EE 7730: Image Analysis I

EE 7730: Image Analysis I PowerPoint PPT Presentation

256x256 - Found on very cheap cameras, this resolution is so low that ... 4064x2704 - A top-of-the-line digital camera with 11.1 megapixels takes pictures at this ...

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EE 7730: Image Analysis I

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Slide 1:EE 7730: Image Analysis I


Slide 2:EE 7730

Dr. Bahadir K. Gunturk Office: EE 225 Email: [email protected] Tel: 8-5621 Office Hours: MW 2:40 – 4:30 Class Hours: MWF 1:40 – 2:30 (CEBA-3129)

Slide 3:EE 7730

We will learn the fundamentals of digital image processing, computer vision, and digital video processing Lecture slides, problems sets, solutions, study materials, etc. will be posted on the class website. [] Textbook is not required. References: Gonzalez/Woods, Digital Image Processing, Prentice-Hall, 2/e. Forsyth/Ponce, Computer Vision: A Modern Approach, Prentice-Hall. Duda, Hart, and Stork, Pattern Classification, John Wiley&Sons, 2001. Tekalp, Digital Video Processing, 1995 Jain, Fundamentals of Digital Image Processing, Prentice-Hall.

Slide 4:Grading Policy

Your grade will be based on Problem Sets + Semester Project: 35% Midterm: 30% Final: 35% Problem Sets Theoretical problems and MATLAB assignments 4-5 Problem Sets Individually or in two-person teams Semester Project Each student will give a 15 minute presentation

Slide 5:EE 7740 Image Analysis II

Semester Project Possible project topics will be provided in a month Projects will be done individually Projects will involve MATLAB or C/C++ implementation Each student will give a 15 minute presentation at the end of the semester

Slide 6:EE 7740 Image Analysis II

Image Analysis I - Outline Digital image fundamentals 2D Fourier transform, sampling, Discrete Cosine Transfrom Image enhancement Human visual system and color image processing Image restoration Image compression Image segmentation Morphology Introduction to digital video processing

Slide 7:Digital Image Acquisition

Sensor array When photons strike, electron-hole pairs are generated on sensor sites. Electrons generated are collected over a certain period of time. The number of electrons are converted to pixel values. (Pixel is short for picture element.)

Slide 8:Digital Image Acquisition

Two types of quantization: There are finite number of pixels. (Spatial resolution) The amplitude of pixel is represented by a finite number of bits. (Gray-scale resolution)

Slide 9:Digital Image Acquisition

Slide 10:Digital Image Acquisition

256x256 - Found on very cheap cameras, this resolution is so low that the picture quality is almost always unacceptable. This is 65,000 total pixels. 640x480 - This is the low end on most "real" cameras. This resolution is ideal for e-mailing pictures or posting pictures on a Web site. 1216x912 - This is a "megapixel" image size -- 1,109,000 total pixels -- good for printing pictures. 1600x1200 - With almost 2 million total pixels, this is "high resolution." You can print a 4x5 inch print taken at this resolution with the same quality that you would get from a photo lab. 2240x1680 - Found on 4 megapixel cameras -- the current standard -- this allows even larger printed photos, with good quality for prints up to 16x20 inches. 4064x2704 - A top-of-the-line digital camera with 11.1 megapixels takes pictures at this resolution. At this setting, you can create 13.5x9 inch prints with no loss of picture quality.

Slide 11:Matrix Representation of Images

A digital image can be written as a matrix

Slide 12:Image Resolution

Slide 13:Bit Depth – Grayscale Resolution

8 bits 7 bits 6 bits 5 bits

Slide 14:Bit Depth – Grayscale Resolution

4 bits 3 bits 2 bits 1 bit

Slide 15:Digital Color Images

Slide 16:Video

= vertical position = horizontal position = frame number ~24 frames per second.

Slide 17:Why do we process images?

