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28,202 | Zero-Shot Learning with Generative Latent Prototype Model | cs.CV | Zero-shot learning, which studies the problem of object classification for
categories for which we have no training examples, is gaining increasing
attention from community. Most existing ZSL methods exploit deterministic
transfer learning via an in-between semantic embedding space. In this paper, we
try to attack this... | computer science |
28,203 | PL-SLAM: a Stereo SLAM System through the Combination of Points and Line
Segments | cs.CV | Traditional approaches to stereo visual SLAM rely on point features to
estimate the camera trajectory and build a map of the environment. In
low-textured environments, though, it is often difficult to find a sufficient
number of reliable point features and, as a consequence, the performance of
such algorithms degrades.... | computer science |
28,204 | Fully Automatic Segmentation and Objective Assessment of Atrial Scars
for Longstanding Persistent Atrial Fibrillation Patients Using Late
Gadolinium-Enhanced MRI | cs.CV | Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and
is correlated with increased morbidity and mortality. It is associated with
atrial fibrosis, which may be assessed non-invasively using late
gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is
visualised as a region ... | computer science |
28,205 | Residual Expansion Algorithm: Fast and Effective Optimization for
Nonconvex Least Squares Problems | cs.CV | We propose the residual expansion (RE) algorithm: a global (or near-global)
optimization method for nonconvex least squares problems. Unlike most existing
nonconvex optimization techniques, the RE algorithm is not based on either
stochastic or multi-point searches; therefore, it can achieve fast global
optimization. Mo... | computer science |
28,206 | Enhancement of SSD by concatenating feature maps for object detection | cs.CV | We propose an object detection method that improves the accuracy of the
conventional SSD (Single Shot Multibox Detector), which is one of the top
object detection algorithms in both aspects of accuracy and speed. The
performance of a deep network is known to be improved as the number of feature
maps increases. However,... | computer science |
28,207 | Extracting 3D Vascular Structures from Microscopy Images using
Convolutional Recurrent Networks | cs.CV | Vasculature is known to be of key biological significance, especially in the
study of cancer. As such, considerable effort has been focused on the automated
measurement and analysis of vasculature in medical and pre-clinical images. In
tumors in particular, the vascular networks may be extremely irregular and the
appea... | computer science |
28,208 | Learning a Robust Society of Tracking Parts | cs.CV | Object tracking is an essential task in computer vision that has been studied
since the early days of the field. Being able to follow objects that undergo
different transformations in the video sequence, including changes in scale,
illumination, shape and occlusions, makes the problem extremely difficult. One
of the re... | computer science |
28,209 | End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth
data | cs.CV | Despite recent advances in 3D pose estimation of human hands, especially
thanks to the advent of CNNs and depth cameras, this task is still far from
being solved. This is mainly due to the highly non-linear dynamics of fingers,
which makes hand model training a challenging task. In this paper, we exploit a
novel hierar... | computer science |
28,210 | Direct Estimation of Regional Wall Thicknesses via Residual Recurrent
Neural Network | cs.CV | Accurate estimation of regional wall thicknesses (RWT) of left ventricular
(LV) myocardium from cardiac MR sequences is of significant importance for
identification and diagnosis of cardiac disease. Existing RWT estimation still
relies on segmentation of LV myocardium, which requires strong prior
information and user i... | computer science |
28,211 | CASENet: Deep Category-Aware Semantic Edge Detection | cs.CV | Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge d... | computer science |
28,212 | Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks | cs.CV | We present a radial basis function solver for convolutional neural networks
that can be directly applied to both distance metric learning and
classification problems. Our method treats all training features from a deep
neural network as radial basis function centres and computes loss by summing
the influence of a featu... | computer science |
28,213 | Abnormality Detection and Localization in Chest X-Rays using Deep
Convolutional Neural Networks | cs.CV | Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart
and lung area. Automatically detecting these abnormalities with high accuracy
could greatly enhance real world diagnosis processes. Lack of standard publicly
available dataset and benchmark studies, however, makes it difficult to compare
vari... | computer science |
28,214 | Probabilistic Global Scale Estimation for MonoSLAM Based on Generic
Object Detection | cs.CV | This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object ... | computer science |
28,215 | Person Depth ReID: Robust Person Re-identification with Commodity Depth
Sensors | cs.CV | This work targets person re-identification (ReID) from depth sensors such as
Kinect. Since depth is invariant to illumination and less sensitive than color
to day-by-day appearance changes, a natural question is whether depth is an
effective modality for Person ReID, especially in scenarios where individuals
wear diffe... | computer science |
