7,758 research outputs found
Adaptive Marginal Median Filter for Colour Images
This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter
Lesion boundary segmentation using level set methods
This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and
a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other
segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician
marked-up boundaries as ground truth
OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects
Counting circular objects such as cell colonies is an important source of
information for biologists. Although this task is often time-consuming and
subjective, it is still predominantly performed manually. The aim of the
present work is to provide a new tool to enumerate circular objects from
digital pictures and video streams. Here, I demonstrate that the created
program, OpenCFU, is very robust, accurate and fast. In addition, it provides
control over the processing parameters and is implemented in an in- tuitive and
modern interface. OpenCFU is a cross-platform and open-source software freely
available at http://opencfu.sourceforge.net
On the Uncertainties of Stellar Mass Estimates via Colour Measurements
Mass-to-light versus colour relations (MLCRs), derived from stellar
population synthesis models, are widely used to estimate galaxy stellar masses
(M) yet a detailed investigation of their inherent biases and limitations
is still lacking. We quantify several potential sources of uncertainty, using
optical and near-infrared (NIR) photometry for a representative sample of
nearby galaxies from the Virgo cluster. Our method for combining multi-band
photometry with MLCRs yields robust stellar masses, while errors in M
decrease as more bands are simultaneously considered. The prior assumptions in
one's stellar population modelling dominate the error budget, creating a
colour-dependent bias of up to 0.6 dex if NIR fluxes are used (0.3 dex
otherwise). This matches the systematic errors associated with the method of
spectral energy distribution (SED) fitting, indicating that MLCRs do not suffer
from much additional bias. Moreover, MLCRs and SED fitting yield similar
degrees of random error (0.1-0.14 dex) when applied to mock galaxies and,
on average, equivalent masses for real galaxies with M 10
M. The use of integrated photometry introduces additional uncertainty
in M measurements, at the level of 0.05-0.07 dex. We argue that using
MLCRs, instead of time-consuming SED fits, is justified in cases with complex
model parameter spaces (involving, for instance, multi-parameter star formation
histories) and/or for large datasets. Spatially-resolved methods for measuring
M should be applied for small sample sizes and/or when accuracies less than
0.1 dex are required. An Appendix provides our MLCR transformations for ten
colour permutations of the filter set.Comment: Accepted to MNRAS. 43 pages, 12 figures, 3 table
Fuzzy metrics and fuzzy logic for colour image filtering
El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido.
En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente.
En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci
Robustifying Vector Median Filter
This paper describes two methods for impulse noise reduction in colour images that outperform the vector median filter from the noise reduction capability point of view. Both methods work by determining first the vector median in a given filtering window. Then, the use of complimentary information from componentwise analysis allows to build robust outputs from more reliable components. The correlation among the colour channels is taken into account in the processing and, as a result, a more robust filter able to process colour images without introducing colour artifacts is obtained. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter. Objective measures demonstrate the goodness of the achieved improvement
Distance Measures for Reduced Ordering Based Vector Filters
Reduced ordering based vector filters have proved successful in removing
long-tailed noise from color images while preserving edges and fine image
details. These filters commonly utilize variants of the Minkowski distance to
order the color vectors with the aim of distinguishing between noisy and
noise-free vectors. In this paper, we review various alternative distance
measures and evaluate their performance on a large and diverse set of images
using several effectiveness and efficiency criteria. The results demonstrate
that there are in fact strong alternatives to the popular Minkowski metrics
Mixed Noise Suppression in Color Images by Signal-Dependent LMS L-Filters
The paper is devoted to the signal-dependent (SD) design of adaptive LMS L-filters with marginal data ordering for color images. The same stem of SD processing of noised grayscale images was applied on noisy color images. Component-wise and multichannel modifications of SD LMS L-filter in R'G'B' (gamma corrected RGB signals) color space were developed. Both modifications for filtering two-dimensional static color images degraded by mixed noise consisting of additive Gaussian white noise and impulsive noise were used. Moreover, single-channel spatial impulse detectors as detectors of impulses and details were used, too. Considering experimental results, SD modifications of L-filters for noisy color images can be concluded to yield the best results
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