# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

17 Pages

### lecture15

Course: CS 791, Fall 2009
Rating:

Word Count: 632

#### Document Preview

791E CS Computer Vision Instructor: Mircea Nicolescu Lecture 15 Region Detection Properties for region-based segmentation - Partition an image R into sub-regions R1, R2,..., Rn - Assume P(Ri) is a logical predicate a property that pixel values of region Ri satisfy (e.g., intensity between 100 and 120). - The following properties must be true: 2 Region Detection Main approaches for region detection -...

Register Now

#### Unformatted Document Excerpt

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
791E CS Computer Vision Instructor: Mircea Nicolescu Lecture 15 Region Detection Properties for region-based segmentation - Partition an image R into sub-regions R1, R2,..., Rn - Assume P(Ri) is a logical predicate a property that pixel values of region Ri satisfy (e.g., intensity between 100 and 120). - The following properties must be true: 2 Region Detection Main approaches for region detection - Thresholding (pixel classification) - Region growing (splitting and merging) - Relaxation 3 Thresholding The simplest approach to image segmentation is by thresholding: if f(x,y) < T then f(x,y) = 0 else f(x,y) = 255 4 Thresholding Automatic thresholding - To make segmentation more robust, the threshold should be automatically selected by the system. - Knowledge about the objects, the application, the environment should be used to choose the threshold automatically: - - - - Intensity characteristics of the objects Sizes of the objects Fractions of an image occupied by the objects Number of different types of objects appearing in an image 5 Thresholding Choosing the threshold using the image histogram - Regions with uniform intensity give rise to strong peaks in the histogram - Multilevel thresholding is also possible - In general, good thresholds can be selected if the histogram peaks are tall, narrow, symmetric, and separated by deep valleys. 6 Thresholding Choosing the threshold using the image histogram - Problem: some pixels from the background may look the same as some pixels from the object - Solution: hysteresis thresholding 7 Thresholding Hysteresis thresholding - Two thresholds can be used in this case - Pixels below the high threshold are classified as object and above the high threshold as background. - Pixels between the low and high thresholds are classified as object only if they are adjacent to other object pixels. 1. if f(x,y) < T1 then f(x,y) = 0 else if T1 < f(x,y) < T2 then f(x,y) = 128 else f(x,y) = 255 1. for each (x,y) where f(x,y) = 128 if has (x,y) neighbors (x',y') where f(x',y') = 0 f(x,y) = 0 repeat step 2 until no pixels are reassigned for each (x,y) where f(x,y) = 128 f(x,y) = 255 // remaining undecided // reassign to background 8 // object // not sure // background //object neighbor // reassign to object 1. 1. Thresholding Hysteresis thresholding 9 Thresholding Using prior knowledge: the P-Tile method - This method requires knowledge about the area or size of the objects present in the image - Assume that there are dark objects against a light background. - If, for example, the objects occupy p% of the image area, an appropriate threshold can be chosen by partitioning the histogram 10 Thresholding Optimal thresholding - Assume that an image contains only two principal regions (object and background) - We can minimize the number of misclassified pixels if we have some prior knowledge about the distributions of the gray level values for the object and the background. - Assume that the distribution of gray-level values in each region follows a Gaussian distribution 11 Thresholding Optimal thresholding cont. - The probability of a pixel value is then given by the following mixture: 12 Thresholding Optimal thr...