28,216 | Cross-modal Subspace Learning for Fine-grained Sketch-based Image
Retrieval | cs.CV | Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient... | computer science |
28,217 | Care about you: towards large-scale human-centric visual relationship
detection | cs.CV | Visual relationship detection aims to capture interactions between pairs of
objects in images. Relationships between objects and humans represent a
particularly important subset of this problem, with implications for challenges
such as understanding human behaviour, and identifying affordances, amongst
others. In addre... | computer science |
28,218 | Continuous Video to Simple Signals for Swimming Stroke Detection with
Convolutional Neural Networks | cs.CV | In many sports, it is useful to analyse video of an athlete in competition
for training purposes. In swimming, stroke rate is a common metric used by
coaches; requiring a laborious labelling of each individual stroke. We show
that using a Convolutional Neural Network (CNN) we can automatically detect
discrete events in... | computer science |
28,219 | Multi-channel Weighted Nuclear Norm Minimization for Real Color Image
Denoising | cs.CV | Most of the existing denoising algorithms are developed for grayscale images,
while it is not a trivial work to extend them for color image denoising because
the noise statistics in R, G, B channels can be very different for real noisy
images. In this paper, we propose a multi-channel (MC) optimization model for
real c... | computer science |
28,220 | Dilated Residual Networks | cs.CV | Convolutional networks for image classification progressively reduce
resolution until the image is represented by tiny feature maps in which the
spatial structure of the scene is no longer discernible. Such loss of spatial
acuity can limit image classification accuracy and complicate the transfer of
the model to downst... | computer science |
28,221 | L1-norm Error Function Robustness and Outlier Regularization | cs.CV | In many real-world applications, data come with corruptions, large errors or
outliers. One popular approach is to use L1-norm function. However, the
robustness of L1-norm function is not well understood so far. In this paper, we
present a new outlier regularization framework to understand and analyze the
robustness of ... | computer science |
28,222 | Robust Online Matrix Factorization for Dynamic Background Subtraction | cs.CV | We propose an effective online background subtraction method, which can be
robustly applied to practical videos that have variations in both foreground
and background. Different from previous methods which often model the
foreground as Gaussian or Laplacian distributions, we model the foreground for
each frame with a s... | computer science |
28,223 | Data Driven Coded Aperture Design for Depth Recovery | cs.CV | Inserting a patterned occluder at the aperture of a camera lens has been
shown to improve the recovery of depth map and all-focus image compared to a
fully open aperture. However, design of the aperture pattern plays a very
critical role. Previous approaches for designing aperture codes make simple
assumptions on image... | computer science |
28,224 | Ensemble of Part Detectors for Simultaneous Classification and
Localization | cs.CV | Part-based representation has been proven to be effective for a variety of
visual applications. However, automatic discovery of discriminative parts
without object/part-level annotations is challenging. This paper proposes a
discriminative mid-level representation paradigm based on the responses of a
collection of part... | computer science |
28,225 | Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks -
Counting, Detection, and Tracking | cs.CV | For crowded scenes, the accuracy of object-based computer vision methods
declines when the images are low-resolution and objects have severe occlusions.
Taking counting methods for example, almost all the recent state-of-the-art
counting methods bypass explicit detection and adopt regression-based methods
to directly c... | computer science |
28,226 | Pose-Aware Person Recognition | cs.CV | Person recognition methods that use multiple body regions have shown
significant improvements over traditional face-based recognition. One of the
primary challenges in full-body person recognition is the extreme variation in
pose and view point. In this work, (i) we present an approach that tackles pose
variations util... | computer science |
28,227 | Feature Incay for Representation Regularization | cs.CV | Softmax loss is widely used in deep neural networks for multi-class
classification, where each class is represented by a weight vector, a sample is
represented as a feature vector, and the feature vector has the largest
projection on the weight vector of the correct category when the model
correctly classifies a sample... | computer science |
28,228 | Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based
Shape Priors | cs.CV | Shape priors have been widely utilized in medical image segmentation to
improve segmentation accuracy and robustness. A major way to encode such a
prior shape model is to use a mesh representation, which is prone to causing
self-intersection or mesh folding. Those problems require complex and expensive
algorithms to mi... | computer science |
28,229 | Learning to Generate Chairs with Generative Adversarial Nets | cs.CV | Generative adversarial networks (GANs) has gained tremendous popularity
lately due to an ability to reinforce quality of its predictive model with
generated objects and the quality of the generative model with and supervised
feedback. GANs allow to synthesize images with a high degree of realism.
