Robot Vision Horn Mit Pdf 28

Robot Vision Horn Mit Pdf 28

Robot Vision Horn Mit Pdf 28 >>> https://bytlly.com/2sUZBk

Computer vision is the study of analysis of pictures andvideos in order to achieve results similar to those as by men.Thus human vision acts as a lower bound on our ambitions withregard to computational image analysis (Turing Test for computervision). The field of computer vision has inspired a large numberof researchers in computer science, engineering, mathematics andeven though we are still far from achieving this ultimate goal,we have gathered a great amount of work and knowledge in theprocess and the techniques developed are widely used in the areassuch as medical imaging, video surveillance, computer graphics,video compression etc.

Staff Best Way to Contact Zoom Office Hours Zoom Link Margrit Betke betke@bu.edu Mon 16:00-18:00, Tue/Thu 10:45-11:15 Hao and Ryan Piazza. But note that there is a 24-hour timer on your Piazza questions to encourage other students to answer questions before TFs will able to see your question. Only send an email if your question is urgent and requires an answer earlier than 24 hours. In that case, send an email to both TFs:Hao haoyu@bu.edu and Ryan ryu1@bu.edu Tue 16:00-18:00 EST, Wed 7:00-9:00 EST TF zoom link Seeing Me in My Office: Due to the pandemic, I will mostly work from home. Please try to reach me after class or on Monday afternoon during my office hours. Youcan also make an appointment by email. I'm happy to talk with youabout the course, computer vision research, your plans for the future,or anything else. Check out mypersonal web page to get to know me a little. Teaching Fellow Responsibilities: The TFs are responsible forteaching the Laboratory section, helping you out during their office hours, andgrading of the homework (or managing graders).

Course Objectives Our goal is to build computer systems that analyze imagesautomatically and determine what the computer "sees" or"recognizes." The course gives you a fundamental introduction tocomputer vision methods. Applications include human-computerinterfaces, face detection, medical image processing, infrared imageanalysis of animals, and vision systems for intelligent vehicles. Prerequisites: 1 year programming experience (e.g., Python, C++or Java at CAS CS 112 level), linear algebra or geometric algorithms, and calculus.Course MaterialsThe syllabus below will be updated throughout the semester with lecture notes,videos, and reading materials. Links to homework assignments will come alive when theassignment is ready for you to work on.

Grading Policy Homework: The homework includes bi-weekly programming,reading assignments, and problem sets. The due dates are listedbelow. Programs and reports must be submitted electronically.Solutions to problem sets must be submitted in class. They do notneed to be typed but should look professional, in particular, writelegibly and leave a margin for grading comments. Guidelines for submission are provided with each assignment. Latesolutions will be levied a late penalty of 20% per day (up to threedays). After three days, no credit will be given.You must demo your bi-weekly programming projects and your final class projectto the TFs or the graders within one of the offered time slots in order to receivefull credit for your project submission. A signup sheet with available demotimes will be circulated during class before the due date.Your electronic project submission should include detailedinstructions for compiling and setting up your program. The graderwill recompile your program and test it on your input videos. Project: Please read the project guidelines. You canpropose your own project topic or use one of my project suggestions. I willdiscuss your project's scope, design, and presentation with you in my officehours and provide guidance throughout the semester. You must work in a group of2-4 students. You will be asked to select a project topic by the middle of thesemester and present the final project in class at the end of the semester.Here will be the projectschedule.Computer Vision Talks: Students are strongly encouragedto attend the Image andVideo Computing talks, which are part of the new AI Research Seminar series on Mondays, 11-12 am, online. Class Participation: Come to class, either in person orremotely, and participate regularly. Reading the textbook andlistening in class will only give you a "passive understanding" of thematerial. I encourage discussions in class to help you acquire an"active understanding" of the material so that you can evaluateexisting computer vision techniques critically and develop your owncreative solutions. Participation counts toward your overall grade.If you have trouble connecting via zoom in real time, let us know andmake sure to be active on Piazza so that you do not receive a failinggrade in participation.

