Review of Local Descriptor in RGB-D Object Recognition
Abstract: The emergence of an
RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB
images gives hope to the computer vision community. Moreover, the use of local
features began to increase over the last few years and has shown impressive
results, especially in the field of object recognition. This article attempts
to provide a survey of the recent technical achievements in this area of
research. We review the use of local descriptors as the feature representation
which is extracted from RGB-D images, in instances and category-level object
recognition. We also highlight the involvement of depth images and how they can
be combined with RGB images in constructing a local descriptor. Three different
approaches are used in involving depth images into compact feature
representation, that is classical approach using distribution based,
kernel-trick, and feature learning. In this article, we show that the
involvement of depth data successfully improves the accuracy of object
recognition.
Author: Ema Rachmawati, Iping
Supriana Suwardi, Masayu Leylia Khodra
Journal Code: jptkomputergg140106
