image processing – Page 3

CH-3 – Image Analysis – Part-1

This step performs the following: From input video(s) extracted the frames, which are called mean frames. These mean frames are store on hard disk, used for decision making. Extract the features from mean frames.

CH-4 – Classification Model – Part-8

After all features calculating these features are send to classification model where these features are used for decision making. Basic theme for all models is same so here only discuss the abstract approach for decision making.

CH-4 – Wavelets Feature Extraction – Part-7

It takes an Image and converts it into gray scale if it is not already in gray scale. For wavelets it apply wavelets HARR on image and return coefficient values.

CH-4 – Slope Feature Extraction – Part-6

It takes an Image and converts it into gray scale if it is not already in gray scale. For slope magnitude it checks the per pixel maximum change with its neighbor’s pixels.

CH-4 – Edge Feature Extraction – Part-5

Algorithm 3 extracts edge map features from BMP images. It begins by converting the image to grayscale if necessary and applies the Canny algorithm, returning the image edges. The process involves steps like Gaussian filtering to remove noise, Sobel operator application in horizontal and vertical directions, and angle calculation for each pixel. The resulting edges are thresholded and subjected to double thresholding for refinement. Finally, the processed image with edge maps is returned.

Algorithm 4 operates similarly but focuses on extracting edge angles instead of edge maps. It follows a comparable process, including Gaussian filtering, Sobel operator application, and angle calculation, but stores the occurrences of each angle in tiles of the image, returning an array defining the edge angles.