pwc-archive/papers-with-abstracts huggingface dataset:
Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images
Authors: Andrea Baraldi, Dirk Tiede, Stefan Lang
Date: 2017-01-08 00:00:00
Tasks: Color Constancy, Computational Efficiency, Earth Observation, Quantization, Superpixels

Capable of automated near real time superpixel detection and quality
assessment in an uncalibrated monitor typical red green blue (RGB) image,
depicted in either true or false colors, an original low level computer vision
(CV) lightweight computer program, called RGB Image Automatic Mapper (RGBIAM),
is designed and implemented. Constrained by the Calibration Validation (CalVal)
requirements of the Quality Assurance Framework for Earth Observation (QA4EO)
guidelines, RGBIAM requires as mandatory an uncalibrated RGB image pre
processing first stage, consisting of an automated statistical model based
color constancy algorithm. The RGBIAM hybrid inference pipeline comprises: (I)
a direct quantitative to nominal (QN) RGB variable transform, where RGB pixel
values are mapped onto a prior dictionary of color names, equivalent to a
static polyhedralization of the RGB cube. Prior color naming is the deductive
counterpart of inductive vector quantization (VQ), whose typical VQ error
function to minimize is a root mean square error (RMSE). In the output multi
level color map domain, superpixels are automatically detected in linear time
as connected sets of pixels featuring the same color label. (II) An inverse
nominal to quantitative (NQ) RGB variable transform, where a superpixelwise
constant RGB image approximation is generated in linear time to assess a VQ
error image. The hybrid direct and inverse RGBIAM QNQ transform is: (i) general
purpose, data and application independent. (ii) Automated, i.e., it requires no
user machine interaction. (iii) Near real time, with a computational complexity
increasing linearly with the image size. (iv) Implemented in tile streaming
mode, to cope with massive images. Collected outcome and process quality
indicators, including degree of automation, computational efficiency, VQ rate
and VQ error, are consistent with theoretical expectations.
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation
Authors: Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang
Date: 2017-01-08 00:00:00
Tasks: Earth Observation, General Classification, Land Cover Classification, Scene Classification, Time Series, Time Series Analysis

The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product as a multispectral (MS) image corrected for geometric, atmospheric,
adjacency and topographic effects, stacked with its scene classification map
(SCM) whose legend includes quality layers such as cloud and cloud-shadow. No
ESA EO Level 2 product has ever been systematically generated at the ground
segment. To contribute toward filling an information gap from EO big sensory
data to the ESA EO Level 2 product, a Stage 4 validation (Val) of an off the
shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program for
prior knowledge based MS color naming was conducted by independent means. A
time-series of annual Web Enabled Landsat Data (WELD) image composites of the
conterminous U.S. (CONUS) was selected as input dataset. The annual SIAM WELD
maps of the CONUS were validated in comparison with the U.S. National Land
Cover Data (NLCD) 2006 map. These test and reference maps share the same
spatial resolution and spatial extent, but their map legends are not the same
and must be harmonized. For the sake of readability this paper is split into
two. The previous Part 1 Theory provided the multidisciplinary background of a
priori color naming. The present Part 2 Validation presents and discusses Stage
4 Val results collected from the test SIAM WELD map time series and the
reference NLCD map by an original protocol for wall to wall thematic map
quality assessment without sampling, where the test and reference map legends
can differ in agreement with the Part 1. Conclusions are that the SIAM-WELD
maps instantiate a Level 2 SCM product whose legend is the FAO Land Cover
Classification System (LCCS) taxonomy at the Dichotomous Phase (DP) Level 1
vegetation/nonvegetation, Level 2 terrestrial/aquatic or superior LCCS level.
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory
Authors: Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang
Date: 2017-01-08 00:00:00
Tasks: Earth Observation, General Classification, Land Cover Classification, Scene Classification, Time Series Analysis

