This branch collect all the code assignment as well as the report of the ETHz course Computer vison. I get full grade for all these projects.
- Computer vison assignments
- exercise1: Introduction
- Introduction to Pytorch
- exercise2: Image Segmentation
- Mean-Shift
- SegNet
- exercise3: Camera calibration and structure from motion
- DLT Algorithm
- Structure from motion
- exercise4: Model fitting and multi-view vision
- Model Fitting
- Stereo/Multi-view vision
- exercise5: Object Recognition
- Bag-of-words Classifier
- CNN-based Classifier
- exercise6: Tracking
- CONDENSATION: Tracker Based On Color Histograms
- exercise1: Introduction
To protect the property of the teaching staff I only show my reports here, not the code
Introduction to Pytorch, A demo for getting used to pytorch modules
I use the mean shift algorithm with gaussian kernel with 2.5 bandwidth and get following results:
I use the SegNet(Like UNet) and get following results:
The reprojection error is 6.253e-4
The reprojection error is 6.253e-4 I use the global SFM instead of the sequential SFM get better resutls.
I implement the ransac line fitting alogrithm and is robust to the outlier of these yellow points.
I implement the MVS Net for the dense depth estimation task and get good results.
In this task I solve the car recognition task using bag-of-words classifier and conduct the experiment about how different cluster number will influence the results.
In this task I solve the CIFAR-10 Dataset using simplified VGG net and achieve 82% accuracy.
In this task I use the particle tracking alogrithm based On Color Histograms. Below are the results for different tracking challenge.
I really learned a lot during this courses and will keep going forward to dive more in the topic of computer vision and deep learning