Results 71 to 80 of about 374,369 (195)

Routing in Dense Human Crowds Using Smartphone Movement Data and Optical Aerial Imagery

ISPRS International Journal of Geo-Information, 2015
In this paper, we propose a navigation approach for smartphones that enables visitors of major events to avoid crowded areas or narrow streets and to navigate out of dense crowds quickly. Two types of sensor data are integrated.
Florian Hillen, Oliver Meynberg, Bernhard Höfle   +2 more
doaj   +1 more source

Texture-based cloud classification [PDF]

The purpose of the 1988 ASEE Summer Program has been to broaden the application of texture-based cloud classification approaches to lower spatial resolution GOES imagery, and to design texture-based approaches for determining cloud cover over high albedo
Welch, Ronald M.
core   +1 more source

A Fast Image Stitching Algorithm Based on Texture Classification and Improved SIFT

IEEE Access
A fast and improved scale-invariant feature transform (SIFT) image stitching algorithm is proposed based on texture classification to solve the problem of huge computational complexity.
Zetian Tang, Zemin Zhang, Junjie Feng, Wei Chen, Kun Zhu, Wentao Yang, Wenjuan Han   +6 more
doaj   +1 more source

Texture Segmentation by Evidence Gathering

, 2011
A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes.
Carter, John, Nixon, Mark, Waller, Ben
core  

An Efficient Weakly Supervised Approach for Texture Segmentation via Graph Cuts

Journal of Intelligent Systems, 2013
We propose an approach for texture segmentation based on weak supervised learning. The weak supervision implies that the user marks only a single small patch for each class in the input image. These patches are used for training.
Bhavsar Arnav V.
doaj   +1 more source

Improving Texture Categorization with Biologically Inspired Filtering

, 2013
Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the
Garcia, Christophe, Nguyen, Thanh Phuong, Vu, Ngoc-Son   +2 more
core  

A Theoretical Analysis of Deep Neural Networks for Texture Classification

, 2016
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations.
Basu, Saikat, DiBiano, Robert, Ganguly, Sangram, Gayaka, Shreekant, Karki, Manohar, Mukhopadhyay, Supratik, Nemani, Ramakrishna   +6 more
core   +1 more source

Texture image analysis and texture classification methods - A review

, 2019
Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image.
Armi, Laleh, Fekri-Ershad, Shervan
openaire   +2 more sources

TCvBsISM: Texture Classification via B-Splines-Based Image Statistical Modeling

IEEE Access, 2018
This paper presents an image statistical modeling-based texture classification (TC) approach via the Bayesian-driven B-splines probability density estimation of the image textural surface appearance (ITSA), termed TCvBsISM.
Jinping Liu, Jiezhou He, Wuxia Zhang, Pengfei Xu, Zhaohui Tang   +4 more
doaj   +1 more source

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization [PDF]

, 2012
Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in
Fakiris, E, Rzhanov, Yuri, Zoura, D
core   +1 more source