Artificial Neural Networks Applied For Digital Images With Matlab Code The Applications Of Artificial Intelligence In Image Processing Field Using Matlab Now

% Load pre-trained detector (requires Deep Learning Toolbox) detector = yolov2ObjectDetector('tiny-yolov2-coco'); % Read image I = imread('street_scene.jpg');

% Annotate I = insertObjectAnnotation(I, 'Rectangle', bboxes, labels); imshow(I); Goal: Assign a class to every pixel (medical imaging, autonomous driving). % Load pre-trained detector (requires Deep Learning Toolbox)

map = gradCAM(net, I, classIdx); imshow(I); hold on; imagesc(map, 'AlphaData', 0.5); Problem: Detect diabetic retinopathy from fundus images. Solution: CNN classifier + heatmap localization. % Read image I = imread('street_scene.jpg')

% Denoise denoisedImgs = predict(autoenc, noisyImgs); Goal: Increase image resolution while preserving details. % Annotate I = insertObjectAnnotation(I

% Predict pred = classify(net, imdsValidation); accuracy = mean(pred == imdsValidation.Labels); disp(['Accuracy: ', num2str(accuracy)]); Goal: Locate and classify multiple objects within an image.

% Load pre-trained VDSR network net = vdsrNetwork; % Low-resolution image lrImage = imresize(highResImage, 0.25); lrImage = imresize(lrImage, size(highResImage));