pwc-archive/papers-with-abstracts huggingface dataset:
Unifying Registration based Tracking: A Case Study with Structural Similarity
Authors: Abhineet Singh, Mennatullah Siam, Martin Jagersand
Date: 2016-07-15 00:00:00

This paper adapts a popular image quality measure called structural
similarity for high precision registration based tracking while also
introducing a simpler and faster variant of the same. Further, these are
evaluated comprehensively against existing measures using a unified approach to
study registration based trackers that decomposes them into three constituent
sub modules - appearance model, state space model and search method. Several
popular trackers in literature are broken down using this method so that their
contributions - as of this paper - are shown to be limited to only one or two
of these submodules. An open source tracking framework is made available that
follows this decomposition closely through extensive use of generic
programming. It is used to perform all experiments on four publicly available
datasets so the results are easily reproducible. This framework provides a
convenient interface to plug in a new method for any sub module and combine it
with existing methods for the other two. It can also serve as a fast and
flexible solution for practical tracking needs due to its highly efficient
implementation.
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information
Authors: Inkyu Sa, Chris Lehnert, Andrew English, Chris McCool, Feras Dayoub, Ben Upcroft, Tristan Perez
Date: 2017-01-30 00:00:00

This paper presents a 3D visual detection method for the challenging task of
detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting
the peduncle cleanly is one of the most difficult stages of the harvesting
process, where the peduncle is the part of the crop that attaches it to the
main stem of the plant. Accurate peduncle detection in 3D space is therefore a
vital step in reliable autonomous harvesting of sweet peppers, as this can lead
to precise cutting while avoiding damage to the surrounding plant. This paper
makes use of both colour and geometry information acquired from an RGB-D sensor
and utilises a supervised-learning approach for the peduncle detection task.
The performance of the proposed method is demonstrated and evaluated using
qualitative and quantitative results (the Area-Under-the-Curve (AUC) of the
detection precision-recall curve). We are able to achieve an AUC of 0.71 for
peduncle detection on field-grown sweet peppers. We release a set of manually
annotated 3D sweet pepper and peduncle images to assist the research community
in performing further research on this topic.
Herding Generalizes Diverse M -Best Solutions
Authors: Ece Ozkan, Gemma Roig, Orcun Goksel, Xavier Boix
Date: 2016-11-14 00:00:00
Tasks: Semantic Segmentation

We show that the algorithm to extract diverse M -solutions from a Conditional
Random Field (called divMbest [1]) takes exactly the form of a Herding
procedure [2], i.e. a deterministic dynamical system that produces a sequence
of hypotheses that respect a set of observed moment constraints. This
generalization enables us to invoke properties of Herding that show that
divMbest enforces implausible constraints which may yield wrong assumptions for
some problem settings. Our experiments in semantic segmentation demonstrate
that seeing divMbest as an instance of Herding leads to better alternatives for
the implausible constraints of divMbest.
Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms
Authors: Jeff Heaton
Date: 2017-01-30 00:00:00

Frequent itemset mining is a popular data mining technique. Apriori, Eclat,
and FP-Growth are among the most common algorithms for frequent itemset mining.
Considerable research has been performed to compare the relative performance
between these three algorithms, by evaluating the scalability of each algorithm
as the dataset size increases. While scalability as data size increases is
important, previous papers have not examined the performance impact of
similarly sized datasets that contain different itemset characteristics. This
paper explores the effects that two dataset characteristics can have on the
performance of these three frequent itemset algorithms. To perform this
empirical analysis, a dataset generator is created to measure the effects of
frequent item density and the maximum transaction size on performance. The
generated datasets contain the same number of rows. This provides some insight
into dataset characteristics that are conducive to each algorithm. The results
of this paper's research demonstrate Eclat and FP-Growth both handle increases
in maximum transaction size and frequent itemset density considerably better
than the Apriori algorithm.
  This paper explores the effects that two dataset characteristics can have on
the performance of these three frequent itemset algorithms. To perform this
empirical analysis, a dataset generator is created to measure the effects of
frequent item density and the maximum transaction size on performance. The
generated datasets contain the same number of rows. This provides some insight
into dataset characteristics that are conducive to each algorithm. The results
of this paper's research demonstrate Eclat and FP-Growth both handle increases
in maximum transaction size and frequent itemset density considerably better
than the Apriori algorithm.
Convolutional Neural Fabrics
Authors: Shreyas Saxena, Jakob Verbeek
Date: 2016-06-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: image-classification, Image Classification, Semantic Segmentation

