Digital Image Processing By Dr Kodge Bheemashankar Swami

Digital Image Processing By Dr. Kodge Bheemashankar Swami Vivekanand Mahavidyalaya, Udgir kodgebg@gmail. com By Dr. Kodge Bheemashankar G

Digital Image Processing 1. Introduction An image (from Latin imago) is an artifact, or has to do with a two-dimensional (a picture), that has a similar appearance to some subject—usually a physical object or a person. An image may be defined as a two dimensional function f (x, y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. A digital image is a representation of a two-dimensional image using ones and zeros (binary). Depending on whether or not the image resolution is fixed, it may be of vector or raster type. Without qualifications, the term "digital image“ usually refers to raster images also called bitmap images. By Dr. Kodge Bheemashankar G © Dept. of Computer Science, Swami Vivekanand Mahavidyalay, Udgir (Mah )

Digital Image Processing 1. Introduction What is DIP? Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subfield of digital signal processing, digital image processing has many advantages over analog image processing; it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. By Dr. Kodge Bheemashankar G © Dept. of Computer Science, Swami Vivekanand Mahavidyalay, Udgir (Mah )

Digital Image Processing 1. Introduction 1. 1 Elements of digital Image processing system The basic operations performed in a digital image processing systems include (1) acquisition, (2) storage, (3) processing, (4) communication and (5) display. By Dr. Kodge Bheemashankar G © Dept. of Computer Science, Swami Vivekanand Mahavidyalay, Udgir (Mah )

Digital Image Processing 1. Introduction 1. 1 Elements of digital Image processing system By Dr. Kodge Bheemashankar G © Dept. of Computer Science, Swami Vivekanand Mahavidyalay, Udgir (Mah )

Digital Image Processing 1. 1 Elements of Visual Perception. By Dr. Kodge Bheemashankar G © Dept. of Computer Science, Swami Vivekanand Mahavidyalay, Udgir (Mah )

The Human Eye • Diameter: 20 mm • 3 membranes enclose the eye – Cornea & sclera – Choroid – Retina By Dr. Kodge Bheemashankar G

The Choroid • The choroid contains blood vessels for eye nutrition and is heavily pigmented to reduce extraneous light entrance and backscatter. • It is divided into the ciliary body and the iris diaphragm, which controls the amount of light that enters the pupil (2 mm ~ 8 mm). By Dr. Kodge Bheemashankar G

The Lens • The lens is made up of fibrous cells and is suspended by fibers that attach it to the ciliary body. • It is slightly yellow and absorbs approx. 8% of the visible light spectrum. By Dr. Kodge Bheemashankar G

The Retina • The retina lines the entire posterior portion. • Discrete light receptors are distributed over the surface of the retina: – cones (6 -7 million per eye) and – rods (75 -150 million per eye) By Dr. Kodge Bheemashankar G

Cones • Cones are located in the fovea and are sensitive to color. • Each one is connected to its own nerve end. • Cone vision is called photopic (or brightlight vision). By Dr. Kodge Bheemashankar G

Rods • Rods are giving a general, overall picture of the field of view and are not involved in color vision. • Several rods are connected to a single nerve and are sensitive to low levels of illumination (scotopic or dim-light vision). By Dr. Kodge Bheemashankar G

The Fovea • The fovea is circular (1. 5 mm in diameter) but can be assumed to be a square sensor array (1. 5 mm x 1. 5 mm). • The density of cones: 150, 000 elements/mm 2 • A CCD imaging chip of medium resolution needs 5 mm x 5 mm for this number of elements By Dr. Kodge Bheemashankar G

Image Formation in the Eye • The eye lens (if compared to an optical lens) is flexible. • It gets controlled by the fibers of the ciliary body and to focus on distant objects it gets flatter (and vice versa). By Dr. Kodge Bheemashankar G

