CS 583: Introduction to Computer Vision CS 583: Introduction to Computer Vision Spring 2017 [] [] [] [] Time/Room Monday 6:00-8:50PM@University Crossings 149 Instructor e-mail: kon drexel.edu office: University Crossing 100G phone: (215) 895-2678 office hours: Monday 1:00-2:00pm or by e-mail appointment TA Paras Wadekar e-mail:psw36 drexel.edu office: Cyber Learning Center office hours: Please check CLC schedule Announcements If you are enrolled in the online section CS583-900 and you do not know where the online presentations are, send the instructor an email. All course materials including lecture slides and notes, and assignments will be posted on BlackboardLearn. [] Syllabus Overview The goal of computer vision is to enable computers see the world. By using a camera as the eye of a computer, studies in computer vision seek to develop better means to capture and extract useful visual information from images and videos and to use such information to automatically interpret the beautiful world surrounding us.

This course provides an introduction to computer vision. The first half of this course will focus on fundamental models and algorithms in computer vision, including such topics as image formation, image sensing, image filtering, edge extraction, brightness and reflectance. In the second half, we will mainly focus on computer vision applications, including various algorithms for reconstructing 3D shape (shape-from-X, stereo, photometric stereo), and recognizing objects in images. Setool2 Lite V.1.11 Download more.

Objectives This course aims for students to (1) understand and apply fundamental mathematical and computational techniques in computer vision and (2) implement basic computer vision applications. Prerequisites Basic (undergraduate-level) understanding of Linear Algebra and Calculus will be necessary. For the assignments, one will need to program in Python (example skeleton codes will be prepared). Assignments will require access to a digital camera. Students are encouraged to purchase one (an inexpensive one will suffice) if he/she does not own one. Topics The following is a list of topics that will be covered in this course. The timeline is preliminary and will most likely change.

Week 1 4/3 Introduction, Image Formation, Image Sensing Week 2 4/10 Camera Models, Projective Geometry (Project 1 assigned) Week 3 4/17 Image Filtering Week 4 4/24 Image Filtering, Edge Detection (Project 1 due) Week 5 5/1 Motion, Mosaicing (Project 2 assigned) Week 6 5/8 Lightness, Radiometry and Reflectance Week 7 5/15 Photometric Stereo, Shape-from-Shading (Project 2 due) Week 8 5/22 Stereo, Structure from Motion (Project 3 assigned) Week 9 5/29 Memorial Day No Class Week 10 6/5 Recognition (Project 3 due) Week 11 6/12 Final Exam Textbook Robot Vision, by B.K.P. Horn, MIT Press, 1986. (ISBN: ) Most of the lectures will follow this book. Although it is not required, it is highly recommended.

Za: Sitemap 9. Sharie Hatchett Bohlmann - Quotes with a View, Sharie Hatchett Bohlmann. Calor y Energia, Editorial Everest. Haralick, L.G. Shapiro, Addison-Wesley 1993. Robot Vision. You can find book Robot Vision Berthold Horn Pdf Download in our library and. Vision b k p horn - cimxngmjfles.wordpress robot vision b k p horn ebook direct link #1 compa Could robot dinner questions thought ebook.

You can order this textbook from Drexel Bookstore. Supplemental readings will be posted in. The following is a list of general computer vision text books recommended (but not required) for supplemental reading. Computer Vision: Algorithms and Applications, by R.

Szeliski, Springer, 2011. Garmin Topo Us 24k Download. Simulazione Modello Unico Persone. (ISBN: 343) Computer Vision: A Modern Approach, by D.A. Forsyth and J. Ponce, Prentice Hall, 2002.

(ISBN: ) A Guided Tour of Computer Vision, by V.S. Nalwa, Addison-Wesley, 1993. Computer Vision: Three-Dimensional Data from Images, by R. Klette, K.Schluns, and A. Koschan, Springer Singapore, 1998. (ISBN: ) Assignments Students will be assigned 3 multi-week individual projects. These projects will bring all aspects of the learned material at each stage.

In each project, graduate students will be required to do additional implementation. The first two of these projects will also be competitions; students will vote for the top 3 artifacts in class.

Those who produced the top 3 artifacts will receive extra credits according to their ranks. See for details. You must be the sole original author of all assignments and examination solutions in their entirety. As the university's policy explains, penalties up to and including a failing grade for the course with no opportunity to withdraw, will be given for plagiarism, fabrication, or cheating*. * The standards for originality in a program are similar to those of other written works. Programs by different authors show clear and substantial differences as judged by most criteria, including but not limited to: choice of variable and procedure names, line spacing and indentation, choice of program structure, choice of algorithms, ordering of modules, module design, and ordering and choice of instructions. The original author of an assignment can explain each detail and how they came to create it on their own.