Fast and Accurate Feature-based Region Identification

Description

– Fast and Accurate Feature-based Region Identification –

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Abstract

There have been several improvements in object detection and semantic segmentation results in recent years. Baseline systems that drives these advances are Fast/Faster R-CNN, Fully Convolutional Network and recently Mask R-CNN and its variant that has a weight transfer function. Mask R-CNN is the state-of-art.

This research extends the application of the state-of-art in object detection and semantic segmentation in drone-based datasets. Existing drone datasets was used to learn semantic segmentation on drone images using Mask R-CNN. 

Introduction

Images and videos are collected everyday by different sources. Recognizing objects, segmenting localizing and classifying them has been a major area of interest in computer vision.

Significant progress has been made commencing from use of low-level image features, such asscale invariant feature transform SIFT [Lowe, 2004] and histogram of oriented gradients HOG [Dalal and Triggs, 2005],

in sophisticated machine learning frameworks to the use of multi-layer convolutional networks to compute highly discriminative, and invariant features [Girshick et al., 2015].

SIFT and HOG are feature descriptor and semi-local orientation histograms that counts occurrences of gradient orientation in localized portions of an image.

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