Scan conversion algorithms convert graphical primitives defined in terms of coordinates into pixels on a raster display. The midpoint line algorithm uses integer calculations to scan convert lines of varying slopes. Area primitives like rectangles are filled by iterating through pixels within the boundary. Anti-aliasing aims to reduce jagged edges by weighting pixel intensities based on overlap with graphical elements.
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.
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.
Using Generic Image Processing Operations to Detect a Calibration GridJan Wedekind
Camera calibration is an important problem in 3D computer vision. The problem of determining the camera parameters has been studied extensively. However the algorithms for determining the required correspondences are either semi-automatic (i.e. they require user interaction) or they involve difficult to implement custom algorithms.
We present a robust algorithm for detecting the corners of a calibration grid and assigning the correct correspondences for calibration . The solution is based on generic image processing operations so that it can be implemented quickly. The algorithm is limited to distortion-free cameras but it could potentially be extended to deal with camera distortion as well. We also present a corner detector based on steerable filters. The corner detector is particularly suited for the problem of detecting the corners of a calibration grid.
- See more at: http://figshare.com/articles/Using_Generic_Image_Processing_Operations_to_Detect_a_Calibration_Grid/696880#sthash.EG8dWyTH.dpuf
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Using Generic Image Processing Operations to Detect a Calibration GridJan Wedekind
Camera calibration is an important problem in 3D computer vision. The problem of determining the camera parameters has been studied extensively. However the algorithms for determining the required correspondences are either semi-automatic (i.e. they require user interaction) or they involve difficult to implement custom algorithms.
We present a robust algorithm for detecting the corners of a calibration grid and assigning the correct correspondences for calibration . The solution is based on generic image processing operations so that it can be implemented quickly. The algorithm is limited to distortion-free cameras but it could potentially be extended to deal with camera distortion as well. We also present a corner detector based on steerable filters. The corner detector is particularly suited for the problem of detecting the corners of a calibration grid.
- See more at: http://figshare.com/articles/Using_Generic_Image_Processing_Operations_to_Detect_a_Calibration_Grid/696880#sthash.EG8dWyTH.dpuf
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Techarea adalah perusahaan di bidang IT & Business Development berasal dari Semarang. Kami juga memberikan pelayanan servis dan jasa di berbagai segi project. Kami memiliki tim yang mempunyai pasion di bidang Software Engineering. Kami selalu memberikan hasil yang lebih dari yang Anda inginkan. Kami selalu bekerja keras untuk memberikan yang terbaik.
Perintah perintah dasar linux Operating SistemRoziq Bahtiar
presentation about base command line interface linux
please see in this link
http://roziq.com/perintah-perintah-dasar-linux-dan-konfigurasi-ip-address-cli/
eigen merupakan vector yang membuat image 1 dengan image yang lain berbeda sehingga dapat digunakan untuk mengenali image, hal ini biasa digunakan untuk melakukan proses recognition seperti pengenalan wajah dan lain sebagainya
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
1. Outline: Scan conversion
• Scan converting lines: optimising for speed.
• Issues with scan conversion.
• Area primitives.
• How OpenGL does it.
Scan converting lines
• A more advanced algorithm is the midpoint line algorithm.
• Does not use floating point arithmetic.
• Generalisation of Bresenham’s well known incremental technique.
• Works only for 0 ≤ m ≤ 1.
• Other slopes are catered for by reflection about the principal
axes of the 2D plane.
• (x0, y0) is the lower left endpoint and (xe, ye) the upper right
endpoint.
Scan converting lines
• Incremental method:
M
Q
NE
E
Choices
pixel
current
Choice for
pixel
Previous
for next
pixel
• Restrictions on the slope of the line implies that if we are at
some pixel (xp, yp), we now need to choose between only two
pixels.
Scan converting lines
M
Q
NE
E
Choices
pixel
current
Choice for
pixel
Previous
for next
pixel
• Either the east pixel, E, or the northeast pixel, NE, is chosen
depending upon which side of the midpoint, M, the crossing
point, Q, lies.
• Write the implicit functional from: f(x, y) = ax + by + γ = 0.
Scan converting lines
• Noting:
y = mx + c =
∆y
∆x
x + c ,
gives:
f(x, y) = ∆y · x − ∆x · y + ∆x · c .
• Now a = ∆y, b = −∆x and γ = ∆x · c.
• For any point on the line, f(x, y) is zero, any point above the
line, f(x, y) is negative and any point below has f(x, y) positive.
• d = f(M) = f(xp + 1, yp + 1/2) = a(xp + 1) + b(yp + 1/2) + γ.
If d is positive we choose NE, otherwise we pick E (including
when d = 0).
Scan converting lines
• So what happens to the location of the next midpoint, Mnew?
This depends on whether E or NE was chosen. If E is chosen
then the new dnew will be:
dnew = f(Mnew) = f(xp + 2, yp + 1/2)
= a(xp + 2) + b(yp + 1/2) + γ .
• dnew = dold + ∆E, ∆E = a.
Similarly ∆NE = a + b.
• First d = f(x0 + 1, y0 + 1/2) = a(x0 + 1) + b(y0 + 1/2) + γ =
f(x0, y0) + a + b
2, since this on the line d = a + b/2 = ∆y − ∆x/2.
• Using d = 2f(x, y), which will not affect the sign of the decision
variable and keep everything integer.