However, the learning ... | computer science |
28,230 | Discriminatively Learned Hierarchical Rank Pooling Networks | cs.CV | In this work, we present novel temporal encoding methods for action and
activity classification by extending the unsupervised rank pooling temporal
encoding method in two ways. First, we present "discriminative rank pooling" in
which the shared weights of our video representation and the parameters of the
action classi... | computer science |
28,231 | Unsupervised Person Re-identification: Clustering and Fine-tuning | cs.CV | The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transf... | computer science |
28,232 | Robust Tracking Using Region Proposal Networks | cs.CV | Recent advances in visual tracking showed that deep Convolutional Neural
Networks (CNN) trained for image classification can be strong feature
extractors for discriminative trackers. However, due to the drastic difference
between image classification and tracking, extra treatments such as model
ensemble and feature eng... | computer science |
28,233 | RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via
Crowdsource Data | cs.CV | Remote sensing image classification is a fundamental task in remote sensing
image processing. Remote sensing field still lacks of such a large-scale
benchmark compared to ImageNet, Place2. We propose a remote sensing image
classification benchmark (RSI-CB) based on crowd-source data which is massive,
scalable, and dive... | computer science |
28,234 | Parcellation of Visual Cortex on high-resolution histological Brain
Sections using Convolutional Neural Networks | cs.CV | Microscopic analysis of histological sections is considered the "gold
standard" to verify structural parcellations in the human brain. Its high
resolution allows the study of laminar and columnar patterns of cell
distributions, which build an important basis for the simulation of cortical
areas and networks. However, s... | computer science |
28,235 | Saliency Revisited: Analysis of Mouse Movements versus Fixations | cs.CV | This paper revisits visual saliency prediction by evaluating the recent
advancements in this field such as crowd-sourced mouse tracking-based databases
and contextual annotations. We pursue a critical and quantitative approach
towards some of the new challenges including the quality of mouse tracking
versus eye trackin... | computer science |
28,236 | Interpreting and Extending The Guided Filter Via Cyclic Coordinate
Descent | cs.CV | In this paper, we will disclose that the Guided Filter (GF) can be
interpreted as the Cyclic Coordinate Descent (CCD) solver of a Least Square
(LS) objective function. This discovery implies a possible way to extend GF
because we can alter the objective function of GF and define new filters as the
first pass iteration ... | computer science |
28,237 | End-to-end Active Object Tracking via Reinforcement Learning | cs.CV | In this paper, we propose an active object tracking approach, which provides
a tracking solution simultaneously addressing tracking and camera control.
Crucially, these two tasks are tackled in an end-to-end manner via
reinforcement learning. Specifically, a ConvNet-LSTM function approximator is
adopted, which takes as... | computer science |
28,238 | Nighttime sky/cloud image segmentation | cs.CV | Imaging the atmosphere using ground-based sky cameras is a popular approach
to study various atmospheric phenomena. However, it usually focuses on the
daytime. Nighttime sky/cloud images are darker and noisier, and thus harder to
analyze. An accurate segmentation of sky/cloud images is already challenging
because of th... | computer science |
28,239 | Discovering Visual Concept Structure with Sparse and Incomplete Tags | cs.CV | Discovering automatically the semantic structure of tagged visual data (e.g.
