Transcript Document
IT – 472 Digital Image Processing Practical details • Lectures – CEP 205 – Monday 09:30 – 10:30 – Wednesday, Thursday 08:30 – 09:30 • Lab – Lab 205 – Wednesday 14:00 – 16:00 • Grades: – Based on 1 or 2 internals, finals, assignments, labs, paper reading etc. – Can change! References & Prerequisites • References: – Digital Image Processing, by Gonzalez and Woods – Fundamentals of Digital Image Processing, by Anil Jain • Prerequisites – Linear algebra – Signals and systems: 1D Fourier transforms, convolution, sampling theorem. Digital Image Processing • DIP: Processing multi-dimensional signals. • What all processes? – Enhancement : Makes the signal more conducive for a specific task. • Noise removal, Contrast stretching, Change brightness, Sharpening – Restoration: Tries to undo a degradation process. Some reasons for degradation: • Camera impulse response is not an impulse • Relative motion between object and camera Digital Image Processing – Compression: Always easier to handle smaller data. • Lossy and Lossless compression. – Segmentation: To separate object of interest from ‘background’ – Morphological Processes: Nonlinear Processing based on set theoretic concepts. • Filtering, computing region descriptors What after DIP • DIP allows you to explore: – – – – Computer Vision Robotics Pattern Recognition Understanding Human Visual System • Subjects offered at DA-IICT: – Computer Vision (Autumn) – Numerical Differential Geometry in CV (Winter) – Pattern Recognition (Autumn) – Image Analysis (Winter, under construction!) Digital Images • Image – 2 dimensional function f(x,y) • Digital image – Sampled and Quantized image, represented by a matrix I(x,y) of size, say m x n. • Each element is called a Pixel. • Grey level digital image – the values of I(x,y) are discrete, usually from 0 to 255, 0 representing black, 255 representing white. Digital Image Acquisition Image formation model • The imaging system senses amount of energy reflected/allowed to pass through by the object. – 0 < f(x,y) < ∞ • The energy reflected by the object comes from an illumination source. • If i(x,y) is the energy incident at point (x,y) of the object and r(x,y) is the reflectivity of that point, then f(x,y) = i(x,y)r(x,y) Sampling and Quantization • Digitizing the coordinate value – sampling. • Digitizing the amplitude – quantization. • For an image of size m x n, with L=2k different grey levels, it requires m x n x k bits of storage: – m = 1024, n = 1024, k = 8 (L = 256) – At 1 Mbps, 8 secs to get 1 image! 1 Mbyte Resolution • Spatial resolution – smallest distinguishable detail in the image. – Higher sampling Higher spatial resolution • Grey level resolution – smallest distinguishable change in grey level