Despite the success of CNNs, selecting the optimal architecture for a given
task remains an open problem. Instead of aiming to select a single optimal
architecture, we propose a "fabric" that embeds an exponentially large number
of architectures. The fabric consists of a 3D trellis that connects response
maps at different layers, scales, and channels with a sparse homogeneous local
connectivity pattern. The only hyper-parameters of a fabric are the number of
channels and layers. While individual architectures can be recovered as paths,
the fabric can in addition ensemble all embedded architectures together,
sharing their weights where their paths overlap. Parameters can be learned
using standard methods based on back-propagation, at a cost that scales
linearly in the fabric size. We present benchmark results competitive with the
state of the art for image classification on MNIST and CIFAR10, and for
semantic segmentation on the Part Labels dataset.
C3A: A Cognitive Collaborative Control Architecture For an Intelligent Wheelchair
Authors: Rupam Bhattacharyya, Adity Saikia, Shyamanta M. Hazarika
Date: 2017-01-30 00:00:00

Retention of residual skills for persons who partially lose their cognitive
or physical ability is of utmost importance. Research is focused on developing
systems that provide need-based assistance for retention of such residual
skills. This paper describes a novel cognitive collaborative control
architecture C3A, designed to address the challenges of developing need- based
assistance for wheelchair navigation. Organization of C3A is detailed and
results from simulation of the proposed architecture is presented. For
simulation of our proposed architecture, we have used ROS (Robot Operating
System) as a control framework and a 3D robotic simulator called USARSim
(Unified System for Automation and Robot Simulation).
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
Authors: John McCormac, Ankur Handa, Stefan Leutenegger, Andrew J. Davison
Date: 2016-12-15 00:00:00
Tasks: 3D Reconstruction, Camera Pose Estimation, Depth Estimation, Instance Segmentation, object-detection, Object Detection, Optical Flow Estimation, Pose Estimation, Scene Understanding, Semantic Segmentation

We introduce SceneNet RGB-D, expanding the previous work of SceneNet to
enable large scale photorealistic rendering of indoor scene trajectories. It
provides pixel-perfect ground truth for scene understanding problems such as
semantic segmentation, instance segmentation, and object detection, and also
for geometric computer vision problems such as optical flow, depth estimation,
camera pose estimation, and 3D reconstruction. Random sampling permits
virtually unlimited scene configurations, and here we provide a set of 5M
rendered RGB-D images from over 15K trajectories in synthetic layouts with
random but physically simulated object poses. Each layout also has random
lighting, camera trajectories, and textures. The scale of this dataset is well
suited for pre-training data-driven computer vision techniques from scratch
with RGB-D inputs, which previously has been limited by relatively small
labelled datasets in NYUv2 and SUN RGB-D. It also provides a basis for
investigating 3D scene labelling tasks by providing perfect camera poses and
depth data as proxy for a SLAM system. We host the dataset at
http://robotvault.bitbucket.io/scenenet-rgbd.html
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
Authors: Mohammad Aliannejadi, Masoud Kiaeeha, Shahram Khadivi, Saeed Shiry Ghidary
Date: 2017-01-30 00:00:00
Proceeding: ALTA 2014 11
Tasks: Spoken Language Understanding

