nyakasko/LO-RANSAC-3DPlaneFitting

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Created about 4 years ago
Updated almost 4 years ago

nyakasko/LO-RANSAC-3DPlaneFitting

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Implementation of the Locally Optimized Random SAmple Consensus (LO-RANSAC) 3D plane fitting algorithm

ImageFiltersAndUpsampling

Various image filters and Guided Joint bilateral filter for guided image upsampling

icp_tricp

Implementation of Iterative Closest Point and Trimmed Iterative Closest Point algorithms.

ZED_2_change_detection

This code is an initial version of the change detection algorithm developed using the ZED 2 stereo camera. The program was developed by Levente Göncz at SZTAKI (Institute for Computer Science and Control) in a framework of a 4 months long project.

stereo_reconstruction

Estimating the fundamental and essential matrices of input stereo images, and then reconstructing the 3d points by triangulation.

ObjectAndKeypointDetection

In this assignment I have to build a Mask R-CNN based keypoint detector model using Detectron2. Detectron2 was written in PyTorch and contains many state-of-the-art obejct detection models with pretrained weights.

Jupyter Notebook

ComputerVision-RANSAC

Fit RANSAC lines on edge detected images - Sequential RANSAC

ParallelProgramming

Various assignments involving parallel programming for the course called Programming Parallel Computers at Aalto University - http://ppc.cs.aalto.fi/

StereoMatching

Disparity image creation with naive and dynamic programming approaches and 3D pointcloud generation from the disparity image

homography_estimation

Feature matching (SIFT) between two images and then applying normalized linear homography estimation, robustified by standard RANSAC

ImageAndVideoAnalysis

Prewitt edge detector: gradient filter és nonmaxima-suppression (NMS), Thresholding algorithm by Otsu, Detection of circular object by edge detection and Hough transform for circles, Motion tracking of feature points and dense optical flow

AnomalyDetection

In this assignment I have to build a reconstruction loss based anomaly detection model using tf.keras. I have to train an autoencoder to reconstruct Homer Simpson images, then use the trained model to classify Simpson and Flower images.

Jupyter Notebook