in each images from above.<\/p>\n\n\n\n

    Filtered01 : Grayscale Image <\/h3>\n\n\n\n
    • Original : Grayscale Image<\/li>
    • Apply Date : 10\/02\/22<\/li><\/ul>\n\n\n\n
      \n
      \"\"
      Sobel_GrayOrigin_01<\/figcaption><\/figure>\n\n\n\n
      \"\"
      Canny_GrayOrigin_01<\/figcaption><\/figure>\n\n\n\n
      \"\"
      Log_GrayOrigin_01<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

      Filtered02 : imadjust Image<\/h3>\n\n\n\n
      • Original image : imadjust : Adjust image intensity values or colormap<\/li>
      • Apply Date : 10\/06\/22<\/li><\/ul>\n\n\n\n
        \n
        \"\"
        Sobel_imadjust_02<\/figcaption><\/figure>\n\n\n\n
        \"\"
        Canny_imadjust_02<\/figcaption><\/figure>\n\n\n\n
        \"\"
        Log_imadjust_02<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

        Filtered03 : histeq Image<\/h3>\n\n\n\n
        • Original image : Histogram Equalization : equalize the image histogram<\/li>
        • Apply Date : 10\/06\/22<\/li><\/ul>\n\n\n\n
          \n
          \"\"
          Sobel_histeq_03<\/figcaption><\/figure>\n\n\n\n
          \"\"
          Canny_histeq_03<\/figcaption><\/figure>\n\n\n\n
          \"\"
          Log_histeq_03<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

          Filtered04 : adapthisteq Image<\/h3>\n\n\n\n
          • Original image : Histogram Equalization : equalize the image histogram<\/li>
          • Apply Date : 10\/06\/22<\/li>
          • <\/li><\/ul>\n\n\n\n
            \n
            \"\"
            Sobel_adapthisteq_04<\/figcaption><\/figure>\n\n\n\n
            \"\"
            Canny_adapthisteq_04<\/figcaption><\/figure>\n\n\n\n
            \"\"
            Log_adapthisteq_04<\/figcaption><\/figure>\n<\/figure>\n\n\n\n

            5. Remove the Noise<\/h2>\n\n\n\n

            <\/p>\n

            \n\t\t\t
            \n\t\t\t <\/div><\/div>\n\t\t