Digital Image Analysis and Understanding

This course helped me gain an understanding of various aspects related to image processing. I can now use the knowledge and skills necessary to manipulate and enhance digital images for a variety of applications.

Throughout the course, I learned the fundamentals of working with digital images, including concepts such as pixel values, intensity transformations, and techniques in both spatial and frequency domains. This knowledge provided me with a strong understanding of the core principles of image processing.

One key area of focus was image enhancement, where I gained proficiency in techniques to improve the visual quality of grayscale and color digital images through intensity transformations, spatial filtering, and frequency domain techniques.

The exploration of binary image processing addressed smoothing and boundary detection problems, employing morphological filtering techniques. Additionally, the course covered edge detection and image segmentation, introducing various techniques such as thresholding, region growing, region splitting and merging, active contours, and conditional random fields. These methods have equipped me with the skills to partition images into meaningful regions and identify object boundaries.

Venturing into advanced topics, I explored the application of neural networks and deep learning in image processing, understanding how convolutional neural networks (CNNs) can be leveraged for tasks such as image classification and object recognition.

Statistical analysis of images was another essential component of the course, teaching me to analyze image statistics, including probability distributions of pixel intensities. This allowed for the exploration of various image characteristics, such as mean, variance, skewness, and kurtosis

Overall, I gained an appreciation for image compression techniques and their significance in reducing data size for storage and transmission without compromising image quality.

In this assignment, I gathered a diverse collection of images, varying in contrast and detail. Then, using OpenCV, I analyzed each image's probability distribution, mean, variance, and central moments. The goal was to explore potential relationships between these statistical measures and variations in image characteristics, such as contrast and detail levels.