scholarly journals Multi-scale Space-time Registration of Growing Plants

2021 ◽  
Author(s):  
Haolin Pan ◽  
Franck Hetroy-Wheeler ◽  
Julie Charlaix ◽  
David Colliaux
Keyword(s):  
Scale Space ◽  
Space Time ◽  
Multi Scale

Multi-scale curvature product for robust image corner detection in curvature scale space

2007 ◽  
Vol 28 (5) ◽  
pp. 545-554 ◽  
Author(s):  
Xiaohong Zhang ◽  
Ming Lei ◽  
Dan Yang ◽  
Yuzhu Wang ◽  
Litao Ma
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Multi Scale ◽  
Curvature Scale Space ◽  
Robust Image

Robust Corner Detection Based on Multi-scale Curvature Product in B-spline Scale Space

2007 ◽  
Vol 33 (4) ◽  
pp. 414-417 ◽  
Author(s):  
Yu-Zhu WANG ◽  
Dan YANG ◽  
Xiao-Hong ZHANG
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
B Spline ◽  
Multi Scale

Investigating Melodic Segmentation through the Temporal Multi-Scaling Framework

2003 ◽  
Vol 7 (1_suppl) ◽  
pp. 125-155
Author(s):  
Maja Serman ◽  
Niall J. L. Griffith
Keyword(s):  
Music Perception ◽  
Signal Propagation ◽  
Scale Space ◽  
Scale Model ◽  
Model Structure ◽  
Multi Scale ◽  
Sensory Signal ◽  
Functional Aspects ◽  
Different Types ◽  
The Subject

In this paper we approach the subject of modelling and understanding segmentation processes in melodic perception using a temporal multi-scale representation framework. We start with the hypothesis that segmentation depends on the ability of the perceptual system to detect changes in the sensory signal. In particular, we are interested in a model of change detection in music perception that would help us to investigate functional aspects of low-level perceptual processes in music and their universality in terms of the general properties of the auditory system. To investigate this hypothesis, we have developed a temporal multi-scale model that mimics the ability of the listener to detect changes in pitch, loudness and timbre when listening to performed melodies. The model is set within the linear scale-space theoretical framework, as developed for image structure analysis but in this case applied to the temporal processing domain. It is structured in such a way as to enable us to verify the assumption that segmentation is influenced by both the dynamics of signal propagation through a neural map and learning and attention factors. Consequently, the model is examined from two perspectives: 1) the computational architecture which models signal propagation is examined for achieving the effects of the universal, inborn aspects of segmentation 2) the model structure capable of influencing choices of segmentation outcomes is explained and some of its effects are examined in view of the known segmentation results. The results of the presented case studies demonstrate that the model accounts for some effects of perceptual organization of the sensory signal and provides a sound basis for analysing different types of changes and coordination across the melodic descriptors in segmentation decisions.

scholarly journals Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

2019 ◽  
Vol 8 (2) ◽  
pp. 72 ◽  
Author(s):  
Yi Qiang ◽  
Nico Van de Weghe
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Space Time ◽  
Unified Framework ◽  
Temporal Modeling ◽  
Multi Scale ◽  
Space And Time ◽  
Pyramid Model ◽  
Temporal And Spatial ◽  
Spatio Temporal

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

AXIOMATIC DERIVATION OF IMAGE PROCESSING MODELS

1994 ◽  
Vol 04 (04) ◽  
pp. 467-475 ◽  
Author(s):  
PIERRE-LOUIS LIONS
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Nonlinear Models ◽  
Nonlinear Partial Differential Equations ◽  
Scale Space ◽  
Scale Analysis ◽  
Multi Scale ◽  
Analysis Scale ◽  
Evolution Type ◽  
Multi Scale Analysis

We briefly review the derivation due to Alvarez, Guichard, Morel and the author of mathematical models in Image Processing. We deduce from classical axions in Computer Vision some nonlinear partial differential equations of evolution type that correspond to general multi-scale analysis (scale-space). We also obtain specific nonlinear models that satisfy additional invariances which are relevant for the analysis of images.

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