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Cs6476 final project

Fig 1. Using the groundtruth evaluation code, I was able to gradually improve my image detection and matching algorithm both in its accuracy and efficiency. A couple main strategies include parameter tuning, expansion on textbook subprocedures adaptive non-maximum suppression and the introduction of alternative implementation strategies, all of which I will detail below.

Note that while this part differs from David Lowe's original SIFT detection algorithm, with some additional finetuning and alternative approximation we are able to still achieve robust results our testing images. The steps are conveniently shown below. The following snipper shows that we can using first-order derivatives to approximate values for second-order derivatives and subsequently the Harris corner response function in a very efficient manner.

Fig 2. Feature detection for Notre Dame with non-maximum suppression. A key stage of the SIFT pipeline is constructing the feature descriptor. My implementation follows the suggested algorithm found in Professor Hays's Lecture slides and the Szeliski textbook. The image had to be padded in all sides to accomodate for the sliding 16x16 window that computes a gradient histogram of 8 bins that describes the dominant gradient of all pixels within a 4x4 subcell.

Everything is pretty straightforward and standard here except for fine-tuning the gaussian parameters and some improvements to optimize the descriptor's effectiveness. This part takes the feature descriptors from the last stage and finds matching between sets of descriptors of the two images by some method of distance evaluation.

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Calculating the nearest neighbor distance ratio NNDR from the textbook was rather straightforward, so I went with the following extensions: Extra credit:. Fig 3. Interesting Findings - It's particularly amusing to notice some unexpected results. For instance, perhaps because PCA is only applied at the feature matching stage, we don't see a time improvement and in most cases actually observe a worsened time due to the extra step of dimensionality reduction maybe?

Project 2: Local Feature Matching Fig 1. Feature Matching This part takes the feature descriptors from the last stage and finds matching between sets of descriptors of the two images by some method of distance evaluation. Also tried dsearchnbut it only gives x coordinates back and not corresponding y so I had to pivot.

Experimented with NNDR with not just the second closest matching feature but a combination of 1st-3rd, 2nd-3rd, and so forth. The conclusion was that for some images and combination of parameters in feature detection that yielded generally low number but more accurate keypoints, it actually improved overall matching effectiveness as it yields lower number false negatives, but in most cases accuracy went down by a bit.

Create a lower dimensional descriptor that is still accurate enough. By running Principle Component Analysis PCA on the original feature vector and extracting the eigenvectors of the coefficient matrix, we can use the eigenvalues from PCA to compress our feature vector by factors of 2 to fasten the matchijng process.

cs6476 final project

In proj2.Comments, questions to James Hays. We'll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, boundary detection, and recognition.

The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the projects. The Advanced Computer Vision course CS in spring not offered will build on this course and deal with advanced and research related topics in Computer Vision, including Machine Learning, Graphics, and Robotics topics that impact Computer Vision.

Learning Objectives Upon completion of this course, students should be able to: 1. Recognize and describe both the theoretical and practical aspects of computing with images.

Connect issues from Computer Vision to Human Vision 2. Describe the foundation of image formation and image analysis. Understand the basics of 2D and 3D Computer Vision. Become familiar with the major technical approaches involved in computer vision.

Describe various methods used for registration, alignment, and matching in images. Get an exposure to advanced concepts leading to object and scene categorization from images. Build computer vision applications. Prerequisites No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class: Data structures: You'll be writing code that builds representations of images, features, and geometric constructions.

Programming: Projects are to be completed and graded in Python. All project starter code will be in Python. TA's will support questions about Python. If you've never used Python that is OK, as long as you have programming experience. Math: Linear algebra, vector calculus, and probability.

Linear algebra is the most important and students who have not taken a linear algebra course have struggled in the past. However, you have three "late days" for the whole course.

That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late day. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible. These late days are intended to cover unexpected clustering of due dates, travel commitments, interviews, hackathons, etc.

Don't ask for extensions to due dates because we are already giving you a pool of late days to manage yourself. Academic Integrity Academic dishonesty will not be tolerated. This includes cheating, lying about course matters, plagiarism, or helping others commit a violation of the Honor Code. Plagiarism includes reproducing the words of others without both the use of quotation marks and citation.

Students are reminded of the obligations and expectations associated with the Georgia Tech Academic Honor Code and Student Code of Conduct, available online at www. For quizzes, no supporting materials are allowed notes, calculators, phones, etc.

9+ Final Year Project Proposal Examples – PDF

You are expected to implement the core components of each project on your own, but the extra credit opportunties often build on third party data sets or code. That's fine.

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Feel free to include results built on other software, as long as you are clear in your handin that it is not your own work. You should not view or edit anyone else's code.To browse Academia. Skip to main content.

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Log In Sign Up. Noman Akhtar. Our Application is dedicated to our parents of and especially we would also like to dedicate this project to our Project advisors because they guided us in a right direction and gave their precious time to us in completion of this project. We discussed our project with them and they gave better suggestions to improve our project. Special appreciation goes to my supervisor, Rao Faizaan Ali, for his supervision and constant support.

