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02-05-2011, 11:22 AM
Post: #1
Computer Vision & Image Processing FULL REPORT
Presented By
Saurabh P. Bahurupi


.docx  computer vision & image processing.docx (Size: 2.12 MB / Downloads: 260)
ABSTRACT
Computer Vision & Image Processing are the two distinct fields which are closely co-related with each other. In computer vision, we analyze the input image and obtain an understanding or make a decision. The goal is the emulation of the visual capability of human beings using computers. In other words, computer vision is making the machine see as we do! In image processing, we attempt to manipulate the input image to obtain a “better” image.
In fact where image processing ends and fields computer vision start. Computer vision is the construction of explicit, meaningful descriptions of physical objects from their images. The outputs of computer vision are a description or an interpretation or some quantitative measurements of the structures in the 3D scene. Image processing and pattern recognition are among many techniques computer vision employs to achieve its goals. Computer Vision, also known As Image Analysis, Scene Analysis, Image Understanding, Image Processing, Computer Graphics, Pattern Recognition, Robotics, Artificial Intelligence.
Following are the area where we find application of Computer Vision & Image Processing Robotics, Medicine, Security, Transportation, Industrial, automation, Image/video databases, pattern reorganization and many more.
Chapter 1
INTRODUCTION
1.1 Image

Before introducing the image processing we first see what is image. “An image (from Latin imago) is an artifact, usually two dimensional (a picture), that has a similar appearance to some subject—usually a physical object or a person” [2].
An image defined in the "real world" is considered to be a function of two real variables, for example, a(x,y) with a as the amplitude (e.g. brightness) of the image at the real coordinate position (x,y).A digital image a[m,n] described in a 2D discrete space is derived from an analog image a(x,y) in a 2D continuous space through a sampling process that is frequently referred to as digitization. The 2D continuous image a(x,y) is divided into N rows and M columns.The intersection of a row and a column is termed a pixel. The value assigned to the integer coordinates [m,n] with {m=0,1,2,...,M1}and {n=0,1,2,...,N1} is a[m,n].
Fig 1.1 Image Acquisition
Common image formats include:
– 1 sample per point (B&W or Grayscale)
– 3 samples per point (Red, Green, and Blue)
– 4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity)
1.2 Image Processing
Image Processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image processing techniques involve treating the image as a two dimensional signal and applying standard signal processing techniques to it.
An image processing operation typically defines a new image g in terms of an existing image f. The simplest operations are those that transform each pixel in isolation. These pixel to pixel operations can be written:
1.3 Computer Vision
Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi dimensional data from a medical scanner. Computer vision is concerned with modeling and Computer replicating human vision using computer software and hardware. Computer vision (image understanding) is a discipline that studies how to reconstruct, interpret and understand a 3Dscene from its 2D images in terms of the properties of the structures present in the scene.fig.1.3 shows the summery view of Computer Vision operation.
Computer vision overlaps significantly with the fields shown in Fig 1.4. The important field as given below
1.3.1 Image processing
1.3.2 Pattern recognition, and
1.3.3 Photogrammetry
1.3.1 Image processing

Image processing focuses on image manipulation to enhance image quality, to restore an image or to compress/decompress an image. Most computer vision algorithms usually assumes a significant amount of image processing has taken place to improve image quality.
1.3.2 Pattern recognition
Pattern recognition studies various techniques (such as statistical techniques, neural network, support vector machine, etc..) to recognize/classify different patterns
Pattern recognition techniques are widely used in computer vision.
1.3.3 Photogrammetry
Photogrammetry is concerned with obtaining accurate and reliable measurements from images. It focuses on accurate mensuration. Camera calibration and 3D reconstruction are two areas of interest to both computer vision and photogrammetry researchers.
21-06-2011, 10:15 AM
Post: #2
RE: Computer Vision & Image Processing FULL REPORT

