40,054 research outputs found
Impulsive noise removal from color images with morphological filtering
This paper deals with impulse noise removal from color images. The proposed
noise removal algorithm employs a novel approach with morphological filtering
for color image denoising; that is, detection of corrupted pixels and removal
of the detected noise by means of morphological filtering. With the help of
computer simulation we show that the proposed algorithm can effectively remove
impulse noise. The performance of the proposed algorithm is compared in terms
of image restoration metrics and processing speed with that of common
successful algorithms.Comment: The 6th international conference on analysis of images, social
networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi
A Learning Framework for Morphological Operators using Counter-Harmonic Mean
We present a novel framework for learning morphological operators using
counter-harmonic mean. It combines concepts from morphology and convolutional
neural networks. A thorough experimental validation analyzes basic
morphological operators dilation and erosion, opening and closing, as well as
the much more complex top-hat transform, for which we report a real-world
application from the steel industry. Using online learning and stochastic
gradient descent, our system learns both the structuring element and the
composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1
Airborne LiDAR for DEM generation: some critical issues
Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented
Multi-Scale Morphological Analysis of SDSS DR5 Survey using the Metric Space Technique
Following novel development and adaptation of the Metric Space Technique
(MST), a multi-scale morphological analysis of the Sloan Digital Sky Survey
(SDSS) Data Release 5 (DR5) was performed. The technique was adapted to perform
a space-scale morphological analysis by filtering the galaxy point
distributions with a smoothing Gaussian function, thus giving quantitative
structural information on all size scales between 5 and 250 Mpc. The analysis
was performed on a dozen slices of a volume of space containing many newly
measured galaxies from the SDSS DR5 survey. Using the MST, observational data
were compared to galaxy samples taken from N-body simulations with current best
estimates of cosmological parameters and from random catalogs. By using the
maximal ranking method among MST output functions we also develop a way to
quantify the overall similarity of the observed samples with the simulated
samples
Detection of leaf structures in close-range hyperspectral images using morphological fusion
Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods
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