From Hw1, I have detected the corner and matching between two 2D images. Now, my task is to find out the projection matrix to map 2D points in first image to second image. To fulfill this task, I need to further run outlier rejections, linear estimation, non-linear estimation. For outliner rejections, I still use the
Month: February 2021
Computer Vision II Homework 3
In this homework, we are again given 2D and 3D points and their correspondence. On top of that, we assume the camera is calibrated and the calibration matrix is given. There are three steps before we finalize our answer to this problem: outlier rejection, linear estimation, and non-linear estimation. For outlier rejection, its maximum number
Computer Vision II Homework 2
In this homework, we are given two sets of points, 3D points in real world and 2D points in a camera image, and their correspondence. Our task is find out the camera projection matrix with both linear estimation and non-linear estimation. Linear estimation is quite straightforward; however, the error is 9.1697 in denormalized space. For
Computer Vision II Homework 1
For the written part of this homework, I need to calculate the intersection between a line and a plane and between a line and a quadric. For the programming part, I am given two images with slightly shift. I need to redo what I have done in one of my homework in last quarter: feature
Algorithm Design and Analysis Theory Homework
This is a collection of all theory problems I have in this class. There are overall twelve submissions in one report file. Some verification codes are all included.
Robotics Introduction Final Project
For this robotics project, we want to derive a robot that can sort out a set of messy objects by their color. To fulfill this functionality, we spent lots of time in tuning the color detection parameters on Robotics Operation System (ROS). Besides that, it is also very challenging for robot to work straight, because
Probabilistic Reasoning and Learning Homework 8
In the last homework, I an calculating the results for a real application with both value iteration and policy iteration. In that application, I need to find a way to derive an algorithm to leave a dungeon made by obstacles `#`. Other than that, convergence for iterative policy evaluation is calculated. Codes are also attached
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Probabilistic Reasoning and Learning Homework 7
The seventh homework is about Viterbi algorithm, Hidden Markov Model, and Mixture Model. The updates and inference procedures for the above-mentioned algorithms are derived and written in the report. Codes are also attached.
Probabilistic Reasoning and Learning Homework 6
In this homework, I am working on Expectation Maximization (EM) Algorithm and Auxiliary function. Handwritten answers and codes are attached in the report.
Probabilistic Reasoning and Learning Homework 5
This homework is mainly about gradient based learning, either first-order gradient or Newton’s Method. I am asked to derive the converging speed and error bounds. After that, two real applications — stock market prediction and handwritten digit classification problems — are given for us to practice. The experiment results and codes are written in report.
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