Tree detection in orchards from VHR satellite images using scale-space theory

2016 ◽  
Author(s):  
Milad Mahour ◽  
Valentyn Tolpekin ◽  
Alfred Stein
Keyword(s):  
Satellite Images ◽  
Scale Space ◽  
Space Theory ◽  
Tree Detection

scholarly journals Stochastic analysis of image acquisition, interpolation and scale-space smoothing

1999 ◽  
Vol 31 (4) ◽  
pp. 855-894 ◽  
Author(s):  
Kalle Åström ◽  
Anders Heyden
Keyword(s):  
Stochastic Analysis ◽  
Traditional Approach ◽  
Image Acquisition ◽  
Scale Space ◽  
Image Features ◽  
Space Theory ◽  
Random Errors ◽  
Feature Detectors ◽  
Stochastic Properties ◽  
Discrete Scale

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors.The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors.The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.

scholarly journals Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 77816-77828 ◽  
Author(s):  
Aftab Khan ◽  
Umair Khan ◽  
Muhammad Waleed ◽  
Ashfaq Khan ◽  
Tariq Kamal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Olive Tree ◽  
Tree Detection ◽  
Detection And Counting

scholarly journals Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images

2013 ◽  
Vol 301 ◽  
pp. 129-141 ◽  
Author(s):  
Jia Zhou ◽  
Christophe Proisy ◽  
Xavier Descombes ◽  
Guerric le Maire ◽  
Yann Nouvellon ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Local Density ◽  
Individual Tree ◽  
Tree Detection ◽  
Eucalyptus Plantations

scholarly journals Selected Applications of Scale Spaces in Microscopic Image Analysis

2015 ◽  
Vol 15 (7) ◽  
pp. 5-12
Author(s):  
Dimiter Prodanov ◽  
Tomasz Konopczynski ◽  
Maciej Trojnar
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Edge Detection ◽  
Scale Space ◽  
Microscopic Image ◽  
Space Theory ◽  
Systematic Treatment ◽  
Microscopic Image Analysis ◽  
Segmentation Methods ◽  
Scale Spaces

Abstract Image segmentation methods can be classified broadly into two classes: intensity-based and geometry-based. Edge detection is the base of many geometry-based segmentation approaches. Scale space theory represents a systematic treatment of the issues of spatially uncorrelated noise with its main application being the detection of edges, using multiple resolution scales, which can be used for subsequent segmentation, classification or encoding. The present paper will give an overview of some recent applications of scale spaces into problems of microscopic image analysis. Particular overviews will be given to Gaussian and alpha-scale spaces. Some applications in the analysis of biomedical images will be presented. The implementation of filters will be demonstrated.

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