Grading Policy:Your final grade will be determined roughly as follows: Homework 30% Projects (design, results, presentation) 40% Class participation 10% Talk reviews 5% Midterm exam 15% Collaboration and Academic IntegrityYou are encouraged to collaborate on the solution of the homework. If you do,you must acknowledge your collaborators. Each student mustsubmit his or her own electronic version of the solutions. You can request anexception to this rule for your final project. If you use algorithms or codethat are not your own original work and that were not provided in class ordiscussed in the textbook, you must give a detailed acknowledgment ofyour source .You are not allowed to collaborate on the solution of theexams. Sources must be acknowledged.Cheating and plagiarism are not worthy of Boston University students.I expect you to abide by the rule stated above and the standards ofacademic honesty and computer ethics policy described in and -conduct-codeHelp Image and Video Computing is an elective course that will introduce youto an exciting topic in computer science. It should be fun and not too much ofa struggle for you. Make sure that you have had the prerequisites. Dependingon your level of programming experience and/or mathematics background, thecourse may be challenging for you. If you do not understand the material, askfor help immediately. Ask questions in class. If one student is confused aboutsomething, then maybe others are also confused and grateful that someone asked.Come and see me or the TF for help or send us email. Our task is to help youlearn a very interesting topic!Course Schedule Dates Topics --- Links will become active with lecture Readings --- Links will become active Assignments 1/26/21 Course Introduction: Why study IVC?Industry successes and current needs. Lecture 1 links,Wiki Intro or Horn Ch. 1. 1/28/21 Image and video formats, color (RGB, HSV), Image Projections, Flood Fill Algorithm,Sequential Multi-object Labeling Algorithm.Skin-color based face detection algorithm. Lecture 2 slides. 1/29/21: A1 out (after lab) 2/2/21 Programming with Images: Pitfalls. Binary Image Analysis: Moments, centroid.Moments and distances. Image Moments,Binary Image Analysis. Horn Ch. 3. . 2/4/21 Binary Image Analysis: Object orientation, circularity measures.Lecture notes photos: 1, 2, 3. Image Moments,Binary Image Analysis. Horn Ch. 3. A1 due 2/9/21 Sequential Labeling Algorithm. Template matching background differencing. Skin-based face detection. Similarity Functions (SSD, NCC), Motion: Template-based Tracking. Image Pyramids:Lecture notes. Wiki on template matching, normalized correlation. Image Moments,Binary Image Analysis. Skeleton,Horn Ch. 3, . 2/11/21 Tumor Detection in Computed Tomography Images. Confusion matrix analysis.Motion energy. Fawcett (confusion matrix analysis), Horn Ch. 4. A2 out. 2/18/21 No class on Tuesday, February 16 (Monday schedule). Segmentation: Thresholdingtechniques. Neighborhoods. Border following algorithm. Hausdorff Distance. Lecture notes. Border following algorithm.Segmentation (most algorithms covered). Hausdorff distance. Thresholding. A2 due, A3 out 2/23 Lecture notes on edge detection & active contours Canny Edge Detection. Active Contours. Sobel, Prewitt, Roberts, Mexican Hat, Difference of Gaussians, Canny Edge Detector.Williams and Shah, 1992: paper, figures, and lecture.Hausdorff distance. A3 due. 2/25 No lecture. Midterm Exam: Available on Gradescope 2/25, 9:30 am, due on Gradescope, 2/26, 9:30 am 3/2 - 3/4/21 Last day to drop class (without a 'W' grade), 3/1/2021. Deep learning and computer vision . 3/9 Vision and Language . 3/11, 3/16 Optical flow and Pinhole Model, Binocular Stereo, Lens Equation Lecture video by Margrit Betke. Lecture video by Xide Xia. Thin lens equation for image formation. 3/18 No class. Wellness Day A4 Out 3/19 3/23 Binocular Stereo, Multiview Stereo, Epipolar Geometry, Active Stereo with Structured Light, Photometric Stereo Lecture video by Margrit Betke. Wiki on Lambert's law, Lambertian reflectance, Lecture slides on photometric stereo 3/25, 3/30 Active Contours. Tracking Methods and Applications: Tracking with Alpha-beta Filter, Kalman Filter, Tracking groups of Animals, Multiple Object Tracking and Data Associattion. Bat tracking presentation, Alpha beta filter, Kalman filter. Multiple-Object Tracking A4 Due (3/26). A5 out (3/26). Team Registration Deadline (3/30) 4/1, 4/6, 4/7, 4/13 Guest Lectures - Topics TBA A5 due (4/2) 4/15/21 Lung Image Analysis of COVID19 and Cancer Patients.Absolute Orientation in 2D. Lecture video by Margrit Betke. Absolute Orientation, Horn 89 . 4/20 Lung Image Analysis of COVID-19 and Cancer Patients: CT Scan Alignment Part 2:Quaternions. Horn's 3D Absolute Orientation Algorithm. Iterative Closest Point Algorithm. Lung Surface Alignment. Lecture video by Margrit Betke. Besl and McKay, 1992. Ko and Betke, 2001. Betke, Hong et al., 2003 . 4/22/21 Style Transfer with Neural Models . 4/27/21, 4/29/21 Student Projects: Guidelines, Topics,Schedule Project Deadline (Code, Slides) Wed., May 6, 12-2 Student Presentations Continued (if needed) Labs The links will become active as the semester progresses. 2b1af7f3a8