.ppt  Computer Vision & Image processing.ppt (Size: 2.58 MB / Downloads: 197)
Computer Vision & Image processing
Introduction
Introducing the new subject 31049 Computer Vision & Image Processing
Guest Lecturer with previous experience in teaching & research in the area
Osteons
Compact bone is comprised of many units called osteons: they consist of a central canal surrounded by closely packed concentric layers called lamallae.
This is an image of some osteons, specimen is from a 32yo male. Image is a ~2.8x3.5mm portion of a contact micro-radiograph taken from a 100micrometre thick, un-embedded, hard-ground section. Black areas are voids, white = high mineral density (maybe a couple of grains of carborundum can be seen).
Attribution: David Thomas, Dental Sciences, University of Melbourne
Near IR Imaging
Hot strip mill temperature measurement
Near IR Imaging (2)
Thermal Map
Attribution: C. Lampe: “A Multi-Processor temperature profiling system for real time control in a hot strip rolling mill” M.Eng. Thesis RMIT 1995
Topics for Discussion Today
Subject objectives
Fundamental Issues
Applications
Towards image understanding
CV & IP Tools
A Simple Beginning
Conclusions
Subject objectives
adequate background knowledge about computer vision and image processing
practical knowledge and skills about computer vision and image processing tools
necessary knowledge to design and implement a prototype of a computer vision application
Fundamental Issues
An imaging system
Fundamental Issues (2)
Resolution - the smallest feature size on your object that the imaging system can distinguish
Field of view - the area of inspection that the camera can acquire
Working distance - the distance from the front of the camera lens to the object under inspection
Sensor size - the size of a sensor's active area
Depth of field - the maximum object depth that remains in focus
Fundamental Issues (3)
Resolution
Resolution indicates the amount of object detail that the imaging system can reproduce. You can determine the required resolution of your imaging system by measuring in real-world units the size of the smallest feature you need to detect in the image.
To make accurate measurements, a minimum of two pixels should represent the smallest feature you want to detect in the digitized image. In the picture , the narrowest vertical bar (w) should be at least two pixels wide in the image. This information can assist to select the appropriate camera and lens for the imaging application.
Fundamental Issues (4)
Sensor resolution is the number of columns and rows of CCD pixels in the camera sensor. To compute the sensor resolution, you need to know the field of view (FOV).
The FOV is the area under inspection that the camera can acquire. The horizontal and vertical dimensions of the inspection area determine the FOV. Make sure the FOV encloses the object you want to inspect. Once you know the FOV, you can use the following equation to determine your required sensor resolution: sensor resolution = (FOV/resolution) x 2 = (FOV/size of smallest feature) x 2
Fundamental Issues (5)
Determine the focal length of your lens. A lens is primarily defined by its focal length. This picture illustrates the relationship between the focal length of the lens, field of view, sensor size, and working distance which is the distance from the front of the lens to the object under inspection. focal length = sensor size x working distance / FOV
Fundamental Issues (6)
Lighting issues
One of the most important aspects of setting up your imaging environment is proper illumination. Images acquired under proper lighting conditions make your image processing software development easier and overall processing time faster. One objective of lighting is to separate the feature or part you want to inspect from the surrounding background by as many gray levels as possible. Another goal is to control the light in the scene. Set up your lighting devices so that changes in ambient illumination-such as sunlight changing with the weather or time of day-do not compromise image analysis and processing.
Fundamental Issues (7)
Backlighting is another lighting technique that can help improve the performance of your vision system. If you can solve your application by looking at only the shape of the object, you may want to create a silhouette of the object by placing the light source behind the object you are imaging. By lighting the object from behind, you create sharp contrasts which make finding edges and measuring distances fast and easy. This picture shows a stamped metal part acquired in a setup using backlighting.
Fundamental Issues (8)
Perspective
Perspective errors occur when the camera axis is not perpendicular to the object under inspection. The figures show both an ideal camera position (I.e. vertical) and a camera imaging an object from an angle.
Fundamental Issues (9)
Perspective and Distortion Errors
Try to position your camera perpendicular to the object under inspection to reduce perspective errors. Integration constraints may prevent you from mounting the camera perpendicular to the scene. Under these constraints, you can still take precise measurements by correcting the perspective errors with spatial calibration techniques.

