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I tried to read Digital Image Processing by Gonzalez/Woods but I found it difficult to understand/grasp. I have taken a Graduate Course in Computer Vision, which is more practically oriented and I am doing lot of cool stuff with OpenCV, however I still feel I am swimming in higher abstractions, and do NOT understand the basics beneath.
I am planning to read a book on Computer Vision/Image Processing during the Winter Break to solidify my understanding of the content and would appreciate some must-read suggestions
I have done assignments like - camera calibration, image transforms, stitching images into panoramas, haar classification.
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Self-driving cars, biometrics and facial recognition all rely on computer vision to work. At the core of computer vision is image processing.
Image processing is mainly focused on processing the raw input images to enhance them or preparing them to do other tasks. Computer vision is focused on extracting information from the input images or videos to have a proper understanding of them to predict the visual input like human brain.
Image Data Collection (Image Datasets) Most computer vision-related models are trained on data sets consisting of hundreds (or even thousands) of images. A good data set is essential to ensure that your AI model can classify or predict the outcomes with high accuracy.
Many advanced Image Processing methods leverage Machine Learning Models like Deep Neural Networks to transform images on a variety of tasks, like applying artistic filters, tuning an image for optimal quality, or enhancing specific image details to maximize quality for computer vision tasks.
You should probably take a look at Szeliski's book
Hartley and Zisserman's book is also excellent.
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Gonzales and woods (or Wintz in my day) is a very good introduction.
There is a more readable but less concise introduction - Image-Processing-Analysis-Machine-Vision
And since you are working with opencv - you can do worse than read the opencv book
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