web videos and images) is important for visual data analysis and
interpretation, enabling the machine intelligence for effectively processing
the fast-growing amount of multi-media data. However, this is non-trivial due
to the need for jointly... | computer science |
28,240 | ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting,
Violent Behaviour Detection and Crowd Density Level Classification | cs.CV | In this paper we propose ResnetCrowd, a deep residual architecture for
simultaneous crowd counting, violent behaviour detection and crowd density
level classification. To train and evaluate the proposed multi-objective
technique, a new 100 image dataset referred to as Multi Task Crowd is
constructed. This new dataset i... | computer science |
28,241 | Multi-View Task-Driven Recognition in Visual Sensor Networks | cs.CV | Nowadays, distributed smart cameras are deployed for a wide set of tasks in
several application scenarios, ranging from object recognition, image
retrieval, and forensic applications. Due to limited bandwidth in distributed
systems, efficient coding of local visual features has in fact been an active
topic of research.... | computer science |
28,242 | Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy | cs.CV | This paper presents a novel hierarchical approach for the simultaneous
tracking of multiple targets in a video. We use a network flow approach to link
detections in low-level and tracklets in high-level. At each step of the
hierarchy, the confidence of candidates is measured by using a new scoring
system, ConfRank, tha... | computer science |
28,243 | Deep manifold-to-manifold transforming network for action recognition | cs.CV | Symmetric positive definite (SPD) matrices (e.g., covariances, graph
Laplacians, etc.) are widely used to model the relationship of spatial or
temporal domain. Nevertheless, SPD matrices are theoretically embedded on
Riemannian manifolds. In this paper, we propose an end-to-end deep
manifold-to-manifold transforming ne... | computer science |
28,244 | A Kernel Redundancy Removing Policy for Convolutional Neural Network | cs.CV | Deep Convolutional Neural Networks (CNN) have won a significant place in the
computer vision recently, which repeatedly convolving an image to extract the
knowledge behind it. However, with the depth of convolutional layers getting
deeper and deeper in recent years, the computational complexity also increases
significa... | computer science |
28,245 | Reflection Invariant and Symmetry Detection | cs.CV | Symmetry detection and discrimination are of fundamental meaning in science,
technology, and engineering. This paper introduces reflection invariants and
defines the directional moment to detect symmetry for shape analysis and object
recognition. And it demonstrates that detection of reflection symmetry can be
done in ... | computer science |
28,246 | PCM-TV-TFV: A Novel Two Stage Framework for Image Reconstruction from
Fourier Data | cs.CV | We propose in this paper a novel two-stage Projection Correction Modeling
(PCM) framework for image reconstruction from (non-uniform) Fourier
measurements. PCM consists of a projection stage (P-stage) motivated by the
multi-scale Galerkin method and a correction stage (C-stage) with an edge
guided regularity fusing tog... | computer science |
28,247 | Generic Tubelet Proposals for Action Localization | cs.CV | We develop a novel framework for action localization in videos. We propose
the Tube Proposal Network (TPN), which can generate generic, class-independent,
video-level tubelet proposals in videos. The generated tubelet proposals can be
utilized in various video analysis tasks, including recognizing and localizing
action... | computer science |
28,248 | Working hard to know your neighbor's margins: Local descriptor learning
loss | cs.CV | We introduce a novel loss for learning local feature descriptors which is
inspired by the Lowe's matching criterion for SIFT. We show that the proposed
loss that maximizes the distance between the closest positive and closest
negative patch in the batch is better than complex regularization methods; it
works well for b... | computer science |
28,249 | Weakly supervised 3D Reconstruction with Adversarial Constraint | cs.CV | Supervised 3D reconstruction has witnessed a significant progress through the
use of deep neural networks. However, this increase in performance requires
large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D
supervision as an alternative for expensive 3D CAD annotation. Specifically, we
use fo... | computer science |
28,250 | Naturally Combined Shape-Color Moment Invariants under Affine
Transformations | cs.CV | We proposed a kind of naturally combined shape-color affine moment invariants
(SCAMI), which consider both shape and color affine transformations
simultaneously in one single system. In the real scene, color and shape
deformations always exist in images simultaneously. Simple shape invariants or
color invariants can no... | computer science |
28,251 | Bridge Simulation and Metric Estimation on Landmark Manifolds | cs.CV | We present an inference algorithm and connected Monte Carlo based estimation
procedures for metric estimation from landmark configurations distributed
according to the transition distribution of a Riemannian Brownian motion
arising from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) metric.