Textbooks related to the document above:
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 16Thresholding Otsu's method A measure of region homogeneity is variance (i.e., regions with high homogeneity will have low variance). Otsu's method selects the threshold by minimizin
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 17Region Merging Region merging using hypothesis testing- This approach considers whether or not to merge adjacent regions based on the probability that they will have the same statist
Segmentation for Videos with Quasi-Stationary Backgrounds A Non-Parametric ApproachAlireza Tavakkoli Advisor: Dr. Mircea Nicolescu Department of Computer Science and EngineeringOutline Problem definition Previous work Motivations The AKD
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 192D Geometric Transformations General form of a transformation matrix Affine transformations- Involve translations, rotations, scale, and shear - Preserve parallelism of lines but no
CS 791E Computer VisionHomework 4 (Due Tuesday, November 13)1. (25 pts) In the context of image segmentation, assume that the intensity values of the object and background pixels are distributed according to the following probability density fun
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 20Method 1: Direct Parameter Calibration Assume that the world reference frame is known (e.g., the origin is the middle lower corner of the calibration pattern). Review of basic equati
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 21Stereo Vision Goal- The recovery of the 3D scene structure, using two or more images of the 3D scene, each acquired from a different viewpoint in space. - The images can be obtained
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 22The Correspondence Problem Methods for establishing correspondences- There are two issues to be considered:- how to select candidate matches? - how to determine the goodness of a ma
CS 791E Computer VisionInstructor: Mircea Nicolescu Lecture 23Estimating the Epipolar Geometry The essential matrix, E The previous equation can now be rewritten as:where E = RS is called the essential matrix. The equation prTEpl = 0 defines
596IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,VOL. 29,NO. 4,APRIL 2007A Bayesian Approach to Deformed Pattern Matching of Iris ImagesJason Thornton, Student Member, IEEE, Marios Savvides, Member, IEEE, and B.V.K. Vijaya
Computer Vision and Image Understanding 108 (2007) 196203 www.elsevier.com/locate/cviuRobust real-time vision for a personal service robot Gerard Medioniaa,*, Alexandre R.J. Francois a, Matheen Siddiqui a, a Kwangsu Kim , Hosub Yoon bInsti
Robotics and Autonomous Systems 55 (2007) 904916 www.elsevier.com/locate/robotIVVI: Intelligent vehicle based on visual informationJos Mara Armingol a, , Arturo de la Escalera a , Cristina Hilario a , Juan M. Collado a , e i e i e Juan Pablo Carra
Hidden Conditional Random Fields for Gesture RecognitionSy Bor Wang Ariadna Quattoni Louis-Philippe Morency Trevor Darrell David Demirdjian{sybor, ariadna, lmorency, demirdji, trevor}@csail.mit.eduComputer Science and Artificial Intelligence Lab
544IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,VOL. 22, NO. 5,MAY 2000Short Papers_Object Tracking Using Deformable TemplatesYu Zhong, Anil K. Jain, Fellow, IEEE, and M.-P. Dubuisson-Jolly, Member, IEEEAbstractWe prop
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 3, NO. 1, MARCH 200237Detection and Classification of VehiclesSurendra Gupte, Osama Masoud, Robert F. K. Martin, and Nikolaos P. Papanikolopoulos, Member, IEEEAbstract-This paper pre
CS 791E Computer VisionProgramming Assignment 3 (Due Thursday, November 29)1. (100 pts) Test and evaluate the camera calibration procedure, by using the OpenCV function cvCalibrateCamera_64d to compute the intrinsic and extrinsic camera paramete
