The extra assignments for the Multimedia Systems course - AUT - Spring - 1403
OpenCV is an open-source library designed for implementing complex algorithms in the field of image processing and machine vision. This library is compatible with programming languages such as C++, Python, and Java, and can be used on various platforms including Windows, Linux, and macOS. OpenCV includes specialized and practical algorithms that cover geometric image transformations, feature extraction, object detection, and motion analysis. Additionally, the library provides capabilities for creating graphical user interfaces, recording video, and analyzing motion.
OpenCV is an indispensable tool in multimedia systems, offering a comprehensive suite for a variety of image and video processing tasks. It is widely used for enhancing media content through operations such as contrast adjustment, histogram analysis, image compression, and video compression. Additionally, OpenCV excels in more complex functions like object detection, facilitating sophisticated multimedia applications that require real-time processing capabilities. This makes it an essential component for developers and researchers working on multimedia projects aiming to improve both the efficiency and quality of media systems.
Assignments Overview
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Section 1: Gamma Correction
- In this section, students will learn how to apply gamma correction to images. Gamma correction is used to adjust the luminance of an image and is essential for correcting the brightness or contrast of an image.
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Section 2: Histogram Equalization
- This section is divided into two parts: Global Histogram Equalization: Students will implement global histogram equalization which adjusts the contrast of an image by modifying the intensity distribution. Adaptive Histogram Equalization (CLAHE): Students will learn to apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of images. This method is useful for enhancing the visibility of features in regions that are darker or lighter than most of the image.
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Section 3: HOG Object Detection
- Here, students will use Histogram of Oriented Gradients (HOG) descriptors for object detection, specifically focusing on detecting pedestrians. This technique is pivotal in computer vision applications such as surveillance and autonomous driving
Installation Requirements
To get started with these assignments, you will need to install Python and OpenCV. OpenCV can be installed via pip, which simplifies the management of library dependencies.
pip install opencv-python