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Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios (GSOC 2017)

Introduction

Unstructured information such as video, text, and audio represents the largest and fastest-growing source of information nowadays. The goal of AI is to approach the human abilities to process efficiently the vast amount of Unstructured Data in this complex real world. This project focuses on Object Hypotheses and Segmentation tasks using Symmetry Constraints in cluttered and occluded scenarios, which is developed as Rotational and Bilateral Symmetry Segmentation Analysis Engines on Robosherlock framework. This project enables RoboSherlock to operate in a knowledge-intensive real world. The project was done to fulfill the requirements of Google Summer of Code 2017.

The project is merged with the branch master. It was designed to not use any other library dependencies than the main repo. The system is now able to robustly detect rotational, bilateral symmetries and segment objects

anindex

Instructions

Status

The current state of this repository on branch master is compilable. It was managed to not use any other library dependencies than the main repo. The branch is now able to robustly detect rotational symmetries and segment round object in cluttered scene.

Features

1. Parallel extracting segments from a cloud with different parameters to generate a resource for symmetry detection:

• Graph structures and graph algorithms
• Implementing merging similar segment mechanism