CS 791E Computer VisionProgramming Assignment 1 (Due Tuesday, September 25)1. (40 pts) Implement image smoothing using convolution with Gaussian masks. You should use 2 input images of your choice. First, implement 2D Gaussian convolution using
CS 791E Computer VisionProgramming Assignment 2 (Due Tuesday, November 6)1. (30 pts) Test and evaluate the Canny edge detector, by using the OpenCV function cvCanny. Images to be used in your experiments are available from the class webpage. Eva
San Diego State - ART - 445
more cakehailey hollander740 yarmouth crt. san diego, ca 92109eatbirthday candlesmore more cakeeat eatblue pinkcake mixmade with organic floursprinklesmore cakeblue pink yellowWhat decorative and contemparary sprinkles look the b
San Diego State - ART - 445
more more more more cake cake cake cakehailey hollander740 yarmouth crt. san diego, ca 92109eat eat eat eatmore eat cake morepresident hailey hollander310-379-6185 310-413-5780 digits president hailey hollander president hailey hollander mail
FIU - PHY - 2053
HOMEWORK CHAPTER 2 5. REASONING AND SOLUTION The velocity of the car is a vector quantity with both magnitude and direction The speed of the car is a scalar quantity and has nothing to do with direction. It is possible for a car to drive around a tra
FIU - PHY - 2053
CHAPTER 3 2. REASONING AND SOLUTION An object thrown upward at an angle will follow the trajectory shown below. Its acceleration is that due to gravity, and, therefore, always points downward. The acceleration is denoted by ay in the figure. In gene
FIU - PHY - 2053
CHAPTER11 FLUIDSCONCEPTUAL QUESTIONS_5.REASONING AND SOLUTION The bottle of juice is sealed under a partial vacuum. Therefore, when the seal is intact, the button remains depressed, because the pressure inside the bottle is less than the atm
FIU - JMART - 054
/** Author: Bart Simpson ** Course: CGS 2423-01 TU-TH 12.30-1.45 ** Professor: Juan Carlos Martinez ** Assignment: #1 Arithmetic &amp; Simple I/O. DU
FIU - JMART - 054
COP 3804 Intermediate Java Summer C 2009Instructor Juan Carlos Martinez ECS 212 jmart054@cs.fiu.edu Class Time and Room Number MON-WED 07:50 PM 9:05 PM, Room CP 151 Office Hours MON-WED 06:50 PM 07:50 PM, Room ECS 212 Prerequisite COP-2250 Java
FIU - JMART - 054
CGS 2423C FOR ENGINEERSSpring 2008Instructor Juan Carlos Martinez ECS 212 jmart054@cs.fiu.edu Class Time and Room Number TUE-THU 12:50 PM 2:05 PM, Room EC 2832 Office Hours TUE-THU 12:50 PM 2:05 PM, Room EC 2832 Prerequisite None. General com
FIU - JMART - 054
CGS 2423C FOR ENGINEERSFall 2008Instructor Juan Carlos Martinez ECS 212 jmart054@cs.fiu.edu Class Time and Room Number TUE-THU 12:55 PM 2:05 PM, Room EC 2832 Office Hours MON-WED 12:00 PM 2:00 PM, Room ECS 212 Prerequisite None. General compu
FIU - JMART - 054
Computer Data Analysis Student Name Alcala,Ibis Alcala,Stephany Arcos,Priscilla Armbrister,Esther E Baldoquin,Christine Michelle Borges,Maynel CARRION,EDMUNDO Chapman,Kevin S Concha,Cristine D'anglade,Luca De La Nuez,Michael Orlando Dezendegui,Adrian
FIU - JMART - 054
Computer Data Analysis Student Name Chang,Alejandro GUNTER,JANEL A Laza,Erica A Loaisiga,Noelia M MARTI,ANTONIO M Marti,Victoria E Matthews,Dylan T Miller,Larry B Mills,Jason V Miranda,Megan N Pelegrin,Marcel Pena,Stephanie A Perez,Alexander Pham,Nhu
San Diego State - ART - 241
241_Project 3_Michelle Garciaprinciple(s): continuation illusion (value) pos/neg soace proximityprinciple(s): empahsis continuation illusion (scale) assymetryprinciple(s): symmetry proximity rhythmprinciple(s): unity asymmetry emphasisprinc
San Diego State - ART - 241
241_Project 3_Michelle Garciaprinciple(s): continuation illusion (value) pos/neg soace proximityprinciple(s): empahsis continuation illusion (scale) assymetryprinciple(s): symmetry proximity rhythmprinciple(s): unity asymmetry emphasisprinc
San Diego State - ART - 241
