204,113 research outputs found
Unsupervised edge map scoring: a statistical complexity approach
We propose a new Statistical Complexity Measure (SCM) to qualify edge maps
without Ground Truth (GT) knowledge. The measure is the product of two indices,
an \emph{Equilibrium} index obtained by projecting the edge map
into a family of edge patterns, and an \emph{Entropy} index ,
defined as a function of the Kolmogorov Smirnov (KS) statistic.
This new measure can be used for performance characterization which includes:
(i)~the specific evaluation of an algorithm (intra-technique process) in order
to identify its best parameters, and (ii)~the comparison of different
algorithms (inter-technique process) in order to classify them according to
their quality.
Results made over images of the South Florida and Berkeley databases show
that our approach significantly improves over Pratt's Figure of Merit (PFoM)
which is the objective reference-based edge map evaluation standard, as it
takes into account more features in its evaluation
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio
An Integrated Content and Metadata based Retrieval System for Art
In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type
- …