This paper presents a novel computational model to detect airports in optical remote sensing
images (RSI). It works in a hierarchical architecture with a coarse layer and a fine layer. At …

Autonomous robot navigation in wild environments is still an open problem and relies
heavily on accurate terrain perception. Traditional machine learning techniques have …

This paper intended to classify land cover of high-resolution satellite image using
supervised classification method. The object of this research was the land cover image of …

In this paper, we propose a novel method to detect glandular structures in microscopic
images of human tissue. We first convert the image from Cartesian space to polar space and …

The insightful effort of this paper to make an impulsive and unhampered an interface
between the physical and virtual world. In general, objects tracked and recognized while …

Semantic segmentation is a typical problem in the field of machine vision. Convolutional
neural networks (CNNs)-based methods all have excellent performance in image semantic …

Semantic image segmentation aims to partition an image into non-overlapping regions and
assign a pre-defined object class label to each region. In this paper, a semantic method …

Image segmentation is one of the most important methods for animal phenome research.
Since the advent of deep learning, many researchers have looked at multilayer …

The feature representation has significantly profound impact on the segmentation accuracy.
This paper proposes a feature extract method for conditional random fields (CRF) to …

This study proposed a land cover classification through an ontology approach from Sentinel-
2 satellite imagery. Five steps were conducted as the research workflow, those were (1) …