22,029 research outputs found
Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images
An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the MumfordâShah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments
An Efficient Vein Pattern-based Recognition System
This paper presents an efficient human recognition system based on vein
pattern from the palma dorsa. A new absorption based technique has been
proposed to collect good quality images with the help of a low cost camera and
light source. The system automatically detects the region of interest from the
image and does the necessary preprocessing to extract features. A Euclidean
Distance based matching technique has been used for making the decision. It has
been tested on a data set of 1750 image samples collected from 341 individuals.
The accuracy of the verification system is found to be 99.26% with false
rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
On morphological hierarchical representations for image processing and spatial data clustering
Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing
Quantale Modules and their Operators, with Applications
The central topic of this work is the categories of modules over unital
quantales. The main categorical properties are established and a special class
of operators, called Q-module transforms, is defined. Such operators - that
turn out to be precisely the homomorphisms between free objects in those
categories - find concrete applications in two different branches of image
processing, namely fuzzy image compression and mathematical morphology
The Discrete radon transform: A more efficient approach to image reconstruction
The Radon transform and its inversion are the mathematical keys that enable tomography. Radon transforms are defined for continuous objects with continuous projections at all angles in [0,Ï). In practice, however, we pre-filter discrete projections take
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors
We describe a novel approach to image based localisation in urban
environments using semantic matching between images and a 2-D map. It contrasts
with the vast majority of existing approaches which use image to image database
matching. We use highly compact binary descriptors to represent semantic
features at locations, significantly increasing scalability compared with
existing methods and having the potential for greater invariance to variable
imaging conditions. The approach is also more akin to human map reading, making
it more suited to human-system interaction. The binary descriptors indicate the
presence or not of semantic features relating to buildings and road junctions
in discrete viewing directions. We use CNN classifiers to detect the features
in images and match descriptor estimates with a database of location tagged
descriptors derived from the 2-D map. In isolation, the descriptors are not
sufficiently discriminative, but when concatenated sequentially along a route,
their combination becomes highly distinctive and allows localisation even when
using non-perfect classifiers. Performance is further improved by taking into
account left or right turns over a route. Experimental results obtained using
Google StreetView and OpenStreetMap data show that the approach has
considerable potential, achieving localisation accuracy of around 85% using
routes corresponding to approximately 200 meters.Comment: 8 pages, submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 201
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