We experiment graph-based Semi-Supervised Learning (SSL) of Conditional
Random Fields (CRF) for the application of Spoken Language Understanding (SLU)
on unaligned data. The aligned labels for examples are obtained using IBM
Model. We adapt a baseline semi-supervised CRF by defining new feature set and
altering the label propagation algorithm. Our results demonstrate that our
proposed approach significantly improves the performance of the supervised
model by utilizing the knowledge gained from the graph.
Self-Adaptation of Activity Recognition Systems to New Sensors
Authors: David Bannach, Martin Jänicke, Vitor F. Rey, Sven Tomforde, Bernhard Sick, Paul Lukowicz
Date: 2017-01-30 00:00:00
Tasks: Activity Recognition, Clustering

Traditional activity recognition systems work on the basis of training,
taking a fixed set of sensors into account. In this article, we focus on the
question how pattern recognition can leverage new information sources without
any, or with minimal user input. Thus, we present an approach for opportunistic
activity recognition, where ubiquitous sensors lead to dynamically changing
input spaces. Our method is a variation of well-established principles of
machine learning, relying on unsupervised clustering to discover structure in
data and inferring cluster labels from a small number of labeled dates in a
semi-supervised manner. Elaborating the challenges, evaluations of over 3000
sensor combinations from three multi-user experiments are presented in detail
and show the potential benefit of our approach.
Binary adaptive embeddings from order statistics of random projections
Authors: Diego Valsesia, Enrico Magli
Date: 2017-01-30 00:00:00
Tasks: General Classification

We use some of the largest order statistics of the random projections of a
reference signal to construct a binary embedding that is adapted to signals
correlated with such signal. The embedding is characterized from the analytical
standpoint and shown to provide improved performance on tasks such as
classification in a reduced-dimensionality space.
Predicting SMT Solver Performance for Software Verification
Authors: Andrew Healy, Rosemary Monahan, James F. Power
Date: 2017-01-30 00:00:00
Tasks: BIG-bench Machine Learning

The Why3 IDE and verification system facilitates the use of a wide range of
Satisfiability Modulo Theories (SMT) solvers through a driver-based
architecture. We present Where4: a portfolio-based approach to discharge Why3
proof obligations. We use data analysis and machine learning techniques on
static metrics derived from program source code. Our approach benefits software
engineers by providing a single utility to delegate proof obligations to the
solvers most likely to return a useful result. It does this in a time-efficient
way using existing Why3 and solver installations - without requiring low-level
knowledge about SMT solver operation from the user.
Fast and robust pushbroom hyperspectral imaging via DMD-based scanning
Authors: Reza Arablouei, Ethan Goan, Stephen Gensemer, Branislav Kusy
Date: 2016-08-01 00:00:00

We describe a new pushbroom hyperspectral imaging device that has no macro
moving part. The main components of the proposed hyperspectral imager are a
digital micromirror device (DMD), a CMOS image sensor with no filter as the
spectral sensor, a CMOS color (RGB) image sensor as the auxiliary image sensor,
and a diffraction grating. Using the image sensor pair, the device can
simultaneously capture hyperspectral data as well as RGB images of the scene.
The RGB images captured by the auxiliary image sensor can facilitate geometric
co-registration of the hyperspectral image slices captured by the spectral
sensor. In addition, the information discernible from the RGB images can lead
to capturing the spectral data of only the regions of interest within the
scene. The proposed hyperspectral imaging architecture is cost-effective, fast,
and robust. It also enables a trade-off between resolution and speed. We have
built an initial prototype based on the proposed design. The prototype can
capture a hyperspectral image datacube with a spatial resolution of 192x192
pixels and a spectral resolution of 500 bands in less than thirty seconds.
StuffNet: Using 'Stuff' to Improve Object Detection
Authors: Samarth Brahmbhatt, Henrik I. Christensen, James Hays
Date: 2016-10-19 00:00:00
Tasks: Object, object-detection, Object Detection, Region Proposal, Segmentation

