Image Processing Lecture 1 PDF

24/10/12 Image Processing - Dr. Ahmed Badawy Image Processing Dr. Ahmed Badawy 1 Lecture 01 1...

24/10/12 Image Processing - Dr. Ahmed Badawy Image Processing Dr. Ahmed Badawy 1 Lecture 01 1 Image Processing - Dr. Ahmed Badawy Contents Student Assessment Criteria: Attendance & Participation 5 points Assignments 10 points Quizzes 10 points Mid-Term Exams 20 points Practical / Project 15 points Final Exam 40 points Total 100 2 2  24/10/12 Image Processing - Dr. Ahmed Badawy Contents Digital image fundamentals Image enhancement in spatial domain Grey level transformation Histogram processing spatial filters Image enhancement in frequency domain: -D fourier transformation, -Other transformation, -smoothing filters, -sharpening filters -image restoration, -noise model, -estimating the degradation function -filters, -geometric transformations Image segmentation: -detection of discontinuities, -edge linking and boundary detection -thresholding -region based segmentation Morphological image processing: -operation concepts -some basic algorithms -image compression 3 3 Image Processing - Dr. Ahmed Badawy The electromagnetic spectrum In this electromagnetic spectrum, we are only able to see the visible spectrum that mainly includes seven different colors that are commonly term as (VIBGOYR). VIBGOYR stands for violet , indigo , blue , green , orange , yellow and Red. (.‫ اﻷصفر واﻷحمر‬،‫ البرتقالي‬،‫ اﻷخضر‬،‫ اﻷزرق‬،‫ النيلي‬،‫)البنفسجي‬ 4 4  24/10/12 Image Processing - Dr. Ahmed Badawy Applications of Digital Image Processing Image sharpening and restoration Medical field Remote sensing Transmission and encoding Machine/Robot vision Color processing Pattern recognition Video processing Microscopic Imaging Others 5 5 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? A digital image can be considered as a discrete (‫ )منفصل‬representation of data possessing both spatial (‫( )مكانى‬layout) and intensity (color) information. we can also consider treating an image as a multidimensional signal. 6 6  24/10/12 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.1 Image layout The two-dimensional (2-D) digital image I(m, n) represents the response of some sensor (or simply a value of some interest) at a series of fixed positions (m = 1; 2;... ;M; n = 1; 2;... ;N) 7 7 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.1 Image layout The indices m and n respectively designate the rows and columns of the image. The individual picture elements or pixels of the image are thus referred to by their 2-D (m, n) index. I(m, n) denotes the response of the pixel located at the mth row and nth column starting from a top-left image origin. 8 8  24/10/12 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.1 Image layout The individual pixel values in most images do actually correspond to some physical response in real 2-D space (e.g. the optical intensity received at the image plane of a camera or the ultrasound intensity at a transceiver). 9 9 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.2 Image color An image contains one or more color channels that define the intensity or color at a particular pixel location I(m, n). In the simplest case, each pixel location only contains a single numerical value representing the signal level at that point in the image. 10 10  24/10/12 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.2 Image color The conversion from this set of numbers to an actual (displayed) image is achieved through a color map. A color map assigns a specific shade of color to each numerical level in the image to give a visual representation of the data. The most common color map is the Greyscale, which assigns all shades of grey from black (zero) to white (maximum) according to the signal level. 11 11 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.2 Image color The greyscale is particularly well suited to intensity images, namely images which express only the intensity of the signal as a single value at each point in the region. In certain instances, it can be better to display intensity images using a false-color map. One of the main motives behind the use of false-color display rests on the fact that the human visual system is only sensitive to approximately 40 shades of grey in the range from black to white, whereas our sensitivity to color is much finer. False-color can also serve to accentuate (‫ )ابراز‬or delineate (‫ )تحديد‬certain features or structures, making them easier to identify for the human observer. This approach is often taken in medical and astronomical images. 12 12  24/10/12 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.2 Image color In this example the jet color map (as defined in Matlab) has been used to highlight the structure and finer detail of the image to the human viewer using a linear color scale ranging from dark blue (low intensity values) to dark red (high intensity values). an astronomical intensity image displayed using both greyscale and a particular false-color map 13 13 Image Processing - Dr. Ahmed Badawy 1 Representation 1.1 What is an image? 1.1.2 Image color 14 14