The document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. The document further states that sample source code is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
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
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms
that use space that is linear or sublinear in the dimension. We prove general results showing that any sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques such as Frequent Directions and random projections to derive efficient and practical algorithms for these problems, which we validate over real-world data sets. Our main technical contribution is to prove matrix perturbation
inequalities for operators arising in the computation of these measures.
-Proceedings: https://arxiv.org/abs/1804.03065
-Origin: https://arxiv.org/abs/1804.03065
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
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms
that use space that is linear or sublinear in the dimension. We prove general results showing that any sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques such as Frequent Directions and random projections to derive efficient and practical algorithms for these problems, which we validate over real-world data sets. Our main technical contribution is to prove matrix perturbation
inequalities for operators arising in the computation of these measures.
-Proceedings: https://arxiv.org/abs/1804.03065
-Origin: https://arxiv.org/abs/1804.03065
3D Reconstruction from Multiple uncalibrated 2D Images of an ObjectAnkur Tyagi
3D reconstruction is the process of capturing the shape and appearance of real objects. In this project we are using passive methods which only use sensors to measure the radiance reflected or emitted by the objects surface to infer its 3D structure.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
COSC 426 Lecture 5 on Mathematical Principles Behind AR Registration. Given by Adrian Clark from the HIT Lab NZ at the University of Canterbury, August 8, 2012
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
4. Contours
A contour is an assembled collection of edges that (hopefully) represent one object in
the image.
• Can be used to automatically segment the different objects present in an image.
5. Preprocessing for cv2.findContours
Can use canny edges
• Images created by cvCanny
Can use thresholded image where edges are boundaries between white and black
• Images created by cvThreshold or cvAdaptiveThreshold
Call cv2.findContours on result of Canny or thresholding.
7. Contour Hierarchy
cvContour can represent data as a contour tree.
0 and 2 are the contours represented at the root, or parent,
nodes.
Other contours are represented as children of 0 or 2.
0
1
2
3
4
8. cvFindContours
Input needs to be 8-bit binary image.
Make a copy of the image before using cvFindContours
• The original image will be written over by
cvFindContours
OpenCV represents the contour hierarchy as an array:
[next, previous, first_child, parent]
0
1
2
3
4
9. Method Variable: Arguments to Find Contours
Method clarifies how the contour is being computed.
CV_CHAIN_CODE
Output = sequence of vertices (Freeman chain code)
CV_CHAIN_APPROX_NONE
All chain code points = contour points
CV_CHAIN_APPROX_SIMPLE
Endpoints of horizontal, vertical, diagonal segments
CV_CHAIN_APPROX_TC89_L1
Teh_Chin chain algorithm
CV_LINK_RUNS
Links horizontal segments of 1s
Limited to CV_RETR_LIST
10. Mode Variable
Mode clarifies what contours should be found and desired format for return
value.
Finds contours and uses horizontal and vertical links to link the found
contours.
Mode options:
CV_RETR_EXTERNAL
CV_RETR_LIST
CV_ RETR_CCOMP
CV_RETR_TREE
11. CV_RETR_LIST
All contours are put into a list
This list does not have parent – child
relationships
Uses horizontal links
All contours are on same level (no actual
hierarchy)
0
1
2
3
4
13. CV_RETR_CCOMP
All contours are used to
generate a 2-level hierarchy
• Top-level hierarchy = external
• Bottom-level = internal
• Horizontal and vertical linkers
0 1
2 3
4
18. Length and Area
Length
• cv2.ContourPerimeter
Returns length of a contour
You can summarize length and area as a bounding structure
• Boxes
• Circles
• Ellipses
19. Bounding Boxes
cv2.BoundingRect()
• Returns rectangle that bounds the contour
• Red box in image
cv2.MinAreaRec2()
• Returns smallest rectangular bound
• Green box in image
Note green box is rotated for best fit
20. Circles and Ellipses
cv2.MinEnclosingCirlce()
• Similar to cv2.BoundingRect() in that it bounds the contour, but not a best fit
• Needs radius as input
• Red circle in image
cv2.FitEllipse2()
• Allows us to get a best fit bound
• Green circle image
21. Geometric Toolkits/Checks
cv2.maxRect()
• Two input rectangles are used to compute a new rectangle
cv2.BoxPoints()
• Computers corner points of cvBox2D structure
cv2.PointPolygonTest()
• Test: is point in polygon?
23. Finding Extreme Points: Min/Max
Min/Max is computed with NumPy.
Extreme points of the cross are min/max left/right and up/down.
24.
25. Foreground/Background Separation
Separate foreground from background
Send foreground on for further analysis
• Cars in security camera
• Skin within an image (reduces complexity of finding faces)
Superpixels
• Groups of pixels in same object/type of object
26. Methods: Image Pyramids
Collection of images where each subsequent image is a downsampling of
the previous.
Gaussian pyramid
Used to downsample
Removes even rows and columns
Will give you higher resolution
Laplacian pyramid
Used to upsample
Works with lower-resolution images and allows for faster computations
27. Gaussian Pyramid
Use cvPyrDown() with a 5x5 Guassian kernel
• Start with G0 (original image)
• Convolve G0 with Gaussian kernel
• Gi is ¼ size of G0 because we removed even-numbered rows and
columns from G0
• Repeat for all desired Gi+1
28. Laplacian Kernel
Use cvPyrUp()
Notice the relationship between Gausian and Laplacian
In OpenCV we see this as:
Lapi = Gausi – PyrUp(Gausi+1 )
Remember, we saw this in Week 3!
