You will learn the essential I/O commands.
The book is logically organized into two major parts. Part I focuses exclusively on Image Processing, building a solid foundation from the ground up.
while hasFrame(videoReader) frame = readFrame(videoReader);
imhist(gray_img) displays the distribution of pixel intensities. practical image and video processing using matlab pdf new
Spatial filtering alters pixel values directly based on their coordinates and surrounding neighbors. Intensity Adjustments
Modern MATLAB versions integrate seamlessly with Deep Learning. You can now import pre-trained models like YOLO (You Only Look Once) or ResNet to perform real-time object detection with just a few lines of code. The Deep Network Designer
% Enhance a low-contrast medical X-ray raw_scan = imread('xray.png'); enhanced_scan = adapthisteq(raw_scan, 'ClipLimit', 0.02, 'Distribution', 'rayleigh'); imshowpair(raw_scan, enhanced_scan, 'montage'); Use code with caution. Automated Object Detection and Tracking You will learn the essential I/O commands
The keyword "new" is critical. Image processing evolves rapidly. A PDF published in 2024 or 2025 will cover topics older books ignore:
While many books stop at images, Part II of Practical Image and Video Processing Using MATLAB extends the concepts into the world of video.
Histograms plot the frequency of each pixel intensity. Equalization spreads out the most frequent intensity values, stretching the dynamic range and improving image contrast. You can now import pre-trained models like YOLO
Multi-dimensional arrays containing extra channels beyond human vision. Key Matrix Commands
Image processing involves manipulating and analyzing digital images to enhance or extract useful information. The basic steps involved in image processing are:
Two-dimensional arrays typically stored as uint8 (0 to 255) or double (0.0 to 1.0). RGB Images: Three-dimensional arrays ( ) representing Red, Green, and Blue color channels.
Some of the new approaches in video processing using MATLAB include: