pwc-archive/papers-with-abstracts huggingface dataset:
Reasoning for Improved Sensor Data Interpretation in a Smart Home
Authors: Marjan Alirezaie, Amy Loutfi
Date: 2014-12-26 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper an ontological representation and reasoning paradigm has been
proposed for interpretation of time-series signals. The signals come from
sensors observing a smart environment. The signal chosen for the annotation
process is a set of unintuitive and complex gas sensor data. The ontology of
this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and
used for representation of both the sensor data and the contextual information.
The interpretation process is mainly done by an incremental ASP solver which as
input receives a logic program that is generated from the contents of the
ontology. The contextual information together with high level domain knowledge
given in the ontology are used to infer explanations (answer sets) for changes
in the ambient air detected by the gas sensors.
Detect2Rank : Combining Object Detectors Using Learning to Rank
Authors: Sezer Karaoglu, Yang Liu, Theo Gevers
Date: 2014-12-26 00:00:00
Tasks: Learning-To-Rank, Object, object-detection, Object Detection

Object detection is an important research area in the field of computer
vision. Many detection algorithms have been proposed. However, each object
detector relies on specific assumptions of the object appearance and imaging
conditions. As a consequence, no algorithm can be considered as universal. With
the large variety of object detectors, the subsequent question is how to select
and combine them.
  In this paper, we propose a framework to learn how to combine object
detectors. The proposed method uses (single) detectors like DPM, CN and EES,
and exploits their correlation by high level contextual features to yield a
combined detection list.
  Experiments on the PASCAL VOC07 and VOC10 datasets show that the proposed
method significantly outperforms single object detectors, DPM (8.4%), CN (6.8%)
and EES (17.0%) on VOC07 and DPM (6.5%), CN (5.5%) and EES (16.2%) on VOC10.
Unsupervised Learning through Prediction in a Model of Cortex
Authors: Christos H. Papadimitriou, Santosh S. Vempala
Date: 2014-12-26 00:00:00

We propose a primitive called PJOIN, for "predictive join," which combines
and extends the operations JOIN and LINK, which Valiant proposed as the basis
of a computational theory of cortex. We show that PJOIN can be implemented in
Valiant's model. We also show that, using PJOIN, certain reasonably complex
learning and pattern matching tasks can be performed, in a way that involves
phenomena which have been observed in cognition and the brain, namely
memory-based prediction and downward traffic in the cortical hierarchy.
A Novel Feature Selection and Extraction Technique for Classification
Authors: Kratarth Goel, Raunaq Vohra, Ainesh Bakshi
Date: 2014-12-26 00:00:00
Tasks: Classification, feature selection, General Classification, Handwritten Digit Recognition, Text Categorization

This paper presents a versatile technique for the purpose of feature
selection and extraction - Class Dependent Features (CDFs). We use CDFs to
improve the accuracy of classification and at the same time control
computational expense by tackling the curse of dimensionality. In order to
demonstrate the generality of this technique, it is applied to handwritten
digit recognition and text categorization.
Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
Authors: Kratarth Goel, Raunaq Vohra, J. K. Sahoo
Date: 2014-12-26 00:00:00
Tasks: Music Generation

In this paper, we propose a generic technique to model temporal dependencies
and sequences using a combination of a recurrent neural network and a Deep
Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state
of the RNN that allows it to provide temporal information and a multi-layer DBN
that helps in high level representation of the data. This makes RNN-DBNs ideal
for sequence generation. Further, the use of a DBN in conjunction with the RNN
makes this model capable of significantly more complex data representation than
an RBM. We apply this technique to the task of polyphonic music generation.
Visualizing and Comparing Convolutional Neural Networks
Authors: Wei Yu, Kuiyuan Yang, Yalong Bai, Hongxun Yao, Yong Rui
Date: 2014-12-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) have achieved comparable error rates to
well-trained human on ILSVRC2014 image classification task. To achieve better
performance, the complexity of CNNs is continually increasing with deeper and
bigger architectures. Though CNNs achieved promising external classification
behavior, understanding of their internal work mechanism is still limited. In
this work, we attempt to understand the internal work mechanism of CNNs by
probing the internal representations in two comprehensive aspects, i.e.,
visualizing patches in the representation spaces constructed by different
layers, and visualizing visual information kept in each layer. We further
compare CNNs with different depths and show the advantages brought by deeper
architecture.
Structure Tensor Based Image Interpolation Method
Authors: Ahmadreza Baghaie, Zeyun Yu
Date: 2014-02-22 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Feature preserving image interpolation is an active area in image processing
field. In this paper a new direct edge directed image super-resolution
algorithm based on structure tensors is proposed. Using an isotropic Gaussian
filter, the structure tensor at each pixel of the input image is computed and
the pixels are classified to three distinct classes; uniform region, corners
and edges, according to the eigenvalues of the structure tensor. Due to
application of the isotropic Gaussian filter, the classification is robust to
noise presented in image. Based on the tangent eigenvector of the structure
tensor, the edge direction is determined and used for interpolation along the
edges. In comparison to some previous edge directed image interpolation
methods, the proposed method achieves higher quality in both subjective and
objective aspects. Also the proposed method outperforms previous methods in
case of noisy and JPEG compressed images. Furthermore, without the need for
optimization in the process, the algorithm can achieve higher speed.
Improved texture image classification through the use of a corrosion-inspired cellular automaton
Authors: Núbia Rosa da Silva, Pieter Van der Weeën, Bernard De Baets, Odemir Martinez Bruno
Date: 2014-12-26 00:00:00
Tasks: General Classification, image-classification, Image Classification, Texture Classification

In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.
Sparkle Vision: Seeing the World through Random Specular Microfacets
Authors: Zhengdong Zhang, Phillip Isola, Edward H. Adelson
Date: 2014-12-26 00:00:00

In this paper, we study the problem of reproducing the world lighting from a
single image of an object covered with random specular microfacets on the
surface. We show that such reflectors can be interpreted as a randomized
mapping from the lighting to the image. Such specular objects have very
different optical properties from both diffuse surfaces and smooth specular
objects like metals, so we design special imaging system to robustly and
effectively photograph them. We present simple yet reliable algorithms to
calibrate the proposed system and do the inference. We conduct experiments to
verify the correctness of our model assumptions and prove the effectiveness of
our pipeline.
Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation
Authors: Marcos W. S. Oliveira, Dalcimar Casanova, João B. Florindo, Odemir Martinez Bruno
Date: 2014-12-26 00:00:00

This work proposes to obtain novel fractal descriptors from gray-level
texture images by combining information from interior and boundary measures of
the Minkowski dilation applied to the texture surface. At first, the image is
converted into a surface where the height of each point is the gray intensity
of the respective pixel in that position in the image. Thus, this surface is
morphologically dilated by spheres. The radius of such spheres is ranged within
an interval and the volume and the external area of the dilated structure are
computed for each radius. The final descriptors are given by such measures
concatenated and subject to a canonical transform to reduce the dimensionality.
The proposal is an enhancement to the classical Bouligand-Minkowski fractal
descriptors, where only the volume (interior) information is considered. As
different structures may have the same volume, but not the same area, the
proposal yields to more rich descriptors as confirmed by results on the
classification of benchmark databases.
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis
Authors: Álvaro Gomez Z., João B. Florindo, Odemir M. Bruno
Date: 2014-12-25 00:00:00
Tasks: General Classification, Texture Classification

Texture analysis and classification remain as one of the biggest challenges
for the field of computer vision and pattern recognition. On this matter, Gabor
wavelets has proven to be a useful technique to characterize distinctive
texture patterns. However, most of the approaches used to extract descriptors
of the Gabor magnitude space usually fail in representing adequately the
richness of detail present into a unique feature vector. In this paper, we
propose a new method to enhance the Gabor wavelets process extracting a fractal
signature of the magnitude spaces. Each signature is reduced using a canonical
analysis function and concatenated to form the final feature vector.
Experiments were conducted on several texture image databases to prove the
power and effectiveness of the proposed method. Results obtained shown that
this method outperforms other early proposed method, creating a more reliable
technique for texture feature extraction.
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
Authors: João Batista Florindo, Odemir Martinez Bruno
Date: 2014-12-25 00:00:00
Tasks: General Classification, Texture Classification

In this work, we propose a novel technique for obtaining descriptors of
gray-level texture images. The descriptors are provided by applying a
multiscale transform to the fractal dimension of the image estimated through
the probability (Voss) method. The effectiveness of the descriptors is verified
in a classification task using benchmark over texture datasets. The results
obtained demonstrate the efficiency of the proposed method as a tool for the
description and discrimination of texture images.
Brachiaria species identification using imaging techniques based on fractal descriptors
Authors: João Batista Florindo, Núbia Rosa da Silva, Liliane Maria Romualdo, Fernanda de Fátima da Silva, Pedro Henrique de Cerqueira Luz, Valdo Rodrigues Herling, Odemir Martinez Bruno
Date: 2014-12-25 00:00:00

The use of a rapid and accurate method in diagnosis and classification of
species and/or cultivars of forage has practical relevance, scientific and
trade in various areas of study. Thus, leaf samples of fodder plant species
\textit{Brachiaria} were previously identified, collected and scanned to be
treated by means of artificial vision to make the database and be used in
subsequent classifications. Forage crops used were: \textit{Brachiaria
decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard;
\textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria
arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed
by the fractal descriptors method, where a set of measures are obtained from
the values of the fractal dimension at different scales. Therefore such values
are used as inputs for a state-of-the-art classifier, the Support Vector
Machine, which finally discriminates the images according to the respective
species.
Texture analysis using volume-radius fractal dimension
Authors: André R. Backes, Odemir M. Bruno
Date: 2014-12-25 00:00:00
Tasks: Texture Classification

Texture plays an important role in computer vision. It is one of the most
important visual attributes used in image analysis, once it provides
information about pixel organization at different regions of the image. This
paper presents a novel approach for texture characterization, based on
complexity analysis. The proposed approach expands the idea of the Mass-radius
fractal dimension, a method originally developed for shape analysis, to a set
of coordinates in 3D-space that represents the texture under analysis in a
signature able to characterize efficiently different texture classes in terms
of complexity. An experiment using images from the Brodatz album illustrates
the method performance.
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
Authors: Philip Lenz, Andreas Geiger, Raquel Urtasun
Date: 2014-07-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Multiple Object Tracking

One of the most popular approaches to multi-target tracking is
tracking-by-detection. Current min-cost flow algorithms which solve the data
association problem optimally have three main drawbacks: they are
computationally expensive, they assume that the whole video is given as a
batch, and they scale badly in memory and computation with the length of the
video sequence. In this paper, we address each of these issues, resulting in a
computationally and memory-bounded solution. First, we introduce a dynamic
version of the successive shortest-path algorithm which solves the data
association problem optimally while reusing computation, resulting in
significantly faster inference than standard solvers. Second, we address the
optimal solution to the data association problem when dealing with an incoming
stream of data (i.e., online setting). Finally, we present our main
contribution which is an approximate online solution with bounded memory and
computation which is capable of handling videos of arbitrarily length while
performing tracking in real time. We demonstrate the effectiveness of our
algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art
performance, while being significantly faster than existing solvers.
Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14)
Authors: MAC Jiffriya, MAC Akmal Jahan, Roshan G. Ragel
Date: 2014-12-25 00:00:00

Plagiarism is known as illegal use of others' part of work or whole work as
one's own in any field such as art, poetry, literature, cinema, research and
other creative forms of study. Plagiarism is one of the important issues in
academic and research fields and giving more concern in academic systems. The
situation is even worse with the availability of ample resources on the web.
This paper focuses on an effective plagiarism detection tool on identifying
suitable intra-corpal plagiarism detection for text based assignments by
comparing unigram, bigram, trigram of vector space model with cosine similarity
measure. Manually evaluated, labelled dataset was tested using unigram, bigram
and trigram vector. Even though trigram vector consumes comparatively more
time, it shows better results with the labelled data. In addition, the selected
trigram vector space model with cosine similarity measure is compared with
tri-gram sequence matching technique with Jaccard measure. In the results,
cosine similarity score shows slightly higher values than the other. Because,
it focuses on giving more weight for terms that do not frequently exist in the
dataset and cosine similarity measure using trigram technique is more
preferable than the other. Therefore, we present our new tool and it could be
used as an effective tool to evaluate text based electronic assignments and
minimize the plagiarism among students.
Improved Parameter Identification Method Based on Moving Rate
Authors: Chol Man Ho, Son Il Gwak, Song Ho Pak, Jong Won Ha
Date: 2014-12-25 00:00:00

To improve the problem that the parameter identification for fuzzy neural
network has many time complexities in calculating, an improved T-S fuzzy
inference method and an parameter identification method for fuzzy neural
network are proposed. It mainly includes three parts. First, improved fuzzy
inference method based on production term for T-S Fuzzy model is explained.
Then, compared with existing Sugeno fuzzy inference based on Compositional
rules and type-distance fuzzy inference method, the proposed fuzzy inference
algorithm has a less amount of complexity in calculating and the calculating
process is simple. Next, a parameter identification method for FNN based on
production inference is proposed. Finally, the proposed method is applied for
the precipitation forecast and security situation prediction. Test results
showed that the proposed method significantly improved the effectiveness of
identification, reduced the learning order, time complexity and learning error.
Locating Tables in Scanned Documents for Reconstructing and Republishing (ICIAfS14)
Authors: Akmal Jahan Mac, Roshan G. Ragel
Date: 2014-12-24 00:00:00

