Upload
yazid
View
30
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Image Processing Lecture 2. Gaurav Gupta Shobhit Niranjan. Today. Image Formation (More Details) Camera Models Perspective Geometry Color Models. Human Visual System (HVS): The Eye. Image is formed on retina Photoreceptors (rods and cones) are stimulated and generate visual signal - PowerPoint PPT Presentation
Citation preview
Image Processing Lecture 2
-Gaurav Gupta-Shobhit Niranjan
Today
Image Formation (More Details) Camera Models Perspective Geometry Color Models
Human Visual System (HVS): The Eye Image is formed on
retina Photoreceptors (rods
and cones) are stimulated and generate visual signal
Received and processed by brain (Cortex)
Pin Hole Camera Model
Light enters through small hole. Image plane is placed between focal point
and object (to have “non-inverted” projection)
Perspective Geometry
Mapping from R3->R2
Convention image coordinate (u,v), object coordinate (x,y,z)
u = (f/z)x ; v = (f/z)y
f = focal length (by geometry)
The linear version is ( S = scale factor)
Contd..
Concept of Vanishing Line, Point and Horizon is important for Reconstruction from 2D image to 3D information
Vanishing point : The point where parallel lines at particular direction meet .
Two sets of parallel lines in different directions will give two vanishing points.
Two vanishing points form a vanishing line for the collection of parallel planes defined by these two sets of parallel lines.
The Horizon
Vanishing Line for ground plane Anything below it will be below horizon and
above it will be above horizon Different heights of viewer ?? What would be
affect on the horizon?
Interpretation of Calibration matrix It gives you location of the vanishing point. The homogeneous coordinate (x,y,0) is the
ideal point or point at infinity in the direction of (x,y). (how??) (guess how to represent point at infinity in x direction), where will this appear in Image
Camera Calibration
Why? To find how the object coordinated are projected in image plane
Parameters: Intrinsic & Extrinsic Model
contd..
From the figure, hence, In other words, =>
In some cases focal lengths can be different in x and y direction fu , fv
f, uo,vo are intrinsic parameters
Extrinsic Parameters
In general, the three dimensional world coordinates of a point will not be specfied in a frame whose origin is at the centre of projection
So we can transform by a linear transformation ( Rotation and Scale)
Where T is 4x4 transformation matrix, R pure rotation (rigid body), t is the rigid body translation
Types of Image Transformation (or Deformation)
Color Models
Three independent quantities are used to describe any particular color. (HVS)
Achromatic light has no color - its only attribute is quantity or intensity. Greylevel is a measure of intensity.
On the other hand, brightness or luminance is determined by the perception of the color
Color depends primarily on the reflectance properties of an object.
contd…
The tristimulus theory of color perception seems to imply that any color can be obtained from a mix of the three primaries, red, green and blue
Color models provide a standard way to specify a particular color and specifies a 3D coordinate system or subspace
Any color that can be specified using a model will correspond to a single point within the subspace it defines
RGB Model
CMY Model
RGB model asks what is added to black to get a particular color, the CMY (cyan-magenta-yellow) model asks what is subtracted from white.
Appropriate to absorption of colors, used in printing devices and filters
HSI Model
The hue is determined by the dominant wavelength The saturation is determined by the excitation purity,
and depends on the amount of white light mixed with the hue
the intensity is determined by the actual amount of light
YIQ
YIQ (luminance-inphase-quadrature) is Recoding of RGB for color television
Some points to think about..
what is the best way to apply the image processing techniques color images ?
Which color space to choose ? If we want to increase the contrast in a dark
image by histogram equalization, can we just equalize each color independently?
Some quick facts
Normally Image is array of RGB values of pixels in BGR order
N-bit , m channel Image => It has m color spaces having N bit quantized data per color space per pixel (Ex. 8 bit RGB Image)
Very Simple data structure is Bitmap Format and Raw
JPEG widely used to store/capture images but it is compressed form
Home Work
Install OpenCV (Intel Open Source Lib) http://sourceforge.net/projects/opencvlibrary Check its documentation and see how image
is described by IplImage data structurehttp://www.cs.bham.ac.uk/resources/courses/robotics/doc/opencvdocs/
Try to write and run sample programs given
in OpenCV tutorial and see for different images loss in JPEG format I will mail you.
ThE eNd