Ali Imran – Page 13 – TechReport.us

Over the past 20+ years, I have been working as a software engineer, architect, and programmer, creating, designing, and programming various applications. My main focus has always been to achieve business goals and transform business ideas into digital reality. I have successfully solved numerous business problems and increased productivity for small businesses as well as enterprise corporations through the solutions that I created. My strong technical background and ability to work effectively in team environments make me a valuable asset to any organization.

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.

CH-4 – Features Extraction – Part-4

Algorithm 2 extracts histogram features from BMP images. It begins by taking an image as input and converts it to grayscale if necessary. The image is divided into tiles, and histograms are calculated for each tile. These histograms are stored in a 2D array, representing the result. The algorithm iterates through tiles, extracting sub-images, calculating histograms, and storing them in the result array. Finally, the resulting array of numerical values defining the histogram is returned, providing essential feature extraction for subsequent analysis in video stream matching.