CMP362 Image Processing and Pattern Recognition
Image Processing and Pattern Recognition notes
8 chapter notes, in syllabus order. Each starts with the key points.
Unit 1
Digital Image Fundamentals: Pixels, Representations & AcquisitionUnit 1 of Image Processing and Pattern Recognition covers the core concepts of digital images—how they are formed, stored, and represented in binary, including pixel structures, color models, image acquisition systems, and spatial relationships. This note explains everything from monochrome to RGB/CMYK, sampling, quant7 min readUnit 2
Image Enhancement in Spatial Domain: Techniques, Filters & ApplicationsUnit 2 of Image Processing and Pattern Recognition explores spatial-domain enhancement methods—point operations, spatial filtering, and morphological processing—to improve image quality, contrast, and features for better analysis and recognition.8 min readUnit 3
Image Enhancement in Frequency Domain · note coming
Unit 4
Image Restoration · note coming
Unit 5
Colour Models, Transformations & Applications in Image ProcessingUnit 5 of Image Processing and Pattern Recognition explores colour representation systems (RGB, CMYK, HSI, YCbCr), colour space transformations, colour image operations, and real-world applications in digital imaging, including compression and display technologies.8 min readUnit 6
Image Compression: Techniques, Methods & ApplicationsUnit 6 of Image Processing and Pattern Recognition explores lossless and lossy compression, transform coding, wavelet-based compression, JPEG/JPEG2000, Huffman coding, and vector quantization, with real-world examples from apps like WhatsApp and Daraz, and worked examples using actual pixel data.13 min readUnit 7
Morphological Image Processing: Erosion, Dilation, Opening, Closing, Hit-or-MissUnit 7 of Image Processing and Pattern Recognition explores morphological operations—erosion, dilation, opening, closing, and hit-or-miss transforms—using structuring elements to analyze and modify binary/grayscale images for shape extraction, noise removal, and segmentation.10 min readUnit 8
Image Segmentation: Techniques, Algorithms & ApplicationsUnit 8 of Image Processing and Pattern Recognition covers image segmentation—partitioning an image into meaningful regions/objects using spatial, spectral, or contextual cues. Learn thresholding, edge-based, region-based, and clustering methods, their mathematical foundations, and real-world applications in medical ima15 min readUnit 9
Feature Extraction: Methods, Techniques & ApplicationsUnit 9 of Image Processing and Pattern Recognition explores how to extract meaningful features from images—key descriptors that distinguish objects, shapes, and patterns for tasks like object recognition, medical diagnosis, and biometrics. This note covers feature types (edges, textures, corners, histograms), extractio14 min readUnit 10
Pattern Recognition: Classifiers, Features & Real-World SystemsUnit 10 of Image Processing and Pattern Recognition covers supervised/unsupervised learning, feature extraction, classifier design (Bayes, SVM, NN), and real-world applications in biometrics, medical diagnosis, and autonomous systems—with visual workflows, math traces, and Nepalese examples like Ncell’s fraud detection10 min read