Image segmentation involves partitioning an image into regions, linear structures, or shapes. There are several main methods of region segmentation including region growing, clustering, and split and merge. Region growing starts with seed pixels and grows regions by adding similar neighboring pixels. Clustering groups pixels into clusters to minimize differences within clusters. Common clustering algorithms include K-means, ISODATA, and histogram-based clustering. Edge detection finds boundaries between regions by looking for changes in intensity values. Popular edge detectors include Sobel, Canny, and zero-crossing operators. Line and curve segments can be found from edge images using tracking or the Hough transform, which accumulates votes for parameter values of lines and curves in an image.
Introduction of metric dimension of circular graphs is connected graph , The distance and diameter , Resolving sets and location number then Examples . Application in facility location problems . is has motivation (Applications in Chemistry and Networks systems). Definitions of Certain Regular Graphs. Main Results for three graphs (Prism , Antiprism and generalized Petersen graphs .
K-means Clustering Algorithm with Matlab Source codegokulprasath06
K-means algorithm
The most common method to classify unlabeled data.
Also Checkout: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
A new algorithm is presented which determines the dimensionality and signature of a measured space. The
algorithm generalizes the Map Maker’s algorithm
from 2D to n dimensions and works the same for 2D
measured spaces as the Map Maker’s algorithm but with better efficiency. The difficulty of generalizing the
geometric approach of the Map Maker’s algorithm from 2D to 3D and then to higher dimensions is
avo
ided by using this new approach. The new algorithm preserves all distances of the distance matrix and
also leads to a method for building the curved space as a subset of the N
-
1 dimensional embedding space.
This algorithm has direct application to Scientif
ic Visualization for data viewing and searching based on
Computational Geometry.
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
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.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Introduction of metric dimension of circular graphs is connected graph , The distance and diameter , Resolving sets and location number then Examples . Application in facility location problems . is has motivation (Applications in Chemistry and Networks systems). Definitions of Certain Regular Graphs. Main Results for three graphs (Prism , Antiprism and generalized Petersen graphs .
K-means Clustering Algorithm with Matlab Source codegokulprasath06
K-means algorithm
The most common method to classify unlabeled data.
Also Checkout: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
A new algorithm is presented which determines the dimensionality and signature of a measured space. The
algorithm generalizes the Map Maker’s algorithm
from 2D to n dimensions and works the same for 2D
measured spaces as the Map Maker’s algorithm but with better efficiency. The difficulty of generalizing the
geometric approach of the Map Maker’s algorithm from 2D to 3D and then to higher dimensions is
avo
ided by using this new approach. The new algorithm preserves all distances of the distance matrix and
also leads to a method for building the curved space as a subset of the N
-
1 dimensional embedding space.
This algorithm has direct application to Scientif
ic Visualization for data viewing and searching based on
Computational Geometry.
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
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.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
1. 1
Image Segmentation
Image segmentation is the operation of partitioning an
image into a collection of connected sets of pixels.
1. into regions, which usually cover the image
2. into linear structures, such as
- line segments
- curve segments
3. into 2D shapes, such as
- circles
- ellipses
- ribbons (long, symmetric regions)
5. 5
Region Segmentation:
Segmentation Criteria
From Pavlidis
A segmentation is a partition of an image I into
a set of regions S satisfying:
1. Si = S Partition covers the whole image.
2. Si Sj = , i j No regions intersect.
3. Si, P(Si) = true Homogeneity predicate is
satisfied by each region.
4. P(Si Sj) = false, Union of adjacent regions
i j, Si adjacent Sj does not satisfy it.
6. 6
So
So all we have to do is define and implement the
similarity predicate.
But, what do we want to be similar in each region?
Is there any property that will cause the regions to
be meaningful objects?
7. 7
Main Methods of Region
Segmentation
1. Region Growing
2. Clustering
3. Split and Merge
8. 8
Region Growing
Region growing techniques start with one pixel of a
potential region and try to grow it by adding adjacent
pixels till the pixels being compared are too disimilar.
• The first pixel selected can be just the first unlabeled
pixel in the image or a set of seed pixels can be chosen
from the image.
• Usually a statistical test is used to decide which pixels
can be added to a region.
9. 9
The RGGROW Algorithm
• Let R be the N pixel region so far and P be a neighboring
pixel with gray tone y.