241_Project 3_Michelle Garciaprinciple(s): continuation illusion (value) pos/neg soace proximityprinciple(s): empahsis continuation illusion (scale) assymetryprinciple(s): symmetry proximity rhythmprinciple(s): unity asymmetry emphasisprinc
San Diego State - ART - 241
black and whiteASTRUMgrayscalecafcolorapplications for for Astrum Cafe symbol mark astrum cafe toile cuisine stern skateboards estrella sports toile cuisine Estrella Sports Stern Skateboards 1. T shirt 2. exterior sign 3. coffee mug wine sho
San Diego State - ART - 241
black and whiteASTRUMgrayscalecafcolorapplications for for Astrum Cafe symbol mark astrum cafe toile cuisine stern skateboards estrella sports toile cuisine Estrella Sports Stern Skateboards 1. T shirt 2. exterior sign 3. coffee mug wine sho
San Diego State - ART - 545
CHOMPR MPSTOMP
San Diego State - ART - 545
CALIFORNIA DREAMING EXCHANGECALIFORNIA DREAMING EXCHANGE
St. Francis IL - DEVS - 300
Development Studies 300 Fall 2006/Spring 2007GlobalizationProfessor Peter TamasOffice: JBB 335G Office Hours: Tuesday/Thursday 2:30-5:00. ALWAYS email in advance with your question so I can: make sure there is time, and make sure I'm properly p
Lehigh - IE - 426
IE426 Optimization models and applicationsFall 2008 Homework #1 This homework accounts for 5% of the final grade. There are 20 points available. Do not cheat: this homework is actually a good training for quiz #1, so you don't really want to copy.
St. Francis IL - DEVS - 300
On Thursday we will 1. Finish up the one left over article from Tuesday 2. Talk through the readings for Thursday (below) 3. Distribute the initial round of articles that are going to get turned into our annotated bibliography 4. Have a look together
St. Francis IL - DEVS - 300
Readings and Questions for Tuesday September 19thRequired Readings (4)Gray, John, From the Great Transformation to the Global Free Market in Lechner and Boli (2004) pp. 22 28.download from: http:/www.nytimes.com/books/first/g/gray-dawn.html(r
St. Francis IL - DEVS - 300
Thursday Sept 21 Keohane, Robert O. and Joseph S. Nye Jr. &quot;Globalization: What's New? What's Not? (And So What?), in Held and McGrew (2003), pp. 75 83. download from an on campus computer from: http:/libproxy.stfx.ca:9000/login?url=http:/proquest.um
St. Francis IL - DEVS - 300
Washington Consensus. I got stuck. I reviewed the articles available for this week, and decided that I didn't want to use any of them for Tuesday's class. Thursday's class is much easier. So I did some research and found an (count it.one) article tha
St. Francis IL - DEVS - 200
DEVELOPMENT STUDIES 200.12INTRODUCTION TO INTERNATIONAL DEVELOPMENTFall Term, 2008 CANADAProfessor James Bickerton Nicholson 510 Ph: 3939 Email: jbickert@stfx.caCourse DescriptionDevelopment is a process and a challenge not only for the peopl
St. Francis IL - DEVS - 200
CANADIAN POLITICAL CULTURE Stephen Brooks University of Windsor Introduction There is no more Canadian pastime than reflecting on what it means to be a Canadian. Unlike the French, the English, the Chinese, the Russians and the Americans, to name a h
St. Francis IL - DEVS - 200
Women (Not) in Politics: Women's Electoral ParticipationLisa Young Associate Professor Department of Political Science University of Calgary Lisa.Young@ucalgary.caRevised 2008-08-24Comparing Canada to much of the rest of the world, it is eviden
St. Francis IL - DEVS - 200
Canadian International Environmental Policy: Context and Directions Peter J. Stoett, Concordia UniversityThere was once a time, not long ago, when we could summarize environmental politics as an emerging field of study consigned largely to the sphe
St. Francis IL - DEVS - 200
Globalization and CanadaMark R. Brawley When world leaders have met in Canada in recent years to discuss the global economy, antiglobalization protesters came out in force. Why does globalization trigger such protests here? Neither academics nor the