The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product as a multispectral (MS) image corrected for geometric, atmospheric,
adjacency and topographic effects, stacked with its scene classification map
(SCM), whose legend includes quality layers such as cloud and cloud-shadow. No
ESA EO Level 2 product has ever been systematically generated at the ground
segment. To contribute toward filling an information gap from EO big data to
the ESA EO Level 2 product, an original Stage 4 validation (Val) of the
Satellite Image Automatic Mapper (SIAM) lightweight computer program was
conducted by independent means on an annual Web-Enabled Landsat Data (WELD)
image composite time-series of the conterminous U.S. The core of SIAM is a one
pass prior knowledge based decision tree for MS reflectance space
hyperpolyhedralization into static color names presented in literature in
recent years. For the sake of readability this paper is split into two. The
present Part 1 Theory provides the multidisciplinary background of a priori
color naming in cognitive science, from linguistics to computer vision. To cope
with dictionaries of MS color names and land cover class names that do not
coincide and must be harmonized, an original hybrid guideline is proposed to
identify a categorical variable pair relationship. An original quantitative
measure of categorical variable pair association is also proposed. The
subsequent Part 2 Validation discusses Stage 4 Val results collected by an
original protocol for wall-to-wall thematic map quality assessment without
sampling where the test and reference map legends can differ. Conclusions are
that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4
class taxonomy of the FAO Land Cover Classification System at the Dichotomous
Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
Authors: Vered Shwartz, Enrico Santus, Dominik Schlechtweg
Date: 2016-12-14 00:00:00
Proceeding: EACL 2017 4
Tasks: Hypernym Discovery

The fundamental role of hypernymy in NLP has motivated the development of
many methods for the automatic identification of this relation, most of which
rely on word distribution. We investigate an extensive number of such
unsupervised measures, using several distributional semantic models that differ
by context type and feature weighting. We analyze the performance of the
different methods based on their linguistic motivation. Comparison to the
state-of-the-art supervised methods shows that while supervised methods
generally outperform the unsupervised ones, the former are sensitive to the
distribution of training instances, hurting their reliability. Being based on
general linguistic hypotheses and independent from training data, unsupervised
measures are more robust, and therefore are still useful artillery for
hypernymy detection.
On Classification of Distorted Images with Deep Convolutional Neural Networks
Authors: Yiren Zhou, Sibo Song, Ngai-Man Cheung
Date: 2017-01-08 00:00:00
Tasks: Classification, General Classification

Image blur and image noise are common distortions during image acquisition.
In this paper, we systematically study the effect of image distortions on the
deep neural network (DNN) image classifiers. First, we examine the DNN
classifier performance under four types of distortions. Second, we propose two
approaches to alleviate the effect of image distortion: re-training and
fine-tuning with noisy images. Our results suggest that, under certain
conditions, fine-tuning with noisy images can alleviate much effect due to
distorted inputs, and is more practical than re-training.
Compositional Model based Fisher Vector Coding for Image Classification
Authors: Lingqiao Liu, Peng Wang, Chunhua Shen, Lei Wang, Anton Van Den Hengel, Chao Wang, Heng Tao Shen
Date: 2016-01-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS
Authors: Xun Zhou, Changle Li, Zhe Liu, Tom H. Luan, Zhifang Miao, Lina Zhu, Lei Xiong
Date: 2017-01-08 00:00:00
Tasks: Scheduling, Time Series, Time Series Analysis

The Intelligent Transportation System (ITS) targets to a coordinated traffic
system by applying the advanced wireless communication technologies for road
traffic scheduling. Towards an accurate road traffic control, the short-term
traffic forecasting to predict the road traffic at the particular site in a
short period is often useful and important. In existing works, Seasonal
Autoregressive Integrated Moving Average (SARIMA) model is a popular approach.
The scheme however encounters two challenges: 1) the analysis on related data
is insufficient whereas some important features of data may be neglected; and
2) with data presenting different features, it is unlikely to have one
predictive model that can fit all situations. To tackle above issues, in this
work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we
first explore the autocorrelation and distribution features existed in traffic
flow to revise structure of the time series model. Based on the Gaussian
distribution of traffic flow, a hybrid model with a Bayesian learning algorithm
is developed which can effectively expand the application scenarios of SARIMA.
We show the efficiency and accuracy of our proposal using both analysis and
experimental studies. Using the real-world trace data, we show that the
proposed predicting approach can achieve satisfactory performance in practice.
Improving the Performance of Neural Machine Translation Involving Morphologically Rich Languages
Authors: Krupakar Hans, R S Milton
Date: 2016-12-07 00:00:00
Tasks: Decoder, Machine Translation, Sentence, Translation

The advent of the attention mechanism in neural machine translation models
has improved the performance of machine translation systems by enabling
selective lookup into the source sentence. In this paper, the efficiencies of
translation using bidirectional encoder attention decoder models were studied
with respect to translation involving morphologically rich languages. The
English - Tamil language pair was selected for this analysis. First, the use of
Word2Vec embedding for both the English and Tamil words improved the
translation results by 0.73 BLEU points over the baseline RNNSearch model with
4.84 BLEU score. The use of morphological segmentation before word
vectorization to split the morphologically rich Tamil words into their
respective morphemes before the translation, caused a reduction in the target
vocabulary size by a factor of 8. Also, this model (RNNMorph) improved the
performance of neural machine translation by 7.05 BLEU points over the
RNNSearch model used over the same corpus. Since the BLEU evaluation of the
RNNMorph model might be unreliable due to an increase in the number of matching
tokens per sentence, the performances of the translations were also compared by
means of human evaluation metrics of adequacy, fluency and relative ranking.
Further, the use of morphological segmentation also improved the efficacy of
the attention mechanism.
Sentence-level dialects identification in the greater China region
Authors: Fan Xu, Mingwen Wang, Maoxi Li
Date: 2017-01-08 00:00:00
Tasks: Sentence, Word Alignment