• Define the mean X and scatter S (sample variance) by
X = 1/N I(r,c)
S = 1/N (I(r,c) - X)
2
2
(r,c) R
(r,c) R
2
10. 10
The RGGROW Statistical Test
The T statistic is defined by
(N-1) * N
T = -------------- (y - X) / S
(N+1)
2
2
1/2
It has a T distribution if all the pixels in R and the
test pixel y are independent and identically distributed
normals (IID assumption) .
N-1
11. 11
Decision and Update
• For the T distribution, statistical tables give us the
probability Pr(T t) for a given degrees of freedom
and a confidence level. From this, pick suitable
threshold t.
• If the computed T t for desired confidence level,
add y to region R and update X and S .
• If T is too high, the value y is not likely to have arisen
from the population of pixels in R. Start a new region.
2
13. 13
Clustering
• There are K clusters C1,…, CK with means m1,…, mK.
• The least-squares error is defined as
• Out of all possible partitions into K clusters,
choose the one that minimizes D.
Why don’t we just do this?
If we could, would we get meaningful objects?
D = || xi - mk || .
k=1 xi Ck
K
2
14. 14
Some Clustering Methods
• K-means Clustering and Variants
• Isodata Clustering
• Histogram-Based Clustering and Recursive Variant
• Graph-Theoretic Clustering
17. 17
Meng-Hee Heng’s K-means Variant
1. Pick 2 points Y and Z that are furthest apart in the
measurement space and make them initial cluster means.
2. Assign all points to the cluster whose mean they are
closest to and recompute means.
3. Let d be the max distance from each point to its cluster mean
and let X be the point with this distance.
4. Let q be the average distance between each pair of means.
5. If d > q / 2, make X a new cluster mean.
6. If a new cluster was formed, repeat from step 2.
18. 18
Illustration of Heng Clustering
Y
D>q/2
q
X
1 2 3
We used this for segmentation of textured scenes.
Z
20. 20
Isodata Clustering
1. Select several cluster means and form clusters.
2. Split any cluster whose variance is too large.
3. Group together clusters that are too small.
4. Recompute means.
5. Repeat till 2 and 3 cannot be applied.
We used this to cluster normal vectors in 3D data.
22. 22
Ohlander’s Recursive Histogram-
Based Clustering
• color images of real indoor and outdoor scenes
• starts with the whole image
• selects the R, G, or B histogram with largest peak
and finds clusters from that histogram
• converts to regions on the image and creates masks for each
• pushes each mask onto a stack for further clustering
24. 24
Jianbo Shi’s Graph-Partitioning
• An image is represented by a graph whose nodes
are pixels or small groups of pixels.
• The goal is to partition the vertices into disjoint sets so
that the similarity within each set is high and
across different sets is low.
25. 25
Minimal Cuts
• Let G = (V,E) be a graph. Each edge (u,v) has a weight w(u,v)
that represents the similarity between u and v.
• Graph G can be broken into 2 disjoint graphs with node sets
A and B by removing edges that connect these sets.
• Let cut(A,B) = w(u,v).
• One way to segment G is to find the minimal cut.
uA, vB
27. 27
Normalized Cut
Minimal cut favors cutting off small node groups,
so Shi proposed the normalized cut.
cut(A, B) cut(A,B)
Ncut(A,B) = ------------- + -------------
asso(A,V) asso(B,V)
asso(A,V) = w(u,t)
uA, tV
How much is A connected
to the graph as a whole.
normalized
cut
30. 30
How Shi used the procedure
Shi defined the edge weights w(i,j) by
w(i,j) = e *
e if ||X(i)-X(j)||2 < r
0 otherwise
||F(i)-F(j)||2 / I
||X(i)-X(j)||2 / X
where X(i) is the spatial location of node i
F(i) is the feature vector for node I
which can be intensity, color, texture, motion…
The formula is set up so that w(i,j) is 0 for nodes that
are too far apart.
32. 32
Lines and Arcs
Segmentation
In some image sets, lines, curves, and circular arcs
are more useful than regions or helpful in addition
to regions.
Lines and arcs are often used in
• object recognition
• stereo matching
• document analysis
33. 33
Edge Detection
Basic idea: look for a neighborhood with strong signs
of change.
81 82 26 24
82 33 25 25
81 82 26 24
Problems:
• neighborhood size
• how to detect change
35. 35
Example: Sobel Operator
-1 0 1 1 2 1
Sx = -2 0 2 Sy = 0 0 0
-1 0 1 -1 -2 -1
On a pixel of the image
• let gx be the response to Sx
• let gy be the response to Sy
Then g = (gx + gy ) is the gradient magnitude.
= atan2(gy,gx) is the gradient direction.
