pwc-archive/papers-with-abstracts huggingface dataset:
Learning Lambek grammars from proof frames
Authors: Roberto Bonato, Christian Retoré
Date: 2013-10-02 00:00:00

In addition to their limpid interface with semantics, categorial grammars
enjoy another important property: learnability. This was first noticed by
Buskowsky and Penn and further studied by Kanazawa, for Bar-Hillel categorial
grammars.
  What about Lambek categorial grammars? In a previous paper we showed that
product free Lambek grammars where learnable from structured sentences, the
structures being incomplete natural deductions. These grammars were shown to be
unlearnable from strings by Foret and Le Nir. In the present paper we show that
Lambek grammars, possibly with product, are learnable from proof frames that
are incomplete proof nets.
  After a short reminder on grammatical inference \`a la Gold, we provide an
algorithm that learns Lambek grammars with product from proof frames and we
prove its convergence. We do so for 1-valued also known as rigid Lambek
grammars with product, since standard techniques can extend our result to
$k$-valued grammars. Because of the correspondence between cut-free proof nets
and normal natural deductions, our initial result on product free Lambek
grammars can be recovered.
  We are sad to dedicate the present paper to Philippe Darondeau, with whom we
started to study such questions in Rennes at the beginning of the millennium,
and who passed away prematurely.
  We are glad to dedicate the present paper to Jim Lambek for his 90 birthday:
he is the living proof that research is an eternal learning process.
Improving the Quality of MT Output using Novel Name Entity Translation Scheme
Authors: Deepti Bhalla, Nisheeth Joshi, Iti Mathur
Date: 2013-10-02 00:00:00
Tasks: Machine Translation, Miscellaneous, Translation, Transliteration

This paper presents a novel approach to machine translation by combining the
state of art name entity translation scheme. Improper translation of name
entities lapse the quality of machine translated output. In this work, name
entities are transliterated by using statistical rule based approach. This
paper describes the translation and transliteration of name entities from
English to Punjabi. We have experimented on four types of name entities which
are: Proper names, Location names, Organization names and miscellaneous.
Various rules for the purpose of syllabification have been constructed.
Transliteration of name entities is accomplished with the help of Probability
calculation. N-Gram probabilities for the extracted syllables have been
calculated using statistical machine translation toolkit MOSES.
A Robust Alternating Direction Method for Constrained Hybrid Variational Deblurring Model
Authors: Ryan Wen Liu, Tian Xu
Date: 2013-08-31 00:00:00
Tasks: Deblurring

In this work, a new constrained hybrid variational deblurring model is
developed by combining the non-convex first- and second-order total variation
regularizers. Moreover, a box constraint is imposed on the proposed model to
guarantee high deblurring performance. The developed constrained hybrid
variational model could achieve a good balance between preserving image details
and alleviating ringing artifacts. In what follows, we present the
corresponding numerical solution by employing an iteratively reweighted
algorithm based on alternating direction method of multipliers. The
experimental results demonstrate the superior performance of the proposed
method in terms of quantitative and qualitative image quality assessments.
Online Learning of Dynamic Parameters in Social Networks
Authors: Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie
Date: 2013-10-01 00:00:00
Proceeding: NeurIPS 2013 12

This paper addresses the problem of online learning in a dynamic setting. We
consider a social network in which each individual observes a private signal
about the underlying state of the world and communicates with her neighbors at
each time period. Unlike many existing approaches, the underlying state is
dynamic, and evolves according to a geometric random walk. We view the scenario
as an optimization problem where agents aim to learn the true state while
suffering the smallest possible loss. Based on the decomposition of the global
loss function, we introduce two update mechanisms, each of which generates an
estimate of the true state. We establish a tight bound on the rate of change of
the underlying state, under which individuals can track the parameter with a
bounded variance. Then, we characterize explicit expressions for the steady
state mean-square deviation(MSD) of the estimates from the truth, per
individual. We observe that only one of the estimators recovers the optimal
MSD, which underscores the impact of the objective function decomposition on
the learning quality. Finally, we provide an upper bound on the regret of the
proposed methods, measured as an average of errors in estimating the parameter
in a finite time.
Joint Bayesian estimation of close subspaces from noisy measurements
Authors: Olivier Besson, Nicolas Dobigeon, Jean-Yves Tourneret
Date: 2013-10-01 00:00:00