Pool of knowledge available to the mankind depends on the source of learning
resources, which can vary from ancient printed documents to present electronic
material. The rapid conversion of material available in traditional libraries
to digital form needs a significant amount of work if we are to maintain the
format and the look of the electronic documents as same as their printed
counterparts. Most of the printed documents contain not only characters and its
formatting but also some associated non text objects such as tables, charts and
graphical objects. It is challenging to detect them and to concentrate on the
format preservation of the contents while reproducing them. To address this
issue, we propose an algorithm using local thresholds for word space and line
height to locate and extract all categories of tables from scanned document
images. From the experiments performed on 298 documents, we conclude that our
algorithm has an overall accuracy of about 75% in detecting tables from the
scanned document images. Since the algorithm does not completely depend on rule
lines, it can detect all categories of tables in a range of scanned documents
with different font types, styles and sizes to extract their formatting
features. Moreover, the algorithm can be applied to locate tables in multi
column layouts with small modification in layout analysis. Treating tables with
their existing formatting features will tremendously help the reproducing of
printed documents for reprinting and updating purposes.
A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)
Authors: G. I. Gunarathna, M. A. P. Chamikara, R. G. Ragel
Date: 2014-12-24 00:00:00

Character recognition techniques for printed documents are widely used for
English language. However, the systems that are implemented to recognize Asian
languages struggle to increase the accuracy of recognition. Among other Asian
languages (such as Arabic, Tamil, Chinese), Sinhala characters are unique,
mainly because they are round in shape. This unique feature makes it a
challenge to extend the prevailing techniques to improve recognition of Sinhala
characters. Therefore, a little attention has been given to improve the
accuracy of Sinhala character recognition. A novel method, which makes use of
this unique feature, could be advantageous over other methods. This paper
describes the use of a fuzzy inference system to recognize Sinhala characters.
Feature extraction is mainly focused on distance and intersection measurements
in different directions from the center of the letter making use of the round
shape of characters. The results showed an overall accuracy of 90.7% for 140
instances of letters tested, much better than similar systems.
Inference for Sparse Conditional Precision Matrices
Authors: Jialei Wang, Mladen Kolar
Date: 2014-12-24 00:00:00

Given $n$ i.i.d. observations of a random vector $(X,Z)$, where $X$ is a
high-dimensional vector and $Z$ is a low-dimensional index variable, we study
the problem of estimating the conditional inverse covariance matrix $\Omega(z)
= (E[(X-E[X \mid Z])(X-E[X \mid Z])^T \mid Z=z])^{-1}$ under the assumption
that the set of non-zero elements is small and does not depend on the index
variable. We develop a novel procedure that combines the ideas of the local
constant smoothing and the group Lasso for estimating the conditional inverse
covariance matrix. A proximal iterative smoothing algorithm is used to solve
the corresponding convex optimization problems. We prove that our procedure
recovers the conditional independence assumptions of the distribution $X \mid
Z$ with high probability. This result is established by developing a uniform
deviation bound for the high-dimensional conditional covariance matrix from its
population counterpart, which may be of independent interest. Furthermore, we
develop point-wise confidence intervals for individual elements of the
conditional inverse covariance matrix. We perform extensive simulation studies,
in which we demonstrate that our proposed procedure outperforms sensible
competitors. We illustrate our proposal on a S&P 500 stock price data set.
Symmetry in Image Registration and Deformation Modeling
Authors: Stefan Sommer, Henry O. Jacobs
Date: 2014-12-23 00:00:00
Tasks: Image Registration

We survey the role of symmetry in diffeomorphic registration of landmarks,
curves, surfaces, images and higher-order data. The infinite dimensional
problem of finding correspondences between objects can for a range of concrete
data types be reduced resulting in compact representations of shape and spatial
structure. This reduction is possible because the available data is incomplete
in encoding the full deformation model. Using reduction by symmetry, we
describe the reduced models in a common theoretical framework that draws on
links between the registration problem and geometric mechanics. Symmetry also
arises in reduction to the Lie algebra using particle relabeling symmetry
allowing the equations of motion to be written purely in terms of Eulerian
velocity field. Reduction by symmetry has recently been applied for
jet-matching and higher-order discrete approximations of the image matching
problem. We outline these constructions and further cases where reduction by
symmetry promises new approaches to registration of complex data types.