My implementation of some segmentation algorithms
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Updated
Oct 9, 2018 - Python
My implementation of some segmentation algorithms
Road Segmentation.Image Segmentation using CNN Tensorflow with SegNet
Performance of various image segmentation models.
Pytorch Implementation of Segnet for the LDC dataset.
Detecting the Ego lane of a car in a video stream using OpenCV methods
Lane detection using Semantic Segmentation. To be used in industries by autonomous shuttles in a controlled environment.
Enhancing lane detection systems using deep learning models: U-Net and SegNet for the course ECE-5554 Computer Vision
Semantic Scene Segmentation for Trajectory Prediction
Project implementation of land cover classification problem. This repository contains the implementation of models in pytorch lightning and their results.
CNN architectures capable of extracting the annual density banding present in coral skeletons
Here I solved the problem classification of the skin lesions.
A comparative study for skin lesion segmentation and melanoma detection where deep learning methods can perform very well without complex pre-processing techniques except for normalization and augmentation.
Different CNN Architectures for Medical Image Segmentation task
This project implements semantic image segmentation using two popular convolutional neural network architectures: U-Net and SegNet. Semantic image segmentation involves partitioning an image into multiple segments, each representing a different class.
A study on deep learning methods to identify precise boundaries for robot navigation
Semi-Automatic testing data augmentation techniques for SegNet.
A SegNet model trained for segmentation of Lanes suitable for driving for automobiles.
Implement slightly different caffe-segnet in tensorflow with a cascading architecture
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