In this letter, we consider two sets of observations defined as subspace
signals embedded in noise and we wish to analyze the distance between these two
subspaces. The latter entails evaluating the angles between the subspaces, an
issue reminiscent of the well-known Procrustes problem. A Bayesian approach is
investigated where the subspaces of interest are considered as random with a
joint prior distribution (namely a Bingham distribution), which allows the
closeness of the two subspaces to be adjusted. Within this framework, the
minimum mean-square distance estimator of both subspaces is formulated and
implemented via a Gibbs sampler. A simpler scheme based on alternative maximum
a posteriori estimation is also presented. The new schemes are shown to provide
more accurate estimates of the angles between the subspaces, compared to
singular value decomposition based independent estimation of the two subspaces.
The complex-valued encoding for dicision-making based on aliasing data
Authors: P. A. Golovinski, V. A. Astapenko
Date: 2013-10-01 00:00:00

It is proposed a complex valued channel encoding for multidimensional data.
The basic approach contains overlapping of complex nonlinear mappings. Its
development leads to sparse representation of multi-channel data, increasing
their dimensions and the distance between the images.
An Overview and Evaluation of Various Face and Eyes Detection Algorithms for Driver Fatigue Monitoring Systems
Authors: Markan Lopar, Slobodan Ribarić
Date: 2013-10-01 00:00:00
Tasks: Face Detection

In this work various methods and algorithms for face and eyes detection are examined in order to decide which of them are applicable for use in a driver fatigue monitoring system. In the case of face detection the standard Viola-Jones face detector has shown best results, while the method of finding the eye centers by means of gradients has proven to be most appropriate in the case of eyes detection. The later method has also a potential for retrieving behavioral parameters needed for estimation of the level of driver fatigue. This possibility will be examined in future work.
Classifying Traffic Scenes Using The GIST Image Descriptor
Authors: Ivan Sikirić, Karla Brkić, Siniša Šegvić
Date: 2013-10-01 00:00:00
Tasks: Clustering, Descriptive, General Classification

This paper investigates classification of traffic scenes in a very low
bandwidth scenario, where an image should be coded by a small number of
features. We introduce a novel dataset, called the FM1 dataset, consisting of
5615 images of eight different traffic scenes: open highway, open road,
settlement, tunnel, tunnel exit, toll booth, heavy traffic and the overpass. We
evaluate the suitability of the GIST descriptor as a representation of these
images, first by exploring the descriptor space using PCA and k-means
clustering, and then by using an SVM classifier and recording its 10-fold
cross-validation performance on the introduced FM1 dataset. The obtained
recognition rates are very encouraging, indicating that the use of the GIST
descriptor alone could be sufficiently descriptive even when very high
performance is required.
Computer Vision Systems in Road Vehicles: A Review
Authors: Kristian Kovačić, Edouard Ivanjko, Hrvoje Gold
Date: 2013-10-01 00:00:00

The number of road vehicles significantly increased in recent decades. This
trend accompanied a build-up of road infrastructure and development of various
control systems to increase road traffic safety, road capacity and travel
comfort. In traffic safety significant development has been made and today's
systems more and more include cameras and computer vision methods. Cameras are
used as part of the road infrastructure or in vehicles. In this paper a review
on computer vision systems in vehicles from the stand point of traffic
engineering is given. Safety problems of road vehicles are presented, current
state of the art in-vehicle vision systems is described and open problems with
future research directions are discussed.
Global Localization Based on 3D Planar Surface Segments
Authors: Robert Cupec, Emmanuel Karlo Nyarko, Damir Filko, Andrej Kitanov, Ivan Petrović
Date: 2013-10-01 00:00:00