We propose a Convolutional Neural Network (CNN) based algorithm - StuffNet -
for object detection. In addition to the standard convolutional features
trained for region proposal and object detection [31], StuffNet uses
convolutional features trained for segmentation of objects and 'stuff'
(amorphous categories such as ground and water). Through experiments on Pascal
VOC 2010, we show the importance of features learnt from stuff segmentation for
improving object detection performance. StuffNet improves performance from
18.8% mAP to 23.9% mAP for small objects. We also devise a method to train
StuffNet on datasets that do not have stuff segmentation labels. Through
experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of
this method and show that StuffNet also significantly improves object detection
performance on such datasets.
The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling
Authors: Chitta Ranjan, Kamran Paynabar, Jonathan E. Helm, Julian Pan
Date: 2015-05-28 00:00:00
Tasks: Clustering, Management, Scheduling

The ability to accurately forecast and control inpatient census, and thereby
workloads, is a critical and longstanding problem in hospital management.
Majority of current literature focuses on optimal scheduling of inpatients, but
largely ignores the process of accurate estimation of the trajectory of
patients throughout the treatment and recovery process. The result is that
current scheduling models are optimizing based on inaccurate input data. We
developed a Clustering and Scheduling Integrated (CSI) approach to capture
patient flows through a network of hospital services. CSI functions by
clustering patients into groups based on similarity of trajectory using a novel
Semi-Markov model (SMM)-based clustering scheme proposed in this paper, as
opposed to clustering by admit type or condition as in previous literature. The
methodology is validated by simulation and then applied to real patient data
from a partner hospital where we see it outperforms current methods. Further,
we demonstrate that extant optimization methods achieve significantly better
results on key hospital performance measures under CSI, compared with
traditional estimation approaches, increasing elective admissions by 97% and
utilization by 22% compared to 30% and 8% using traditional estimation
techniques. From a theoretical standpoint, the SMM-clustering is a novel
approach applicable to any temporal-spatial stochastic data that is prevalent
in many industries and application areas.
Adam: A Method for Stochastic Optimization
Authors: Diederik P. Kingma, Jimmy Ba
Date: 2014-12-22 00:00:00
Tasks: Stochastic Optimization

We introduce Adam, an algorithm for first-order gradient-based optimization
of stochastic objective functions, based on adaptive estimates of lower-order
moments. The method is straightforward to implement, is computationally
efficient, has little memory requirements, is invariant to diagonal rescaling
of the gradients, and is well suited for problems that are large in terms of
data and/or parameters. The method is also appropriate for non-stationary
objectives and problems with very noisy and/or sparse gradients. The
hyper-parameters have intuitive interpretations and typically require little
tuning. Some connections to related algorithms, on which Adam was inspired, are
discussed. We also analyze the theoretical convergence properties of the
algorithm and provide a regret bound on the convergence rate that is comparable
to the best known results under the online convex optimization framework.
Empirical results demonstrate that Adam works well in practice and compares
favorably to other stochastic optimization methods. Finally, we discuss AdaMax,
a variant of Adam based on the infinity norm.
SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility
Authors: Junaed Sattar, Jiawei Mo
Date: 2017-01-29 00:00:00
Tasks: Autonomous Driving, Lane Detection, Visual Tracking

We present an approach towards robust lane tracking for assisted and
autonomous driving, particularly under poor visibility. Autonomous detection of
lane markers improves road safety, and purely visual tracking is desirable for
widespread vehicle compatibility and reducing sensor intrusion, cost, and
energy consumption. However, visual approaches are often ineffective because of
a number of factors, including but not limited to occlusion, poor weather
conditions, and paint wear-off. Our method, named SafeDrive, attempts to
improve visual lane detection approaches in drastically degraded visual
conditions without relying on additional active sensors. In scenarios where
visual lane detection algorithms are unable to detect lane markers, the
proposed approach uses location information of the vehicle to locate and access
alternate imagery of the road and attempts detection on this secondary image.
Subsequently, by using a combination of feature-based and pixel-based
alignment, an estimated location of the lane marker is found in the current
scene. We demonstrate the effectiveness of our system on actual driving data
from locations in the United States with Google Street View as the source of
alternate imagery.
Matrix Completion has No Spurious Local Minimum
Authors: Rong Ge, Jason D. Lee, Tengyu Ma
Date: 2016-05-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Collaborative Filtering, Matrix Completion, Recommendation Systems