Image Formation in the Eye • Distance between the center of the lens and the retina (focal length): – varies from 17 mm to 14 mm (refractive power of lens goes from minimum to maximum). • Objects farther than 3 m use minimum refractive lens powers (and vice versa). By Dr. Kodge Bheemashankar G

Image Formation in the Eye • Example: – Calculation of retinal image of an object By Dr. Kodge Bheemashankar G

Image Formation in the Eye • Perception takes place by the relative excitation of light receptors. • These receptors transform radiant energy into electrical impulses that are ultimately decoded by the brain. By Dr. Kodge Bheemashankar G

Brightness Adaptation & Discrimination • Range of light intensity levels to which HVS (human visual system) can adapt: on the order of 1010. • Subjective brightness (i. e. intensity as perceived by the HVS) is a logarithmic function of the light intensity incident on the eye. By Dr. Kodge Bheemashankar G

Brightness Adaptation & Discrimination • The HVS cannot operate over such a range simultaneously. • For any given set of conditions, the current sensitivity level of HVS is called the brightness adaptation level. By Dr. Kodge Bheemashankar G

Electromagnetic Spectrum By Dr. Kodge Bheemashankar G

Brightness Adaptation & Discrimination • Small values of Weber ratio mean good brightness discrimination (and vice versa). • At low levels of illumination brightness discrimination is poor (rods) and it improves significantly as background illumination increases (cones). By Dr. Kodge Bheemashankar G

Brightness Adaptation & Discrimination • The typical observer can discern one to two dozen different intensity changes – i. e. the number of different intensities a person can see at any one point in a monochrome image By Dr. Kodge Bheemashankar G

Brightness Adaptation & Discrimination • Overall intensity discrimination is broad due to different set of incremental changes to be detected at each new adaptation level. • Perceived brightness is not a simple function of intensity – Scalloped effect, Mach band pattern – Simultaneous contrast By Dr. Kodge Bheemashankar G

Perceived Brightness By Dr. Kodge Bheemashankar G

Simultaneous Contrast By Dr. Kodge Bheemashankar G

Illusions By Dr. Kodge Bheemashankar G

• M-File Element Description Function definition line functions only Defines the function name, and the number and order of input and output arguments H 1 line A one line summary description of the program, displayed when you request help on an entire directory, or when you use lookfor Help text A more detailed description of the program, displayed together with the H 1 line when you request help on a specific function Function or script body Program code that performs the actual computations and assigns values to any output arguments Comments Text in the body of the program that explains the internal workings of the program By Dr. Kodge Bheemashankar G

• Basic Parts of an M-File • This simple function shows the basic parts of an M-file. Note that any line that begins with % is not executable: function f = fact(n) Function definition line % Compute a factorial value. H 1 line % FACT(N) returns the factorial of N, Help text % usually denoted by N! % Put simply, FACT(N) is PROD(1: N). Comment f = prod(1: n); Function body By Dr. Kodge Bheemashankar G

Arithmetic Operators The arithmetic operators have M-file function equivalents, as shown: • • • • Binary addition A+B plus(A, B) Unary plus +A uplus(A) Binary subtraction A-B minus(A, B) Unary minus -A uminus(A) Matrix multiplication A*B mtimes(A, B) Arraywise multiplication A. *B times(A, B) Matrix right division A/B mrdivide(A, B) Arraywise right division A. /B rdivide(A, B) Matrix left division AB mldivide(A, B) Arraywise left division A. B ldivide(A, B) Matrix power A^B mpower(A, B) Arraywise power A. ^B power(A, B) Complex transpose A‘ ctranspose(A) Matrix transpose A. ‘Bheemashankar transpose(A) By Dr. Kodge G

Image Arithmetic Functions • The following table lists the image arithmetic functions. For more complete descriptions, see their reference pages. Function • • Description imabsdiff Absolute difference of two images imadd Add two images imcomplement Complement an image imdivide Divide two images imlincomb Compute linear combination of two images immultiply Multiply two images imsubtract Subtract images G By Dr. Kodgetwo Bheemashankar