Global localization of a mobile robot using planar surface segments extracted
from depth images is considered. The robot's environment is represented by a
topological map consisting of local models, each representing a particular
location modeled by a set of planar surface segments. The discussed
localization approach segments a depth image acquired by a 3D camera into
planar surface segments which are then matched to model surface segments. The
robot pose is estimated by the Extended Kalman Filter using surface segment
pairs as measurements. The reliability and accuracy of the considered approach
are experimentally evaluated using a mobile robot equipped by a Microsoft
Kinect sensor.
Multiclass Road Sign Detection using Multiplicative Kernel
Authors: Valentina Zadrija, Siniša Šegvić
Date: 2013-10-01 00:00:00
Tasks: Classification, Clustering, General Classification

We consider the problem of multiclass road sign detection using a
classification function with multiplicative kernel comprised from two kernels.
We show that problems of detection and within-foreground classification can be
jointly solved by using one kernel to measure object-background differences and
another one to account for within-class variations. The main idea behind this
approach is that road signs from different foreground variations can share
features that discriminate them from backgrounds. The classification function
training is accomplished using SVM, thus feature sharing is obtained through
support vector sharing. Training yields a family of linear detectors, where
each detector corresponds to a specific foreground training sample. The
redundancy among detectors is alleviated using k-medoids clustering. Finally,
we report detection and classification results on a set of road sign images
obtained from a camera on a moving vehicle.
A Novel Georeferenced Dataset for Stereo Visual Odometry
Authors: Ivan Krešo, Marko Ševrović, Siniša Šegvić
Date: 2013-10-01 00:00:00
Tasks: Camera Calibration, Visual Odometry

In this work, we present a novel dataset for assessing the accuracy of stereo
visual odometry. The dataset has been acquired by a small-baseline stereo rig
mounted on the top of a moving car. The groundtruth is supplied by a consumer
grade GPS device without IMU. Synchronization and alignment between GPS
readings and stereo frames are recovered after the acquisition. We show that
the attained groundtruth accuracy allows to draw useful conclusions in
practice. The presented experiments address influence of camera calibration,
baseline distance and zero-disparity features to the achieved reconstruction
performance.
Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data
Authors: Karla Brkić, Srđan Rašić, Axel Pinz, Siniša Šegvić, Zoran Kalafatić
Date: 2013-10-01 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

This paper proposes combining spatio-temporal appearance (STA) descriptors
with optical flow for human action recognition. The STA descriptors are local
histogram-based descriptors of space-time, suitable for building a partial
representation of arbitrary spatio-temporal phenomena. Because of the
possibility of iterative refinement, they are interesting in the context of
online human action recognition. We investigate the use of dense optical flow
as the image function of the STA descriptor for human action recognition, using
two different algorithms for computing the flow: the Farneb\"ack algorithm and
the TVL1 algorithm. We provide a detailed analysis of the influencing optical
flow algorithm parameters on the produced optical flow fields. An extensive
experimental validation of optical flow-based STA descriptors in human action
recognition is performed on the KTH human action dataset. The encouraging
experimental results suggest the potential of our approach in online human
action recognition.
Flexible Visual Quality Inspection in Discrete Manufacturing
Authors: Tomislav Petković, Darko Jurić, Sven Lončarić
Date: 2013-10-01 00:00:00

Most visual quality inspections in discrete manufacturing are composed of
length, surface, angle or intensity measurements. Those are implemented as
end-user configurable inspection tools that should not require an image
processing expert to set up. Currently available software solutions providing
such capability use a flowchart based programming environment, but do not fully
address an inspection flowchart robustness and can require a redefinition of
the flowchart if a small variation is introduced. In this paper we propose an
acquire-register-analyze image processing pattern designed for discrete
manufacturing that aims to increase the robustness of the inspection flowchart
by consistently addressing variations in product position, orientation and
size. A proposed pattern is transparent to the end-user and simplifies the
flowchart. We describe a developed software solution that is a practical
implementation of the proposed pattern. We give an example of its real-life use
in industrial production of electric components.
Filtering for More Accurate Dense Tissue Segmentation in Digitized Mammograms
Authors: Mario Muštra, Mislav Grgić
Date: 2013-10-01 00:00:00
Tasks: Diagnostic, General Classification, Segmentation