The distribution... | computer science |
28,252 | Class Specific Feature Selection for Interval Valued Data Through
Interval K-Means Clustering | cs.CV | In this paper, a novel feature selection approach for supervised interval
valued features is proposed. The proposed approach takes care of selecting the
class specific features through interval K-Means clustering. The kernel of
K-Means clustering algorithm is modified to adapt interval valued data. During
training, a s... | computer science |
28,253 | Deep Supervised Discrete Hashing | cs.CV | With the rapid growth of image and video data on the web, hashing has been
extensively studied for image or video search in recent years. Benefit from
recent advances in deep learning, deep hashing methods have achieved promising
results for image retrieval. However, there are some limitations of previous
deep hashing ... | computer science |
28,254 | Neuron Segmentation Using Deep Complete Bipartite Networks | cs.CV | In this paper, we consider the problem of automatically segmenting neuronal
cells in dual-color confocal microscopy images. This problem is a key task in
various quantitative analysis applications in neuroscience, such as tracing
cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using
fully conv... | computer science |
28,255 | EvaluationNet: Can Human Skill be Evaluated by Deep Networks? | cs.CV | With the recent substantial growth of media such as YouTube, a considerable
number of instructional videos covering a wide variety of tasks are available
online. Therefore, online instructional videos have become a rich resource for
humans to learn everyday skills. In order to improve the effectiveness of the
learning ... | computer science |
28,256 | Representation Learning by Rotating Your Faces | cs.CV | The large pose discrepancy between two face images is one of the fundamental
challenges in automatic face recognition. Conventional approaches to
pose-invariant face recognition either perform face frontalization on, or learn
a pose-invariant representation from, a non-frontal face image. We argue that
it is more desir... | computer science |
28,257 | Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and
Image-to-Image Translation from Unpaired Supervision | cs.CV | Researchers have developed excellent feed-forward models that learn to map
images to desired outputs, such as to the images' latent factors, or to other
images, using supervised learning. Learning such mappings from unlabelled data,
or improving upon supervised models by exploiting unlabelled data, remains
elusive. We ... | computer science |
28,258 | Long-term Correlation Tracking using Multi-layer Hybrid Features in
Sparse and Dense Environments | cs.CV | Tracking a target of interest in both sparse and crowded environments is a
challenging problem, not yet successfully addressed in the literature. In this
paper, we propose a new long-term visual tracking algorithm, learning
discriminative correlation filters and using an online classifier, to track a
target of interest... | computer science |
28,259 | U-Phylogeny: Undirected Provenance Graph Construction in the Wild | cs.CV | Deriving relationships between images and tracing back their history of
modifications are at the core of Multimedia Phylogeny solutions, which aim to
combat misinformation through doctored visual media. Nonetheless, most recent
image phylogeny solutions cannot properly address cases of forged composite
images with mult... | computer science |
28,260 | Blood capillaries and vessels segmentation in optical coherence
tomography angiogram using fuzzy C-means and Curvelet transform | cs.CV | This paper has been removed from arXiv as the submitter did not have
ownership of the data presented in this work. | computer science |
28,261 | Superhuman Accuracy on the SNEMI3D Connectomics Challenge | cs.CV | For the past decade, convolutional networks have been used for 3D
reconstruction of neurons from electron microscopic (EM) brain images. Recent
years have seen great improvements in accuracy, as evidenced by submissions to
the SNEMI3D benchmark challenge. Here we report the first submission to surpass
the estimate of h... | computer science |
28,262 | Faster Spatially Regularized Correlation Filters for Visual Tracking | cs.CV | Discriminatively learned correlation filters (DCF) have been widely used in
online visual tracking filed due to its simplicity and efficiency. These
methods utilize a periodic assumption of the training samples to construct a
circulant data matrix, which implicitly increases the training samples and
reduces both storag... | computer science |
28,263 | Shape and Positional Geometry of Multi-Object Configurations | cs.CV | In previous work, we introduced a method for modeling a configuration of
objects in 2D and 3D images using a mathematical "medial/skeletal linking
structure." In this paper, we show how these structures allow us to capture
positional properties of a multi-object configuration in addition to the shape
properties of the ... | computer science |
28,264 | Depth Structure Preserving Scene Image Generation | cs.CV | Key to automatically generate natural scene images is to properly arrange
among various spatial elements, especially in the depth direction. To this end,
we introduce a novel depth structure preserving scene image generation network
(DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the
purpose ... | computer science |
28,265 | An Effective Approach for Point Clouds Registration Based on the Hard
and Soft Assignments | cs.CV | For the registration of partially overlapping point clouds, this paper
proposes an effective approach based on both the hard and soft assignments.