2 2 1/2
37. 37
Zero Crossing Operators
Motivation: The zero crossings of the second derivative
of the image function are more precise than
the peaks of the first derivative.
step edge
smoothed
1st derivative
2nd derivative
zero crossing
38. 38
Marr/Hildreth Operator
• First smooth the image via a Gaussian convolution
• Apply a Laplacian filter (estimate 2nd derivative)
• Find zero crossings of the Laplacian of the Gaussian
This can be done at multiple resolutions.
39. 39
Haralick Operator
• Fit the gray-tone intensity surface to a piecewise
cubic polynomail approximation.
• Use the approximation to find zero crossings of the
second directional derivative in the direction that
maximizes the first directional derivative.
The derivatives here are calculated from direct
mathematical expressions wrt the cubic polynomial.
40. 40
Canny Edge Detector
• Smooth the image with a Gaussian filter.
• Compute gradient magnitude and direction at each pixel of
the smoothed image.
• Zero out any pixel response the two neighboring pixels
on either side of it, along the direction of the gradient.
• Track high-magnitude contours.
• Keep only pixels along these contours, so weak little
segments go away.
44. 44
Finding Line and Curve Segments
from Edge Images
Given an edge image, how do we find line and arc segments?
Method 1: Tracking
Use masks to identify the following events:
1. start of a new segment
2. interior point continuing a segment
3. end of a segment
4. junction between multiple segments
5. corner that breaks a segment into two
junction
corner
45. 45
Edge Tracking Procedure
for each edge pixel P {
classify its pixel type using masks
case
1. isolated point : ignore it
2. start point : make a new segment
3. interior point : add to current segment
4. end point : add to current segment and finish it
5. junction or corner : add to incoming segment
finish incoming segment
make new outgoing segment(s)
The ORT package uses a fancier corner finding approach.
46. 46
Hough Transform
• The Hough transform is a method for detecting
lines or curves specified by a parametric function.
• If the parameters are p1, p2, … pn, then the Hough
procedure uses an n-dimensional accumulator array
in which it accumulates votes for the correct parameters
of the lines or curves found on the image.
y = mx + b
image
m
b
accumulator
47. 47
Finding Straight Line Segments
• y = mx + b is not suitable (why?)
• The equation generally used is: d = r sin + c cos
d
r
c
d is the distance from the line to origin
is the angle the perpendicular makes
with the column axis
48. 48
Procedure to Accumulate Lines
• Set accumulator array A to all zero.
Set point list array PTLIST to all NIL.
• For each pixel (R,C) in the image {
• compute gradient magnitude GMAG
• if GMAG > gradient_threshold {
• compute quantized tangent angle THETAQ
• compute quantized distance to origin DQ
• increment A(DQ,THETAQ)
• update PTLIST(DQ,THETAQ) } }
50. 50
How do you extract the line
segments from the accumulators?
pick the bin of A with highest value V
while V > value_threshold {
order the corresponding pointlist from PTLIST
merge in high gradient neighbors within 10 degrees
create line segment from final point list
zero out that bin of A
pick the bin of A with highest value V }
51. 51
Finding Circles
Equations:
r = r0 + d sin
c = c0 + d cos
r, c, d are parameters
Main idea: The gradient vector at an edge pixel points
to the center of the circle.
*(r,c)
d
52. 52
Why it works
Filled Circle:
Outer points of circle have gradient
direction pointing to center.
Circular Ring:
Outer points gradient towards center.
Inner points gradient away from center.
The points in the away direction don’t
accumulate in one bin!
53. 53
Procedure to Accumulate Circles
• Set accumulator array A to all zero.
Set point list array PTLIST to all NIL.
• For each pixel (R,C) in the image {
For each possible value of D {
- compute gradient magnitude GMAG
- if GMAG > gradient_threshold {
. Compute THETA(R,C,D)
. R0 := R - D*cos(THETA)
. C0 := C - D* sin(THETA)
. increment A(R0,C0,D)
. update PTLIST(R0,C0,D) }}
54. 54
The Burns Line Finder
1. Compute gradient magnitude and direction at each pixel.
2. For high gradient magnitude points, assign direction labels
to two symbolic images for two different quantizations.
3. Find connected components of each symbolic image.
1
2
3
4
5
6 7
8
1
2
3
4
5
6 7 8
• Each pixel belongs to 2 components, one for each symbolic image.
• Each pixel votes for its longer component.
• Each component receives a count of pixels who voted for it.
• The components that receive majority support are selected.
-22.5
+22.5
0
45