Identifying the different varieties of the same language is more challenging
than unrelated languages identification. In this paper, we propose an approach
to discriminate language varieties or dialects of Mandarin Chinese for the
Mainland China, Hong Kong, Taiwan, Macao, Malaysia and Singapore, a.k.a., the
Greater China Region (GCR). When applied to the dialects identification of the
GCR, we find that the commonly used character-level or word-level uni-gram
feature is not very efficient since there exist several specific problems such
as the ambiguity and context-dependent characteristic of words in the dialects
of the GCR. To overcome these challenges, we use not only the general features
like character-level n-gram, but also many new word-level features, including
PMI-based and word alignment-based features. A series of evaluation results on
both the news and open-domain dataset from Wikipedia show the effectiveness of
the proposed approach.
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
Authors: Gil Ben-Artzi, Yoni Kasten, Shmuel Peleg, Michael Werman
Date: 2015-06-25 00:00:00
Proceeding: CVPR 2016 6
Tasks: Camera Calibration

Computing the epipolar geometry between cameras with very different
viewpoints is often problematic as matching points are hard to find. In these
cases, it has been proposed to use information from dynamic objects in the
scene for suggesting point and line correspondences.
  We propose a speed up of about two orders of magnitude, as well as an
increase in robustness and accuracy, to methods computing epipolar geometry
from dynamic silhouettes. This improvement is based on a new temporal
signature: motion barcode for lines. Motion barcode is a binary temporal
sequence for lines, indicating for each frame the existence of at least one
foreground pixel on that line. The motion barcodes of two corresponding
epipolar lines are very similar, so the search for corresponding epipolar lines
can be limited only to lines having similar barcodes. The use of motion
barcodes leads to increased speed, accuracy, and robustness in computing the
epipolar geometry.
Epipolar Geometry Based On Line Similarity
Authors: Gil Ben-Artzi, Tavi Halperin, Michael Werman, Shmuel Peleg
Date: 2016-04-17 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

It is known that epipolar geometry can be computed from three epipolar line
correspondences but this computation is rarely used in practice since there are
no simple methods to find corresponding lines. Instead, methods for finding
corresponding points are widely used. This paper proposes a similarity measure
between lines that indicates whether two lines are corresponding epipolar lines
and enables finding epipolar line correspondences as needed for the computation
of epipolar geometry.
  A similarity measure between two lines, suitable for video sequences of a
dynamic scene, has been previously described. This paper suggests a stereo
matching similarity measure suitable for images. It is based on the quality of
stereo matching between the two lines, as corresponding epipolar lines yield a
good stereo correspondence.
  Instead of an exhaustive search over all possible pairs of lines, the search
space is substantially reduced when two corresponding point pairs are given.
  We validate the proposed method using real-world images and compare it to
state-of-the-art methods. We found this method to be more accurate by a factor
of five compared to the standard method using seven corresponding points and
comparable to the 8-points algorithm.
Urban Scene Segmentation with Laser-Constrained CRFs
Authors: Charika De Alvis, Lionel Ott, Fabio Ramos
Date: 2017-01-07 00:00:00
Tasks: Scene Segmentation, Segmentation

Robots typically possess sensors of different modalities, such as colour
cameras, inertial measurement units, and 3D laser scanners. Often, solving a
particular problem becomes easier when more than one modality is used. However,
while there are undeniable benefits to combine sensors of different modalities
the process tends to be complicated. Segmenting scenes observed by the robot
into a discrete set of classes is a central requirement for autonomy as
understanding the scene is the first step to reason about future situations.
Scene segmentation is commonly performed using either image data or 3D point
cloud data. In computer vision many successful methods for scene segmentation
are based on conditional random fields (CRF) where the maximum a posteriori
(MAP) solution to the segmentation can be obtained by inference. In this paper
we devise a new CRF inference method for scene segmentation that incorporates
global constraints, enforcing the sets of nodes are assigned the same class
label. To do this efficiently, the CRF is formulated as a relaxed quadratic
program whose MAP solution is found using a gradient-based optimisation
approach. The proposed method is evaluated on images and 3D point cloud data
gathered in urban environments where image data provides the appearance
features needed by the CRF, while the 3D point cloud data provides global
spatial constraints over sets of nodes. Comparisons with belief propagation,
conventional quadratic programming relaxation, and higher order potential CRF
show the benefits of the proposed method.
Deep Learning for Time-Series Analysis
Authors: John Cristian Borges Gamboa
Date: 2017-01-07 00:00:00
Tasks: Deep Learning, General Classification, speech-recognition, Speech Recognition, Time Series, Time Series Analysis