Scan converting lines
void MidpointLine ( int x0, int xe,
int y0, int ye, int value)
{ /* Assumes 0 <= m <= 1, x0 < xe, y0 < ye */
int x,y,dx,dy,d,incE,incNE;
dx = xe - x0;
dy = ye - y0;
d = 2*dy - dx;
incE = 2*dy;
incNE = 2*(dy-dx);
x = x0;
y = y0;
WritePixel(x,y,value);
while (x < xe) {
if (d <= 0) {
d += incE;
x++;
} else {
d += incNE;
x++;
y++;
}
WritePixel(x,y,value);
}
}
Scan converting lines
• There are several improvements that could be envisaged to the
midpoint algorithm.
• One method involves looking ahead two pixels at a time (so
called double-step algorithm).
• Another uses the symmetry about the midpoint of the whole
line, which allows both ends to be scan converted simultaneously.
• The midpoint algorithm defines that E is chosen when Q = M so
to ensure lines look the same drawn from each end the algorithm
should choose SW rather than W in the inverted version.
2. Line clipping
NE
y = y_min
x = x_min
M
E
• It is common to clip a line by a bounding rectangle (often the
virtual or real screen boundaries).
• Assume the bounding rectangle has coordinates, (xmin, ymin),
(xmax, ymax).
Line clipping
NE
y = y_min
x = x_min
M
E
• If the line intersects the left hand vertical edge, x = xmin the
intersection point of the line with the boundary is
(xmin, (m · xmin + c)).
• Start the line from (xmin, Round(m · xmin + c)).
Line clipping
x = x_min
y = y_min-0.5
y = y_min
• Assume that any of the lines pixels falling on or inside the clip
region are drawn.
• The line does not start at the point ((ymin − c)/m, ymin) where
the line crosses the bounding line.
• The first pixel is
Round
(ymin − 0.5 − c)
m
, ymin .
Line intensity
• Lines of different slopes will have different intensities on the
display, unless care is taken.
• 2 lines, both 4 pixels but the diagonal one is
√
2 times as long as
the horizontal line.
• Intensity can be set as a function of the line slope.
Scan converting area primitives
void FillRectangle ( int xmin, int xmax,
int ymin, int ymax, int value)
{
int x,y;
for (y = ymin; y <= ymax; y++) {
for (x = xmin; x <= xmax; x++) {
WritePixel(x,y,value);
}
}
}
• Scan converting objects with area is more complex than scan
converting linear objects, due to the boundaries.
A rule that is commonly used to decide what to do with edge
pixels is as follows.
• A boundary pixel is not considered part of the primitive if the
half-plane defined by the edge and containing the primitives lies
below a non-vertical edge or to the left of a vertical edge.
Filling polygons
• Most algorithms work as follows:
– find the intersections of the scan line with all polygon edges;
– sort the intersections;
– fill those points which are interior.
• The first step involves the use of a scan-line algorithm that
takes advantage of edge coherence to produce a data structure
called an active-edge table.
• Edge coherence simply means that if an edge is intersected in
scan line i, it will probably be intersected in scan line i + 1.
Other issues
• Patterns will typically be defined by some form of pixmap
pattern, as in texture mapping.
• In this case the pattern is assumed to fill the entire screen, then
anded with the filled region of the primitive, determining where
the pattern can ‘show through’.
• It is convenient to combine scan conversion with clipping in
integer graphics packages, this being called scissoring.
• Floating point graphics are most efficiently implemented by
performing analytical clipping in the floating point coordinate
system and then scan converting the clipped region.
Scan conversion: OpenGL
• OpenGL performs scan conversion efficiently behind the scenes
– typically using hardware on the graphics card.
• However, we can manipulate pixels using OpenGL with
glRasterPos2i(GLint x, GLint y) and glDrawPixels(·) – in the
labs you will code your own scan conversion routines.
• Speed is often of the essence in computer graphics, so designing
and developing efficient algorithms forms a large part of
computer graphics research.
3. Anti-aliasing
• All raster primitives outlined so far have a common problem,
that of jaggies : jaggies are a particular instance of aliasing. The
term alias originates from signal processing.
• In the limit, as the pixel size shrinks to an infinitely small dot,
these problems will be minimised, thus one solution is to
increase the screen resolution.
• Doubling screen resolution will quadruple the memory
requirements and the scan conversion time.
Anti-aliasing
• One solution to the problem involves recognising that primitives,
such as lines are really areas in the raster world.
• In unweighted area sampling the intensity of the pixel is set
according to how much of its area is overlapped by the primitive.
• More complex methods involve weighted area sampling.
• Weighted area sampling assumes a realistic model for pixel
intensity. Using a sensible weighting function, such as a cone or
Gaussian function, will result in a smoother anti-aliasing, but at
the price of even greater computational burden.
Anti-aliasing: OpenGL
• Since anti-aliasing is an expensive operation, and may not always
be required OpenGL allows the user to control the level of
anti-aliasing.
• Can be turned on using: glEnable(GL LINE SMOOTH)
• Can also use glHint(GL LINE SMOOTH HINT,GL BEST) to set quality:
GL BEST, GL FASTEST, and GL DONT CARE – hints not always
implemented – based on number of samples.
• Works by using the alpha parameter and colour blending.
Anti-aliasing of polygons treated in the same way in RGBA
mode.
Summary
• Having finished this lecture you should:
– be able to contrast different approaches and sketch their
application;
– provide simple solutions to the problems of clipping and
aliasing;
– understand how scan conversion works in OpenGL.