Examples of Vision Applications
Attribution: National Instruments IMAQ Vision Product Demonstration (http://www.ni.com/vision)


Examples:
Battery Clamp
Spark Plug Gap Measurement
Blister Pack Inspection
PCB Inspection
Examples of Vision Applications (2)
Battery Clamp
Examples of Vision Applications (3)
Spark Plug Gap Measurement
Examples of Vision Applications (4)
Blister Pack Inspection: Ensuring that blister packs contain the correct number and type of pills before they reach pharmacies, ensuring the integrity of the product and increase the yield of production by automating the inspection of blister pack contents.







Acquire colour images of the blister packs. Use colour location to count the number of green areas in the image. With colour location, you create a model or template that represents the colours that you are searching. Then the machine vision application searches for the model in each acquired image and calculates a score for each match. The surface area of each pill in the pack must be at least 50% green to pass inspection.

Examples of Vision Applications (5)
Blister Pack Inspection (cont’d)
Examples of Vision Applications (6)
PCB Inspection
To ensure that components are present and at the correct orientation on a PCB.











Examples of Vision Applications (7)
PCB Inspection (cont’d): Colour information simplifies a monochrome problem by improving contrast or separation of the components from the background. Colour pattern matching can distinguish objects from the background more efficiently than grayscale pattern matching.
This example uses rotation-invariant pattern matching because it can detect the components regardless of their orientations. You can use the orientation information to determine the correct placement of orientation-sensitive components, such as capacitors or diodes.


Towards image understanding
Computer Vision: making useful decisions about real physical objects and scenes based on sensed images

To get to that understanding we need to process images: hence Image Processing is a step towards this understanding

Image Processing issues:
Image Statistics & Histograms
Image Enhancement
Image Restoration
Image Analysis: Edge Detection & Feature extraction
Representation & Description
Pattern Recognition
Histograms
The cumulative histogram of a grey-level image f(w,h) is a function H(k) which provides the total number pixels (number of occurrences) that have grey-level less than the value k.




Where card stands for the cardinality (i.e. number of pixels) of a set.
The histogram of a grey-level image f(w,h) is a table h(k) which is the discrete difference of the cumulative histogram. It provides the total number pixels (number of occurrences) that have a specific grey-level value k.
Attribution: http://www.khoral.com
Histograms (2)


Histogram Equalisation


CV & IP Tools
NIH Image is a public domain image processing and analysis program for the Macintosh. It was developed at the Research Services Branch (RSB) of the National Institute of Mental Health (NIMH), part of the National Institutes of Health (NIH). A free PC version of Image, called Scion Image for Windows, is available from Scion Corporation. Image can acquire, display, edit, enhance, analyse and animate images. It reads and writes TIFF, PICT, PICS and MacPaint files, providing compatibility with many other applications, including programs for scanning, processing, editing, publishing and analysing images. It supports many standard image processing functions, including contrast enhancement, density profiling, smoothing, sharpening, edge detection, median filtering, and spatial convolution with user defined kernels.
Image can be used to measure area, mean, centroid, perimeter, etc. of user defined regions of interest. It also performs automated particle analysis and provides tools for measuring path lengths and angles. Spatial calibration is supported to provide real world area and length measurements. Density calibration can be done against radiation or optical density standards using user specified units. Results can be printed, exported to text files, or copied to the Clipboard.

