This slide contain description about the line, circle and ellipse drawing algorithm in computer graphics. It also deals with the filled area primitive.
This slide contain description about the line, circle and ellipse drawing algorithm in computer graphics. It also deals with the filled area primitive.
Initial Introduction of Image processing is included in these slides which contain 1. Introduction of Image Processing
2.Elements of visual perception
3. Image sensing and Quantization
4.A simple image formation model
5.Basic concept of Sampling and Quantization
Reader will find it easy to understand the topics described here in slides . A detailed description of each topic illustrated here.
Please read and if you like do comments also.... Thanks
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
Operations in Digital Image Processing + Convolution by ExampleAhmed Gad
Digital image processing operations can be either point or group.
This presentation explains both operations (point and group) and shows how convolution works by a numerical example.
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
Information Technology Department
Faculty of Computers and Information (FCI)
Menoufia University
Egypt
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
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Clipping is used in the Computer Graphics sector and My presentation Will Help you to Enrich your idea's on Clipping. Utilize my Presentation as a Reference...
To highlight the contribution made to the total image appearance by specific bits.i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes.Useful for analyzing the relative importance played by each bit of the image.
Initial Introduction of Image processing is included in these slides which contain 1. Introduction of Image Processing
2.Elements of visual perception
3. Image sensing and Quantization
4.A simple image formation model
5.Basic concept of Sampling and Quantization
Reader will find it easy to understand the topics described here in slides . A detailed description of each topic illustrated here.
Please read and if you like do comments also.... Thanks
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
Operations in Digital Image Processing + Convolution by ExampleAhmed Gad
Digital image processing operations can be either point or group.
This presentation explains both operations (point and group) and shows how convolution works by a numerical example.
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
Information Technology Department
Faculty of Computers and Information (FCI)
Menoufia University
Egypt
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Clipping is used in the Computer Graphics sector and My presentation Will Help you to Enrich your idea's on Clipping. Utilize my Presentation as a Reference...
To highlight the contribution made to the total image appearance by specific bits.i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes.Useful for analyzing the relative importance played by each bit of the image.
In Computer Graphics, Hidden surface determination also known as Visible Surface determination or hidden surface removal is the process used to determine which surfaces
of a particular object are not visible from a particular angle or particular viewpoint. In this scribe we will describe the object-space method and image space method. We
will also discuss Algorithm based on Z-buffer method, A-buffer method, and Scan-Line Method.
Identify those parts of a scene that are visible from a chosen viewing position.
Visible-surface detection algorithms are broadly classified according to whether
they deal with object definitions directly or with their projected images.
These two approaches are called object-space methods and image-space methods, respectively
An object-space method compares
objects and parts of objects to each other within the scene definition to determine which surfaces, as a whole, we should label as visible.
In an image-space algorithm, visibility is decided point by point at each pixel position on the projection plane.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Similar to The geometry of perspective projection (20)
1. The Geometry of Perspective Projection
• Pinhole camera and perspective projection
- This is the simplest imaging device which, however, captures accurately the geome-
try of perspective projection.
- Rays of light enters the camera through an infinitesimally small aperture.
- The intersection of the light rays with the image plane form the image of the object.
- Such a mapping from three dimensions onto two dimensions is called perspective
projection.
2. -2-
• A simplified geometric arrangement
- In general, the world and camera coordinate systems are not aligned.
- To simplify the derivation of the perspective projection equations, we will make the
following assumptions:
(1) the center of projection coincides with the origin of the world.
(2) the camera axis (optical axis) is aligned with the world’s z-axis.
3. -3-
(3) avoid image inversion by assuming that the image plane is in front of the cen-
ter of projection.
• Some terminology
- The model consists of a plane (image plane) and a 3D point O (center of projection).
- The distance f between the image plane and the center of projection O is the focal
length (e.g., the distance between the lens and the CCD array).
- The line through O and perpendicular to the image plane is the optical axis.
- The intersection of the optical axis with the image place is called principal point or
image center.
(note: the principal point is not always the "actual" center of the image)
4. -4-
• The equations of perspective projection
(notation: (x, y, z) → (X, Y , Z), r → R, (x′, y′, z′) → (x, y, z), r′ → r)
- Using the following similar triangles:
f r
(1) from OA′B′ and OAB: =
Z R
x y r
(2) from A′B′C′ and ABC: = =
X Y R
Xf Yf
perspective proj. eqs: x= y= z= f
Z Z
- Using matrix notation:
xh f 0 0 0 X
yh 0 f 0 0 Y
=
zh 0 0 f 0 Z
w 0 0 1 0 1
- Verify the correctness of the above matrix (homogenize using w = Z):
xh fX yh fY zh
x= = y= = z= = f
w Z w Z w
5. -5-
• Properties of perspective projection
Many-to-one mapping
- The projection of a point is not unique (any point on the line OP has the same
projection).
Scaling/Foreshor tening
- The distance to an object is inversely proportional to its image size.
6. -6-
- When a line (or surface) is parallel to the image plane, the effect of perspective
projection is scaling.
- When an line (or surface) is not parallel to the image plane, we use the term
foreshortening to describe the projective distortion (i.e., the dimension parallel to
the optical axis is compressed relative to the frontal dimension).
Effect of focal length
- As f gets smaller, more points project onto the image plane (wide-angle cam-
era).
- As f gets larger, the field of view becomes smaller (more telescopic).
Lines, distances, angles
- Lines in 3D project to lines in 2D.
- Distances and angles are not preserved.
- Parallel lines do not in general project to parallel lines (unless they are parallel
to the image plane).
7. -7-
Vanishing point
* parallel lines in space project perspectively onto lines that on extension inter-
sect at a single point in the image plane called vanishing point or point at infinity.
* (alternative definition) the vanishing point of a line depends on the orientation
of the line and not on the position of the line.
* the vanishing point of any given line in space is located at the point in the
image where a parallel line through the center of projection intersects the image
plane.
Vanishing line
* the vanishing points of all the lines that lie on the same plane form the vanish-
ing line.
* also defined by the intersection of a parallel plane through the center of projec-
tion with the image plane.
8. -8-
Orthographic Projection
- It is the projection of a 3D object onto a plane by a set of parallel rays orthogonal to
the image plane.
- It is the limit of perspective projection as f − > ∞ (i.e., f /Z − > 1)
orthographic proj. eqs: x = X, y=Y (drop Z)
- Using matrix notation:
xh 1 0 0 0 X
yh 0 1 0 0 Y
=
zh 0 0 0 0 Z
w 0 0 0 1 1
- Verify the correctness of the above matrix (homogenize using w=1):
xh yh
x= =X y= =Y
w w
• Properties of orthographic projection
- Parallel lines project to parallel lines.
- Size does not change with distance from the camera.
9. -9-
Weak Perspective Projection
- Perspective projection is a non-linear transformation.
- We can approximate perspective by scaled orthographic projection (i.e., linear trans-
formation) if:
(1) the object lies close to the optical axis.
(2) the object’s dimensions are small compared to its average distance Z from the
camera (i.e., z < Z/20)
Xf Xf Yf Yf
weak perspective proj. eqs: x= ≈ y= ≈ (drop Z)
Z Z Z Z
f
- The term is a scale factor now (e.g., every point is scaled by the same factor).
Z
- Using matrix notation:
xh f 0 0 0X
yh 0 f 0 0 Y
=
zh 0 0 0 0Z
w 0 0 0 Z1
- Verify the correctness of the above matrix (homogenize using w = Z):
xh fX yh fY
x= = y= =
w Z w Z