29. Methods to Segment an Image
cvPyrSegmentation ()
• Generates an image pyramid
• Associates pyramid layers to parent-child relationships
• Map pixels between Gi+1 and Gi
• Perform segmentation on lower-resolution images
• Note: Start image must be divisible by 2 for each layer of the pyramid
30. Mean Shift Clustering Over Color
pyrMeanShiftFiltering()
Peak of color-spatial distribution over space
Data dimensions are:
Spatial (x, y)
Color (blue, green, red)
Parameters are spatialRadius, colorRadius, max_level, and CvTermCriteria.
31. Watershed
Used to separate objects when you do not have a separate background image.
Input is a grayscale image or a smoothed color image.
Can cause oversegmentation or undersegmentation
• If you make too many basins or too few
Lines converted to peaks; uniform regions to catchment basins.
32. Watershed Method
Take the gradient of intensity image.
Marker points, or known areas of region of interest, are defined by user.
Basins get connected to marker points to create segments.
38. Grabcut: Graph Cutting Techniques
Pixel-based energy function
Basic binary segmentation algorithm to identify object
Iteratively recalculates the region statistics (Gaussians) to perform segmentation
39. GrabCut Procedure
(1) Assign background and unknown
Unknown becomes potential foreground
(2) Create five probabilistic clusters (each) of foreground and background
(3) Assign every background pixel to a cluster of background
Do the same for foreground
(4) Compute cluster statistics for all clusters
40. GrabCut Procedure (continued)
(5) Create a graph where:
Pixels are linked to their neighbors and to two special nodes
Nodes represent foreground and background
The weights of the links are related to neighbor similarity and class probability
41. GrabCut Procedure (continued)
(6) Apply a classic computer science graph theory algorithm min-cut to the
graph
The net result cuts the connections from each pixel to one of the two
special nodes.
In turn, this gives us a new assignment for foreground/background to the
pixels.
(7) Until convergence, we loop back to Step 4 and repeat
Classifications remain similar enough from one round to the next.
42.
43. Contour Moments: Matching Contours
Compare statistical moments of contours:
In statistics, first moment is the mean, second moment is the variance, and so
on.
Moments are used to compare two contours (outlines) for similarity.
Another technique: Compute rough contour characteristic computed by
summation of all pixels over contour boundary:
p: x-order q: y-order
Normalized moments are better for comparing similar shapes of different sizes.
𝑚 𝑝,𝑞 =
𝑖=1
𝑛
𝐼 𝑥, 𝑦 𝑥 𝑝 𝑦 𝑞
44. Statistical Moments
In classical statistics, we use mean, variance, skew, and kurtosis.
cvMoments()
cvGetCentralMoment()
invariant with respect to translation
cvGetNormalizedMoment()
invariant with respect to scale
cvGetHuMoments()
invariant with respect to rotation
45. Normalized Moments
Central moments normalized with respect to a function intensity.
First need to calculate central moment:
Then you can calculate normalized moments:
𝜂 𝑝,𝑞 =
𝜇 𝑝,𝑞
𝑚00 𝑝 + 𝑞 /2 + 1
𝜇𝑝, 𝑞 =
𝑥,𝑦
𝐼 𝑥, 𝑦 𝑥 − 𝑥𝑎𝑣𝑔 𝑝 𝑦 − 𝑦𝑎𝑣𝑔 𝑞
46. Hu Invariant Moments
Linear combinations of centralized moments.
Use central moments to get invariant functions
• Invariant to scale, rotation, and reflection
cvGetHuMoments()
ℎ1 = 𝜂20 + 𝜂02
ℎ4 = (𝜂30+ 𝜂12)2 + (𝜂21 + 𝜂03)2
47.
48. Template Matching Overview
Use cvMatchTemplate() to find one image within another.
Uses the actual image of matching object instead of a histogram
• This is called an image patch
Input image
• 8-bit, floating point plane, or color image
Output
• Single-channel byte or floating point image
51. Histogram Backprojection
Better thought of as segmentation to foreground ROIs used for further
analysis.
If you have a histogram of the ROI, we can use that to find the object in
another image.
Use histogram from one image to match to histogram profile from another
image
Normalize to pixel number
Find matching object, or ROI
cvCalcBackProjectPatch()
52. Histogram Backprojection
Given a color, we want to know the probability of foreground/background.
Approximate probability that a particular color belongs to a particular object
You can also make a ratio histogram where you de-emphasize colors not in the ROI.
𝑝 𝑜𝑏𝑗𝑒𝑐𝑡 𝑐𝑜𝑙𝑜𝑟 =
𝑝 𝑐𝑜𝑙𝑜𝑟 𝑜𝑏𝑗𝑒𝑐𝑡 ∗ 𝑝(𝑜𝑏𝑗𝑒𝑐𝑡)
𝑝(𝑐𝑜𝑙𝑜𝑟)
~ 𝑝 𝑐𝑜𝑙𝑜𝑟 𝑜𝑏𝑗𝑒𝑐𝑡
Recall: White is the object and black is the background.
Quick test for students: What would happen if there were a box around everything?
We have only drawn the contour for the last lightning bolt.
NOTE: Green points are the defects.
Defects are points furthest from the outer contour. For example, the top-right green is furthest from the top-right red contour.
Calculated using NumPy, not built-ins.
Image url?
Probabilistic clusters are components of a Gaussian mixture model.
The class node probabilities can be set to 100% FG/BG, which guarantees the pixel keeping its association with that class after min-cutting.
NOTE: Equality is from Bayes’ Theorem.
Approximation is made by assuming p(o)/p(c) is a constant, which, if we normalize probabilities, can be dropped.