CV & IP Tools (2)
Khoral Inc:It started out as Khoros about 10 years ago, for UNIX-based X-systems, has become available for the Windows platform as well. Costs money today!
Their online DIP course is very good: http://www.khoral.com/contrib/contrib/di...index.html
CV & IP Tools (3)
UTHSCSA ImageTool
UTHSCSA ImageTool (IT) is a free image processing and analysis program for Microsoft Windows 9x, Windows ME or Windows NT. IT can acquire, display, edit, analyse, process, compress, save and print gray scale and colour images.IT can read and write over 22 common file formats including BMP, PCX, TIF, GIF and JPEG. Image analysis functions include dimensional (distance, angle, perimeter, area) and gray scale measurements (point, line and area histogram with statistics). ImageTool supports standard image processing functions such as contrast manipulation, sharpening, smoothing, edge detection, median filtering and spatial convolutions with user-defined convolution masks. ImageTool was designed with an open architecture that provides extensibility via a variety of plug-ins. Support for image acquisition using either Adobe Photoshop plug-ins or Twain scanners is built-in. Custom analysis and processing plug-ins can be developed using the software development kit (SDK) provided (with source code). This approach makes it possible to solve almost any data acquisition or analysis problem with IT.
CV & IP Tools (4)
Intel open source computer vision library OpenCV

CVIPtools: is a UNIX/Win32-based software package developed \ under the continuing direction of Dr. Scott E Umbaugh One of the primary purposes of the CVIPtools development is to allow students, faculty, and other researchers to explore the power of computer processing of digital images.
CVIPtools is a collection of computer imaging tools providing services to the users at three layers. At the bottom level are the CVIPtools libraries (the application programming interface). Based on the CVIPtools libraries are the cviptcl and cvipwish shells. The cviptcl shell is an extension of Tcl with additional CVIP capabilities. With cviptcl, the user can either use the command line for interactive image processing, or write cviptcl shell scripts for batch processing. The cvipwish shell is the extension of cviptcl with the added functionality for building a graphical user interface (GUI) which allows even the casual computer users to experiment with many of the sophisticated tools available to computer imaging specialists without the need for any knowledge of computer programming.



Topics in Course
Image formation. Human vision. Computer vision.
Image representation. Analog and digital images. Common image and video formats. Image compression. JPEG and MPEG.
Image acquisition. Image acquisition systems. Cameras and frame grabbers.
Image processing. Image enhancement. Convolution. Filtering. Edge detection. Texture analysis. Labelling. Contour tracing. Image morphology. Image segmentation.
Image analysis. Feature extraction. Geometrical features. Hough transform.
Image sequences. Motion detection. Optical flow. Background subtraction. Feature tracking.
Pattern recognition. Object classification. Statistical, neural networks, symbolic classifiers.
Computer Vision. Model-based vision. Applications in industrial quality inspection, video surveillance, robotics, medicine, multimedia .

A Simple Beginning
Paradigm for an image processing code:
Declarations

File In: Bring in image into Array I[r,c]

Processing on Array I[r,c] to produce
Array O[r,c]

File Out: Output Array O[r,c] as image


Conclusions
An overview of what is ahead for you in this exciting field.
You might like to experiment with the above simple code to attempt to gain the histogram for an image, and try out “histogram equalisation”
20-04-2012, 01:54 PM
Post: #3
RE: Computer Vision & Image Processing FULL REPORT
Computer Vision & Image Processing


.ppt  edge_detection.ppt (Size: 4.96 MB / Downloads: 121)


Introduction to image analysis (computer vision)

Its connection with psychology and neuroscience
Why is image analysis difficult?

Theory of edge detection

Gradient operator
Advanced operators

Applications

Road/sign detection in intelligent driving systems
Pupil detection in iris recognition systems


Computer Vision: the Grand Challenge

Teach a computer to see is nontrivial at all
Unlike binary images, grayscale/color images acquired by the sensor are often easy to understand by human being but difficult for a machine or a robot
There are lots of interesting problems in the field of computer vision (image analysis)
Image segmentation, image understanding, face detection/recognition, object tracking …


Edge detection

Gradient operator
Advanced operators
Image segmentation
Basic techniques
Texture segmentation*
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