To facilitate their storage and transmission To prepare them for display or printing To enhance or restore them To extract information from them To hide information in them

Slide 18:Image Processing Example

Image Restoration Original image Blurred Restored by Wiener filter

Slide 19:Image Processing Example

Noise Removal Noisy image Denoised by Median filter

Slide 20:Image Processing Example

Image Enhancement Histogram equalization

Slide 21:Image Processing Example

Artifact Reduction in Digital Cameras Original scene Captured by a digital camera Processed to reduce artifacts

Slide 22:Image Processing Example

Image Compression Original image 64 KB JPEG compressed 15 KB JPEG compressed 9 KB

Slide 23:Image Processing Example

Object Segmentation “Rice” image Edges detected using Canny filter

Slide 24:Image Processing Example

Resolution Enhancement

Slide 25:Image Processing Example

Watermarking Original image Hidden message Generate watermark Watermarked image Secret key

Slide 26:Image Processing Example

Face Recognition Surveillance video Search in the database

Slide 27:Image Processing Example

Fingerprint Matching

Slide 28:Image Processing Example


Slide 29:Image Processing Example

Texture Analysis and Synthesis Pattern repeated Computer generated Photo

Slide 30:Image Processing Example

Face detection and tracking

Slide 31:Image Processing Example

Face Tracking

Slide 32:Image Processing Example

Object Tracking

Slide 33:Image Processing Example

Virtual Controls

Slide 34:Image Processing Example

Visually Guided Surgery

Slide 35:Cameras

First camera was invented in 16th century. It used a pinhole to focus light rays onto a wall or translucent plate. Take a box, prick a small hole in one of its sides with a pin, and then replace the opposite side with a translucent plate. Place a candle on the pinhole side, you will see an inverted image of the candle on the translucent plate.

Slide 36:Perspective Projection

Perspective projection equations

Slide 37:Pinhole Camera Model

If the pinhole were really reduced to a point, exactly one light ray would pass through each point in the image plane. In reality, each point in the image place collects light from a cone of rays.

Slide 38:Pinhole Cameras

Pinhole too big - many directions are averaged, blurring the image Pinhole too small - diffraction effects blur the image

Slide 39:Cameras With Lenses

Most cameras are equipped with lenses. There are two main reasons for this: To gather light. For an ideal pinhole, a single light ray would reach each point the image plane. Real pinholes have a finite size, so each point in the image plane is illuminated by a cone of light rays. The larger the hole, the wider the cone and the brighter the image => blurry pictures. Shrinking the pinhole produces sharper images, but reduces the amount of light and may introduce diffraction effects. To keep the picture in sharp focus while gathering light from a large area.

Slide 40:Compound Lens Systems

Slide 41:Real Lenses

Rays may not focus at a single point. Spherical aberration Spherical aberration can be eliminated completely by designing aspherical lenses.

Slide 42:Real Lenses

Chromatic aberration The index of refraction is a function of wavelength. Light at different wavelengths follow different paths.

Slide 43:Real Lenses

Chromatic Aberration

Slide 44:Real Lenses

Special lens systems using two or more pieces of glass with different refractive indeces can reduce or eliminate this problem. However, not even these lens systems are completely perfect and still can lead to visible chromatic aberrations.

Slide 45:Real Lenses

Barrel Distortion & Pincushion Distortion Stop (Aperture) Causes of distortion (normal) Chief ray

Slide 46:Real Lenses

Barrel Distortion & Pincushion Distortion Distorted Corrected

Slide 47:Real Lenses

Vignetting effect in a two-lens system. The shaded part of the beam never reaches the second lens. The brightness drop in the image perimeter.

Slide 48:Real Lenses

Optical vignetting example. Left: f/1.4. Right: f/5.6. f-number focal length to diameter ratio

Slide 49:Real Lenses

Long exposure time Short exposure time

Slide 50:Real Lenses

Flare Hood may prevent flares

Slide 51:Real Lenses


Slide 52:Compound Lens Systems

Slide 53:Digital Camera Pipeline

Auto-exposure algorithms measure brightness over discrete scene regions to compensate for overexposed or underexposed areas by manipulating shutter speed and/or aperture size. The net goals here are to maintain relative contrast between different regions in the image and to achieve a good overall quality. (from Katz and Gentile)

Slide 54:Digital Camera Pipeline

Auto-focus algorithms divide into two categories. Active methods use infrared or ultrasonic emitters/receivers to estimate the distance between the camera and the object being photographed. Passive methods, on the other hand, make focusing decisions based on the received image in the camera.

Slide 55:Digital Camera Pipeline

Lens distortion correction This set of algorithms accounts for the physical properties of lenses that warp the output image compared to the actual scene the user is viewing. Different lenses can cause different distortions; for instance, wide-angle lenses create a "barrel distortion", while telephoto lenses create a "pincushion distortion“.

Slide 56:Digital Camera Pipeline

Vignetting (shading distortion) reduces image brightness in the area around the lens. Chromatic aberration causes color fringes around an image. The media processor needs to mathematically transform the image in order to correct for these distortions.

