Practical Image and Video Processing Using MATLAB by Oge Marques is a cornerstone text for anyone serious about learning the practical aspects of visual data processing. Its strength lies in its structured, clear, and technically accurate presentation of the fundamentals, supported by an extensive collection of hands-on MATLAB tutorials. While the core first edition is not "new" in terms of publication date (2011), its pedagogical value remains immense and it is widely available in digital PDF format through numerous academic libraries and online retailers.
: The Image Processing Toolbox and Computer Vision Toolbox provide ready-to-use apps and functions for complex tasks like object detection, feature extraction, and camera calibration. Core Processing Techniques Practical workflows typically follow a structured pipeline: Practical Image and Video Processing Using MATLAB® | PDF
was authored by Oge Marques . While the original text was published in 2011, several recent companion resources and related updated textbooks are available for modern applications in 2024 and 2025. Core Content Overview
If you are working with a (e.g., live webcam feeds, high-frame-rate cameras).
% Initialize video reader videoSource = VideoReader('traffic.mp4'); % Initialize video writer videoTarget = VideoWriter('output_traffic.avi'); open(videoTarget); % Loop through each frame while hasFrame(videoSource) frame = readFrame(videoSource); % Perform processing (e.g., convert to grayscale) processedFrame = rgb2gray(frame); % Write the frame (convert back to 3D for standard color video structures) writeVideo(videoTarget, im2uint8(cat(3, processedFrame, processedFrame, processedFrame))); end close(videoTarget); Use code with caution. practical image and video processing using matlab pdf new
: Academic and professional previews are often accessible through platforms like O'Reilly Media Wiley Online Library specific MATLAB code examples
MATLAB allows you to import pre-trained deep learning networks (such as ResNet, MobileNet, or YOLO) directly from open-source frameworks like OpenVINO, TensorFlow, and ONNX.
Dilation followed by erosion; joins broken segments.
% Binarize using Otsu's method bw = imbinarize(enhanced, 'global'); % Clean up background noise se = strel('disk', 2); clean_bw = imopen(bw, se); Use code with caution. Feature Detection and Analysis Practical Image and Video Processing Using MATLAB by
To begin processing, you must master reading, displaying, and writing images. The Image Processing Toolbox simplifies this process.
Real-world scenarios, such as detecting defects in industrial products or tracking vehicles in surveillance footage.
: Build a solution for object detection and tracking within video sequences Key Learning Features 30+ MATLAB Tutorials : Step-by-step guides for exploring algorithms firsthand Amazon.com Minimal Math, Maximum Action
After mastering the PDF, you will be capable of building: : The Image Processing Toolbox and Computer Vision
Whether you're an engineering student or a researcher, mastering digital media requires more than just theory; it requires hands-on experimentation. Practical Image and Video Processing Using MATLAB
Filtering removes unwanted noise and highlights critical structural edges.Operations occur in either the spatial or frequency domain. Spatial Domain Filtering
: Unlike lower-level languages like C++, MATLAB offers concise syntax and rapid prototyping, allowing users to test algorithms without long compilation times.
This book is explicitly designed for a diverse group of readers:
imadjust maps intensity values to new values, increasing global contrast. adapthisteq performs Contrast-Limited Adaptive Histogram Equalization (CLAHE), which enhances local contrast. Noise Reduction: imfilter(img, w) applies a custom linear filter matrix