Breast tissue segmentation into dense and fat tissue is important for
determining the breast density in mammograms. Knowing the breast density is
important both in diagnostic and computer-aided detection applications. There
are many different ways to express the density of a breast and good quality
segmentation should provide the possibility to perform accurate classification
no matter which classification rule is being used. Knowing the right breast
density and having the knowledge of changes in the breast density could give a
hint of a process which started to happen within a patient. Mammograms
generally suffer from a problem of different tissue overlapping which results
in the possibility of inaccurate detection of tissue types. Fibroglandular
tissue presents rather high attenuation of X-rays and is visible as brighter in
the resulting image but overlapping fibrous tissue and blood vessels could
easily be replaced with fibroglandular tissue in automatic segmentation
algorithms. Small blood vessels and microcalcifications are also shown as
bright objects with similar intensities as dense tissue but do have some
properties which makes possible to suppress them from the final results. In
this paper we try to divide dense and fat tissue by suppressing the scattered
structures which do not represent glandular or dense tissue in order to divide
mammograms more accurately in the two major tissue types. For suppressing blood
vessels and microcalcifications we have used Gabor filters of different size
and orientation and a combination of morphological operations on filtered image
with enhanced contrast.
Surface Registration Using Genetic Algorithm in Reduced Search Space
Authors: Vedran Hrgetić, Tomislav Pribanić
Date: 2013-10-01 00:00:00
Tasks: Object Recognition, parameter estimation

Surface registration is a technique that is used in various areas such as
object recognition and 3D model reconstruction. Problem of surface registration
can be analyzed as an optimization problem of seeking a rigid motion between
two different views. Genetic algorithms can be used for solving this
optimization problem, both for obtaining the robust parameter estimation and
for its fine-tuning. The main drawback of genetic algorithms is that they are
time consuming which makes them unsuitable for online applications. Modern
acquisition systems enable the implementation of the solutions that would
immediately give the information on the rotational angles between the different
views, thus reducing the dimension of the optimization problem. The paper gives
an analysis of the genetic algorithm implemented in the conditions when the
rotation matrix is known and a comparison of these results with results when
this information is not available.
On the origin of ambiguity in efficient communication
Authors: Jordi Fortuny, Bernat Corominas-Murtra
Date: 2011-07-01 00:00:00

This article studies the emergence of ambiguity in communication through the
concept of logical irreversibility and within the framework of Shannon's
information theory. This leads us to a precise and general expression of the
intuition behind Zipf's vocabulary balance in terms of a symmetry equation
between the complexities of the coding and the decoding processes that imposes
an unavoidable amount of logical uncertainty in natural communication.
Accordingly, the emergence of irreversible computations is required if the
complexities of the coding and the decoding processes are balanced in a
symmetric scenario, which means that the emergence of ambiguous codes is a
necessary condition for natural communication to succeed.
Object Detection Using Keygraphs
Authors: Marcelo Hashimoto, Roberto Marcondes Cesar Junior
Date: 2013-10-01 00:00:00
Tasks: Object, object-detection, Object Detection

We propose a new framework for object detection based on a generalization of
the keypoint correspondence framework. This framework is based on replacing
keypoints by keygraphs, i.e. isomorph directed graphs whose vertices are
keypoints, in order to explore relative and structural information. Unlike
similar works in the literature, we deal directly with graphs in the entire
pipeline: we search for graph correspondences instead of searching for
individual point correspondences and then building graph correspondences from
them afterwards. We also estimate the pose from graph correspondences instead
of falling back to point correspondences through a voting table. The
contributions of this paper are the proposed framework and an implementation
that properly handles its inherent issues of loss of locality and combinatorial
explosion, showing its viability for real-time applications. In particular, we
introduce the novel concept of keytuples to solve a running time issue. The
accuracy of the implementation is shown by results of over 800 experiments with
a well-known database of images. The speed is illustrated by real-time tracking
with two different cameras in ordinary hardware.
Improving CUR Matrix Decomposition and the Nyström Approximation via Adaptive Sampling
Authors: Shusen Wang, Zhihua Zhang
Date: 2013-03-18 00:00:00