His vital help of useful comments and suggestions during the tentative and proposal works have contributed to the success of this research. Not forgotten, my appreciation to my co-supervisor, Bilal Hassan for his support and awareness about this topic. We would like to express my appreciation to the Department of computer Science Head, Dr. Adnan Abid and also to the Program Director, Dr.

Hafiz Sajid Mehmood for their support and help towards my Bachelor affairs. My acknowledgement also goes to all the technicians and office staffs of School of System and Technology for their co-operations. Heartfelt thanks to all my friends and to those who indirectly contributed in this research, your kindness means a lot to me. Thank you very much. In any event many service providers work simultaneously and it is very hard to manage these providers.

It is also important for event organizer that he has all the contacts details of these service providers so that he can contact them any time to plan an event at given time. To manage all these activity we have developed this software. To get success in the event management business, user should have strong network contacts of service provider.

These contacts are essentially providers of specific services who can be mobilized quickly to participate in any given event.

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To make an event successful event manager needs different service provider like Sound systems services, Lighting providers, Canteen services, stage construction and so on. In present system Event Company has to do all management work manually. They keep all payment information on papers.Over the past few decades, machines have come a long way in their ability to "see".

Some examples are autonomous navigators such as self-driving cars, medical imaging technologies, image search engines, face detection and recognition systems in apps, aids for the visually impaired, control-free video games, and industrial automation systems. In this introductory Computer Vision course, we will learn how to "teach machines to see".

We will explore several fundamental concepts including image formation, feature detection, segmentation, multiple view geometry, recognition, and video processing. We will use these concepts to build applications that aid machines to see the world around them. No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful.

cs6476 final project

The following skills are necessary for this class:. These will involve a combination of conceptual questions and programming problems.

cs6476 final project

The programming problems will provide hands-on experience working with techniques covered in or related to the lectures. All code and written responses must be completed individually and submitted to Canvas. Most problem sets will take significant time to complete.

Please start early. You can also extend a technique, or empirically analyze it. Comparisons between two approaches are also welcome. It is wonderful if you design and evaluate a novel approach to an important existing or new vision problem. Be creative! Students are allowed to use existing code for their projects.

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You must work in teams of Students should maintain a nice, professional looking, visual, self-contained webpage describing their project. We will link to all project pages from the class webpage. The following are deliverables for your project. All the deliverables including the proposal are to be submitted via the project web-page.

The webpage source files should be added to a ZIP folder and uploaded to Canvas. When someone uses your system, what is the expected input to the system, and what is the desired output? Approach: Describe the technical approach you plan to employ. Experiments and results: Describe the experimental setup you will follow, which datasets you will use, which existing code you will exploit, what you will implement yourself, and what you would define as a success for the project.

If you plan on collecting your own data, describe what data collection protocol you will follow.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

cs6476 final project

Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History.

Utility function. There is no need to modify it. Default set to [0, ]. Returns: numpy.

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Use cv2. Sobel to help you in this function. Additionally you should set cv2. Sobel's 'scale' parameter to one eighth. Args: image numpy. Output from cv2.

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Sobel imagecv2. For efficiency, you should apply a convolution-based method similar to the approach used in the last problem sets. Note: Implement this method using the instructions in the lectures and the documentation. Here we assume the kernel window is a square so you will use the same value for both width and height. To implement a Gaussian kernel use cv2. The autograder will use 'uniform'. Default value set to 1 because the autograder does not use this parameter. Returns: tuple: 2-element tuple containing: U numpy.

V numpy. The autograder uses this flag. This flag is not tested but may yield better results in some images. The autograder will pass images with even width and height. When dealing with odd dimensions, the output image should be the result of rounding up the division by 2. Follow the process shown in the lecture 6B-L3.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. It appears that MatConvNet was updated to beta 17 while this project was released and the example networks reformatted to be incompatible with beta Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. No description, website, or topics provided. CSS Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit Fetching latest commit…. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Project 1. CS Computer Vision. Mar 14, Project 2. Project 3. Project 4. Project 5. Project 6.Over the past few decades, machines have come a long way in their ability to "see". Some examples are autonomous navigators such as self-driving cars, medical imaging technologies, image search engines, face detection and recognition systems in apps, aids for the visually impaired, control-free video games, and industrial automation systems.

In this introductory Computer Vision course, we will learn how to "teach machines to see". We will explore several fundamental concepts including image formation, feature detection, segmentation, multiple view geometry, recognition, and video processing.

We will use these concepts to build applications that aid machines to see the world around them. No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:. These will involve a combination of conceptual questions and programming problems.

The programming problems will provide hands-on experience working with techniques covered in or related to the lectures. All code and written responses must be completed individually and submitted to Canvas. Most problem sets will take significant time to complete. Please start early. You can also extend a technique, or empirically analyze it. Comparisons between two approaches are also welcome. It is wonderful if you design and evaluate a novel approach to an important existing or new vision problem.

Be creative!

CS 6476: Computer Vision, Fall 2019

Students are allowed to use existing code for their projects. You must work in teams of Students should maintain a nice, professional looking, visual, self-contained webpage describing their project.

We will link to all project pages from the class webpage.