Slide 57:Digital Camera Pipeline

Sensor's output needs to be gamma-corrected to account for eventual display, as well as to compensate for nonlinearities in the sensor's capture response.

Slide 58:Digital Camera Pipeline

Image stability compensation, or hand-shaking correction is another area of preprocessing. Here, the processor adjusts for the translational motion of the received image, often with the help of external transducers that relate the real-time motion profile of the sensor.

Slide 59:Digital Camera Pipeline

White balance is another important stage of preprocessing. When we look at a scene, regardless of lighting conditions, our eyes tend to normalize everything to the same set of natural colors. For instance, an apple looks deep red to us whether we're indoors under fluorescent lighting, or outside in sunny weather. However, an image sensor's "perception" of color depends largely on lighting conditions, so it needs to map its acquired image to appear natural in its final output. This mapping can be done either manually or automatically.

Slide 60:Digital Camera Pipeline

Demosaicking (Bayer interpolation) estimates missing color samples in single-chip cameras.

Slide 61:Digital Camera Pipeline

In this stage, the interpolated RGB image is transformed to the targeted output color space (if not already in the right space). For compression or display to a television, this will usually involve an RGB?YCbCr matrix transformation, often with another gamma correction stage to accommodate the target display. The YCbCr outputs may also be chroma subsampled at this stage to the standard 4:2:2 format for color bandwidth reduction with little visual impact.

Slide 62:Digital Camera Pipeline

Postprocessing In this phase, the image is perfected via a variety of filtering operations before being sent to the display and/or storage media. For instance, edge enhancement, pixel thresholding for noise reduction, and color-artifact removal are all common at this stage.

Slide 63:Digital Camera Pipeline

Display / Compress / Store Once the image itself is ready for viewing, the image pipe branches off in two different directions. In the first, the postprocessed image is output to the target display, usually an integrated LCD screen (but sometimes an NTSC/PAL television monitor, in certain camera modes). In the second, the image is sent to the media processor's compression algorithm, where industry-standard compression techniques (JPEG, for instance) are applied before the picture is stored locally in some storage medium (e.g., Flash memory card).

Slide 64:Review: Linear Systems

We define a system as a unit that converts an input function into an output function. System operator Independent variable

Slide 65:Linear Systems

Then the system H is called a linear system. where fi(x) is an arbitrary input in the class of all inputs {f(x)}, and gi(x) is the corresponding output. Let If A linear system has the properties of additivity and homogeneity.

Slide 66:Linear Systems

for all fi(x) ?{f(x)} and for all x0. The system H is called shift invariant if This means that offsetting the independent variable of the input by x0 causes the same offset in the independent variable of the output. Hence, the input-output relationship remains the same.

Slide 67:Linear Systems

The operator H is said to be causal, and hence the system described by H is a causal system, if there is no output before there is an input. In other words, A linear system H is said to be stable if its response to any bounded input is bounded. That is, if where K and c are constants.

Slide 68:Linear Systems

?(a) a x ?(x-a) A unit impulse function, denoted ?(a), is defined by the expression

Slide 69:Linear Systems

A unit impulse function, denoted ?(a), is defined by the expression Then

Slide 70:Linear Systems

is called the impulse response of H. The term From the previous slide It states that, if the response of H to a unit impulse [i.e., h(x, ?)], is known, then response to any input f can be computed using the preceding integral. In other words, the response of a linear system is characterized completely by its impulse response.

Slide 71:Linear Systems

and the integral becomes If H is a shift-invariant system, then This expression is called the convolution integral. It states that the response of a linear, fixed-parameter system is completely characterized by the convolution of the input with the system impulse response.

Slide 72:Linear Systems

Convolution of two functions is defined as In the discrete case

Slide 73:Linear Systems

is a linear filter. In the 2D discrete case

Slide 74:Example

* =

Slide 75:Example

* =

Slide 76:Try MATLAB

f=imread(‘saturn.tif’); figure; imshow(f); [height,width]=size(f); f2=f(1:height/2,1:width/2); figure; imshow(f2); [height2,width2=size(f2); f3=double(f2)+30*rand(height2,width2); figure;imshow(uint8(f3)); h=[1 1 1 1; 1 1 1 1; 1 1 1 1; 1 1 1 1]/16; g=conv2(f3,h); figure;imshow(uint8(g));

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