My implementation of some segmentation algorithms

Road Segmentation.Image Segmentation using CNN Tensorflow with SegNet

Performance of various image segmentation models.

Detecting the Ego lane of a car in a video stream using OpenCV methods

Проект по сегментации кожных новообразований с использованием архитектур сегментационных сетей.

Lane detection using Semantic Segmentation. To be used in industries by autonomous shuttles in a controlled environment.

Pytorch Implementation of Segnet for the LDC dataset.

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.

Portrait segmentation is to separate the portrait in a picture from the background to form different areas, which are distinguished using different tags.

Image segmentation implemented using pytorch on a COCO format Dataset of Ingredients with various models including U-NET, U-NET++, SegNet and DeepLabV3+

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.

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.