The CUR matrix decomposition and the Nystr\"{o}m approximation are two
important low-rank matrix approximation techniques. The Nystr\"{o}m method
approximates a symmetric positive semidefinite matrix in terms of a small
number of its columns, while CUR approximates an arbitrary data matrix by a
small number of its columns and rows. Thus, CUR decomposition can be regarded
as an extension of the Nystr\"{o}m approximation.
  In this paper we establish a more general error bound for the adaptive
column/row sampling algorithm, based on which we propose more accurate CUR and
Nystr\"{o}m algorithms with expected relative-error bounds. The proposed CUR
and Nystr\"{o}m algorithms also have low time complexity and can avoid
maintaining the whole data matrix in RAM. In addition, we give theoretical
analysis for the lower error bounds of the standard Nystr\"{o}m method and the
ensemble Nystr\"{o}m method. The main theoretical results established in this
paper are novel, and our analysis makes no special assumption on the data
matrices.
Structured learning of sum-of-submodular higher order energy functions
Authors: Alexander Fix, Thorsten Joachims, Sam Park, Ramin Zabih
Date: 2013-09-28 00:00:00
Tasks: Interactive Segmentation

Submodular functions can be exactly minimized in polynomial time, and the
special case that graph cuts solve with max flow \cite{KZ:PAMI04} has had
significant impact in computer vision
\cite{BVZ:PAMI01,Kwatra:SIGGRAPH03,Rother:GrabCut04}. In this paper we address
the important class of sum-of-submodular (SoS) functions
\cite{Arora:ECCV12,Kolmogorov:DAM12}, which can be efficiently minimized via a
variant of max flow called submodular flow \cite{Edmonds:ADM77}. SoS functions
can naturally express higher order priors involving, e.g., local image patches;
however, it is difficult to fully exploit their expressive power because they
have so many parameters. Rather than trying to formulate existing higher order
priors as an SoS function, we take a discriminative learning approach,
effectively searching the space of SoS functions for a higher order prior that
performs well on our training set. We adopt a structural SVM approach
\cite{Joachims/etal/09a,Tsochantaridis/etal/04} and formulate the training
problem in terms of quadratic programming; as a result we can efficiently
search the space of SoS priors via an extended cutting-plane algorithm. We also
show how the state-of-the-art max flow method for vision problems
\cite{Goldberg:ESA11} can be modified to efficiently solve the submodular flow
problem. Experimental comparisons are made against the OpenCV implementation of
the GrabCut interactive segmentation technique \cite{Rother:GrabCut04}, which
uses hand-tuned parameters instead of machine learning. On a standard dataset
\cite{Gulshan:CVPR10} our method learns higher order priors with hundreds of
parameter values, and produces significantly better segmentations. While our
focus is on binary labeling problems, we show that our techniques can be
naturally generalized to handle more than two labels.
An information measure for comparing top $k$ lists
Authors: Arun Konagurthu, James Collier
Date: 2013-10-01 00:00:00

Comparing the top $k$ elements between two or more ranked results is a common
task in many contexts and settings. A few measures have been proposed to
compare top $k$ lists with attractive mathematical properties, but they face a
number of pitfalls and shortcomings in practice. This work introduces a new
measure to compare any two top k lists based on measuring the information these
lists convey. Our method investigates the compressibility of the lists, and the
length of the message to losslessly encode them gives a natural and robust
measure of their variability. This information-theoretic measure objectively
reconciles all the main considerations that arise when measuring
(dis-)similarity between lists: the extent of their non-overlapping elements in
each of the lists; the amount of disarray among overlapping elements between
the lists; the measurement of displacement of actual ranks of their overlapping
elements.