St. Francis IL - DEVS - 200
1 Understanding Canada's Origins: Federalism, Multiculturalism, and the Will to Live Together Samuel V. LaSelva&quot;More than most other countries, Canada is a creation of human will. It has been called a `geographical absurdity,' an `appendage of the
St. Francis IL - DEVS - 200
CANADA'S PATH TO DEVELOPMENT (preWWI)STATE (state elites, political structures, state capacities, state policies)Exogenous factors Dominion status within British Empire Quebec Act (1774) American revolution Repeal of `corn laws' 1846
St. Francis IL - DEVS - 200
DEVELOPMENT: DEFINITIONS AND FACTORS Development as process and outcome: Development is the process by which all members of society gain access to an expanding number of opportunities for personal and collective growth. (APEC) A `developed society'
St. Francis IL - DEVS - 200
DEVELOPMENT STUDIES 200 Term Paper Assignment Due On or Before April 1, 2009Students are required to submit a 10-12 page research paper in the spring term (25003000 words.). This paper will be worth 30% of the term mark. When choosing a topic studen
St. Francis IL - DEVS - 200
Development Studies 200, 20089, 6/4/2009St. Francis Xavier University DEVELOPMENT STUDIES 200, 20089 Fall Term INTRODUCTION TO INTERNATIONAL DEVELOPMENT Dr. Alison Mathie Coady MacDonald 103 Ph: 3235 Email: amathie@stfx.caThis course is an introd
St. Francis IL - DEVS - 200
Development Studies 200:11, Fall Semester, 2008 REVISED COURSE OUTLINE Current priorities: Food security November 3: Climate change, biofuels, and the impact on food security Presentation and discussion with Elizabeth May Readings: http:/www.oxfam.or
St. Francis IL - DEVS - 200
What is Development? The history of an idea1945 United Nations 1947 Marshall Plan 1949 Truman's inaugural address 1960: UN &quot;Decade for Development&quot; (&quot;1.0% of GNP to be allocated to development aid&quot;)1970s1969: Pearson Commission: 19
St. Francis IL - DEVS - 200
What is development?Howdowemeasureit? Learning from Ladakh Oursurvivaldependsonrecognitionofourinterdependencelocallyand globally. Localwellbeingisassociatedwithastrongsenseofcultural(andor spiritual)identity. Freedomandtheabilityofpeopleto
St. Francis IL - DEVS - 200
Why should the &quot;rich&quot; help those in poverty?Development Studies, September 22, 2008 Because. (class responses)Altruistic reasons Reasons of self interest It is our moral obligation It is a question of economic justice We ca
St. Francis IL - DEVS - 200
Colonialism and its LegacyDevs 200, 2007 Colonialism: Why relevant?&quot; Modern history cannot be understood outside the colonial context which burdened poorer countries with technological, financial, and trading disadvantages vis a vis form
St. Francis IL - DEVS - 200
The Postwar Development Project: Theory and PracticeDevelopment Studies, Fall 2008 Time LineIn UN di a' In / s st Br In itu et de to tio pe ns n W nd oo en ds ceOilS hoDebt Crisis &amp; Structural Adjustment Policiesck 9/ 1119401950
St. Francis IL - DEVS - 200
The UN Millennium Development ProjectDevelopment Studies 200 2008Millennium Development Goals1. 2. 3.4.Set in 2000 by UN members nations to address global poverty and inequality in the new millennium A departure from WB/IMF focus on market
St. Francis IL - DEVS - 200
Development and the EnvironmentDevs Studies 200:11 Fall 2008 Significant historical events1972 UN conference on Environment Brundtland Commission (1987) &quot;Our Common Future&quot;, showing relationship between environment crisis, debt crisis an
St. Francis IL - DEVS - 200
Achieving sustainable livelihoodsRestoring, conserving, building, and mobilizing assets www.antigonishfilmfest.org Sustainable Livelihoods Framework Vulnerability contextTrends Shocks Seasonality Asset building to dive