In many real-world application, e.g., speech recognition or sleep stage
classification, data are captured over the course of time, constituting a
Time-Series. Time-Series often contain temporal dependencies that cause two
otherwise identical points of time to belong to different classes or predict
different behavior. This characteristic generally increases the difficulty of
analysing them. Existing techniques often depended on hand-crafted features
that were expensive to create and required expert knowledge of the field. With
the advent of Deep Learning new models of unsupervised learning of features for
Time-series analysis and forecast have been developed. Such new developments
are the topic of this paper: a review of the main Deep Learning techniques is
presented, and some applications on Time-Series analysis are summaried. The
results make it clear that Deep Learning has a lot to contribute to the field.
Group Visual Sentiment Analysis
Authors: Zeshan Hussain, Tariq Patanam, Hardie Cate
Date: 2017-01-07 00:00:00
Tasks: Clustering, Emotion Classification, General Classification, Pose Estimation, Sentiment Analysis

In this paper, we introduce a framework for classifying images according to
high-level sentiment. We subdivide the task into three primary problems:
emotion classification on faces, human pose estimation, and 3D estimation and
clustering of groups of people. We introduce novel algorithms for matching body
parts to a common individual and clustering people in images based on physical
location and orientation. Our results outperform several baseline approaches.
DeepFace: Face Generation using Deep Learning
Authors: Hardie Cate, Fahim Dalvi, Zeshan Hussain
Date: 2017-01-07 00:00:00
Tasks: Deep Learning, Face Generation, General Classification, Image Generation

We use CNNs to build a system that both classifies images of faces based on a
variety of different facial attributes and generates new faces given a set of
desired facial characteristics. After introducing the problem and providing
context in the first section, we discuss recent work related to image
generation in Section 2. In Section 3, we describe the methods used to
fine-tune our CNN and generate new images using a novel approach inspired by a
Gaussian mixture model. In Section 4, we discuss our working dataset and
describe our preprocessing steps and handling of facial attributes. Finally, in
Sections 5, 6 and 7, we explain our experiments and results and conclude in the
following section. Our classification system has 82\% test accuracy.
Furthermore, our generation pipeline successfully creates well-formed faces.
Sign Language Recognition Using Temporal Classification
Authors: Hardie Cate, Fahim Dalvi, Zeshan Hussain
Date: 2017-01-07 00:00:00
Tasks: Classification, General Classification, Position, Sequential Pattern Mining, Sign Language Recognition, Sign Language Translation, Translation

Devices like the Myo armband available in the market today enable us to
collect data about the position of a user's hands and fingers over time. We can
use these technologies for sign language translation since each sign is roughly
a combination of gestures across time. In this work, we utilize a dataset
collected by a group at the University of South Wales, which contains
parameters, such as hand position, hand rotation, and finger bend, for 95
unique signs. For each input stream representing a sign, we predict which sign
class this stream falls into. We begin by implementing baseline SVM and
logistic regression models, which perform reasonably well on high quality data.
Lower quality data requires a more sophisticated approach, so we explore
different methods in temporal classification, including long short term memory
architectures and sequential pattern mining methods.
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English
Authors: Mohaddeseh Bastan, Shahram Khadivi, Mohammad Mehdi Homayounpour
Date: 2017-01-07 00:00:00
Tasks: Machine Translation, NMT, Translation, Transliteration, Word Alignment

Neural Machine Translation (NMT) is a new approach for Machine Translation
(MT), and due to its success, it has absorbed the attention of many researchers
in the field. In this paper, we study NMT model on Persian-English language
pairs, to analyze the model and investigate the appropriateness of the model
for scarce-resourced scenarios, the situation that exists for Persian-centered
translation systems. We adjust the model for the Persian language and find the
best parameters and hyper parameters for two tasks: translation and
transliteration. We also apply some preprocessing task on the Persian dataset
which yields to increase for about one point in terms of BLEU score. Also, we
have modified the loss function to enhance the word alignment of the model.
This new loss function yields a total of 1.87 point improvements in terms of
BLEU score in the translation quality.