Recognition Based on Decision-Theoretic Methods and Matching in DIP and its implementation in MATLAB

Video lecture series on Digital Image Processing, Lecture: 73, Recognition Based on Decision-Theoretic Methods and Matching in Object Recognition for DIP and its implementation in MATLAB Which are different object recognition methods? Which are different types of Decision- Theoretic methods of object recognition? Implementation of Matching using minimum distance classifier in MATLAB Link to download ppts/lecture notes: https://drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link #DIP #DIPwithMATLAB #DigitalImageProcessingUsingMATLAB #digitalimageprocessing #digitalimageprocessinglecturesin4K #studywithdrdafda The MATLAB code used in the video is given below: % MATLAB program that generates random binary prototype % objects and a random binary test object, then matches % test object to prototypes using minimum distance classifier clc; close all; clear all; % Define the size of the binary objects (e.g., 10x10 pixels) object_size = [10, 10]; % Generate random binary prototype objects num_prototypes = 5; % Can be changed to any number of prototypes prototypes = cell(1, num_prototypes); for i = 1:num_prototypes prototypes{i} = randi([0, 1], object_size); end % Generate a random binary test object test_object = randi([0, 1], object_size); % Initialize variables to keep track of minimum distance and closest prototype min_distance = Inf; closest_prototype = []; % Loop through each prototype to calculate distance and find closest match for i = 1:num_prototypes prototype = prototypes{i}; % Calculate the Euclidean distance between the test object and the prototype distance = sqrt(sum((double(test_object(:)) - double(prototype(:))).^2)); % Update the closest match if this prototype is closer if distance (less than) min_distance min_distance = distance; closest_prototype = prototype; closest_index = i; end end % Display the test object and the closest matching prototype figure; subplot(1, 2, 1); imshow(test_object, 'InitialMagnification', 'fit'); title('Test Object'); subplot(1, 2, 2); imshow(closest_prototype, 'InitialMagnification', 'fit'); title(['Closest Prototype (Index ' num2str(closest_index) ')']);