2,214 research outputs found
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with
regions of high magnetic activity on the Sun, visible on magnetogram images.
Their complexity is correlated with explosive solar activity and so classifying
these active regions is useful for predicting future solar activity. Current
classification of sunspot groups is visually based and suffers from bias.
Supervised learning methods can reduce human bias but fail to optimally
capitalize on the information present in sunspot images. This paper uses two
image modalities (continuum and magnetogram) to characterize the spatial and
modal interactions of sunspot and magnetic active region images and presents a
new approach to cluster the images. Specifically, in the framework of image
patch analysis, we estimate the number of intrinsic parameters required to
describe the spatial and modal dependencies, the correlation between the two
modalities and the corresponding spatial patterns, and examine the phenomena at
different scales within the images. To do this, we use linear and nonlinear
intrinsic dimension estimators, canonical correlation analysis, and
multiresolution analysis of intrinsic dimension.Comment: 5 pages, 7 figures, accepted to ICIP 201
ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing
Single materials have colors which form straight lines in RGB space. However,
in severe shadow cases, those lines do not intersect the origin, which is
inconsistent with the description of most literature. This paper is concerned
with the detection and correction of the offset between the intersection and
origin. First, we analyze the reason for forming that offset via an optical
imaging model. Second, we present a simple and effective way to detect and
remove the offset. The resulting images, named ORGB, have almost the same
appearance as the original RGB images while are more illumination-robust for
color space conversion. Besides, image processing using ORGB instead of RGB is
free from the interference of shadows. Finally, the proposed offset correction
method is applied to road detection task, improving the performance both in
quantitative and qualitative evaluations.Comment: Project website: https://baidut.github.io/ORGB
Cast shadow modelling and detection
Computer vision applications are often confronted by the need to differentiate between objects and their shadows. A number of shadow detection algorithms have been
proposed in literature, based on physical, geometrical, and other heuristic techniques.
While most of these existing approaches are dependent on the scene environments and
object types, the ones that are not, are classified as superior to others conceptually
and in terms of accuracy. Despite these efforts, the design of a generic, accurate,
simple, and efficient shadow detection algorithm still remains an open problem. In
this thesis, based on a physically-derived hypothesis for shadow identification, novel,
multi-domain shadow detection algorithms are proposed and tested in the spatial and
transform domains.
A novel "Affine Shadow Test Hypothesis" has been proposed, derived, and validated
across multiple environments. Based on that, several new shadow detection algorithms
have been proposed and modelled for short-duration video sequences, where
a background frame is available as a reliable reference, and for long duration video
sequences, where the use of a dedicated background frame is unreliable. Finally, additional
algorithms have been proposed to detect shadows in still images, where the
use of a separate background frame is not possible. In this approach, the author
shows that the proposed algorithms are capable of detecting cast, and self shadows
simultaneously.
All proposed algorithms have been modelled, and tested to detect shadows in the
spatial (pixel) and transform (frequency) domains and are compared against state-of-art approaches, using popular test and novel videos, covering a wide range of
test conditions. It is shown that the proposed algorithms outperform most existing
methods and effectively detect different types of shadows under various lighting and
environmental conditions
Mathematical morphology for tensor data induced by the Loewner orderingin higher dimensions
Positive semidefinite matrix fields are becoming increasingly important in digital imaging. One reason for this tendency consists of the introduction of diffusion tensor magnetic resonance imaging (DTMRI). In order to perform shape analysis, enhancement or segmentation of such tensor fields, appropriate image processing tools must be developed. This paper extends fundamental morphological operations to the matrix-valued setting. We start by presenting novel definitions for the maximum and minimum of a set of matrices since these notions lie at the heart of the morphological operations. In contrast to naive approaches like the component-wise maximum or minimum of the matrix channels, our approach is based on the Loewner ordering for symmetric matrices. The notions of maximum and minimum deduced from this partial ordering satisfy desirable properties such as rotation invariance, preservation of positive semidefiniteness, and continuous dependence on the input data. We introduce erosion, dilation, opening, closing, top hats, morphological derivatives, shock filters, and mid-range filters for positive semidefinite matrix-valued images. These morphological operations incorporate information simultaneously from all matrix channels rather than treating them independently. Experiments on DT-MRI images with ball- and rod-shaped structuring elements illustrate the properties and performance of our morphological operators for matrix-valued data
User-Assisted Image Shadow Removal
This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piece-wise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods
- …