Matrix completion is a basic machine learning problem that has wide
applications, especially in collaborative filtering and recommender systems.
Simple non-convex optimization algorithms are popular and effective in
practice. Despite recent progress in proving various non-convex algorithms
converge from a good initial point, it remains unclear why random or arbitrary
initialization suffices in practice. We prove that the commonly used non-convex
objective function for \textit{positive semidefinite} matrix completion has no
spurious local minima --- all local minima must also be global. Therefore, many
popular optimization algorithms such as (stochastic) gradient descent can
provably solve positive semidefinite matrix completion with \textit{arbitrary}
initialization in polynomial time. The result can be generalized to the setting
when the observed entries contain noise. We believe that our main proof
strategy can be useful for understanding geometric properties of other
statistical problems involving partial or noisy observations.
An Extremal Optimization approach to parallel resonance constrained capacitor placement problem
Authors: André R. Goncalves, Celso Cavelucci, Christiano Lyra Filho, Fernando J. Von Zuben
Date: 2017-01-29 00:00:00

Installation of capacitors in distribution networks is one of the most used
procedure to compensate reactive power generated by loads and, consequently, to
reduce technical losses. So, the problem consists in identifying the optimal
placement and sizing of capacitors. This problem is known in the literature as
optimal capacitor placement problem. Neverthless, depending on the location and
size of the capacitor, it may become a harmonic source, allowing capacitor to
enter into resonance with the distribution network, causing several undesired
side effects. In this work we propose a parsimonious method to deal with the
capacitor placement problem that incorporates resonance constraints, ensuring
that every allocated capacitor will not act as a harmonic source. This proposed
algorithm is based upon a physical inspired metaheuristic known as Extremal
Optimization. The results achieved showed that this proposal has reached
significant gains when compared with other proposals that attempt repair, in a
post-optimization stage, already obtained solutions which violate resonance
constraints.
The HASYv2 dataset
Authors: Martin Thoma
Date: 2017-01-29 00:00:00
Tasks: General Classification

This paper describes the HASYv2 dataset. HASY is a publicly available, free
of charge dataset of single symbols similar to MNIST. It contains 168233
instances of 369 classes. HASY contains two challenges: A classification
challenge with 10 pre-defined folds for 10-fold cross-validation and a
verification challenge.
MSCM-LiFe: Multi-scale cross modal linear feature for horizon detection in maritime images
Authors: D. K. Prasad, D. Rajan, C. K. Prasath, L. Rachmawati, E. Rajabaly, C. Quek
Date: 2017-01-29 00:00:00

This paper proposes a new method for horizon detection called the multi-scale
cross modal linear feature. This method integrates three different concepts
related to the presence of horizon in maritime images to increase the accuracy
of horizon detection. Specifically it uses the persistence of horizon in
multi-scale median filtering, and its detection as a linear feature commonly
detected by two different methods, namely the Hough transform of edgemap and
the intensity gradient. We demonstrate the performance of the method over 13
videos comprising of more than 3000 frames and show that the proposed method
detects horizon with small error in most of the cases, outperforming three
state-of-the-art methods.
Optimizing Expectation with Guarantees in POMDPs (Technical Report)
Authors: Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez, Jean-François Raskin, Đorđe Žikelić
Date: 2016-11-26 00:00:00

A standard objective in partially-observable Markov decision processes
(POMDPs) is to find a policy that maximizes the expected discounted-sum payoff.
However, such policies may still permit unlikely but highly undesirable
outcomes, which is problematic especially in safety-critical applications.
Recently, there has been a surge of interest in POMDPs where the goal is to
maximize the probability to ensure that the payoff is at least a given
threshold, but these approaches do not consider any optimization beyond
satisfying this threshold constraint. In this work we go beyond both the
"expectation" and "threshold" approaches and consider a "guaranteed payoff
optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and
the objective is to find a policy $\sigma$ such that a) each possible outcome
of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected
discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all
policies satisfying a). We present a practical approach to tackle the GPO
problem and evaluate it on standard POMDP benchmarks.