Given two initially posed clouds, it firstly establishes the forward
correspondence for each point in the data shape and calculates the value of
binary variable, which can i... | computer science |
28,266 | TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level
Estimation | cs.CV | We address unsupervised optical flow estimation for ego-centric motion. We
argue that optical flow can be cast as a geometrical warping between two
successive video frames and devise a deep architecture to estimate such
transformation in two stages. First, a dense pixel-level flow is computed with
a geometric prior imp... | computer science |
28,267 | Deep Mutual Learning | cs.CV | Model distillation is an effective and widely used technique to transfer
knowledge from a teacher to a student network. The typical application is to
transfer from a powerful large network or ensemble to a small network, that is
better suited to low-memory or fast execution requirements. In this paper, we
present a dee... | computer science |
28,268 | DiracNets: Training Very Deep Neural Networks Without Skip-Connections | cs.CV | Deep neural networks with skip-connections, such as ResNet, show excellent
performance in various image classification benchmarks. It is though observed
that the initial motivation behind them - training deeper networks - does not
actually hold true, and the benefits come from increased capacity, rather than
from depth... | computer science |
28,269 | Line Profile Based Segmentation Algorithm for Touching Corn Kernels | cs.CV | Image segmentation of touching objects plays a key role in providing accurate
classification for computer vision technologies. A new line profile based
imaging segmentation algorithm has been developed to provide a robust and
accurate segmentation of a group of touching corns. The performance of the line
profile based ... | computer science |
28,270 | Fader Networks: Manipulating Images by Sliding Attributes | cs.CV | This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying th... | computer science |
28,271 | A Vision System for Multi-View Face Recognition | cs.CV | Multimodal biometric identification has been grown a great attention in the
most interests in the security fields. In the real world there exist modern
system devices that are able to detect, recognize, and classify the human
identities with reliable and fast recognition rates. Unfortunately most of
these systems rely ... | computer science |
28,272 | Data Augmentation of Wearable Sensor Data for Parkinson's Disease
Monitoring using Convolutional Neural Networks | cs.CV | While convolutional neural networks (CNNs) have been successfully applied to
many challenging classification applications, they typically require large
datasets for training. When the availability of labeled data is limited, data
augmentation is a critical preprocessing step for CNNs. However, data
augmentation for wea... | computer science |
28,273 | Integrated Deep and Shallow Networks for Salient Object Detection | cs.CV | Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifical... | computer science |
28,274 | SAR Image Despeckling Using a Convolutional | cs.CV | Synthetic Aperture Radar (SAR) images are often contaminated by a
multiplicative noise known as speckle. Speckle makes the processing and
interpretation of SAR images difficult. We propose a deep learning-based
approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for
automatically removing speckle ... | computer science |
28,275 | Rank Persistence: Assessing the Temporal Performance of Real-World
Person Re-Identification | cs.CV | Designing useful person re-identification systems for real-world applications
requires attention to operational aspects not typically considered in academic
research. Here, we focus on the temporal aspect of re-identification; that is,
instead of finding a match to a probe person of interest in a fixed candidate
galler... | computer science |
28,276 | r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial
Patches | cs.CV | We start by asking an interesting yet challenging question, "If an eyewitness
can only recall the eye features of the suspect, such that the forensic artist
can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig.
1), can advanced computer vision techniques help generate the whole face
image?" A m... | computer science |
28,277 | Image Restoration from Patch-based Compressed Sensing Measurement | cs.CV | A series of methods have been proposed to reconstruct an image from
compressively sensed random measurement, but most of them have high time
complexity and are inappropriate for patch-based compressed sensing capture,
because of their serious blocky artifacts in the restoration results. In this
paper, we present a non-... | computer science |
28,278 | Facies classification from well logs using an inception convolutional
network | cs.CV | The idea to use automated algorithms to determine geological facies from well
logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent
and dramatic increase in research in the field of machine learning makes it a
good time to revisit the topic. Following an exercise proposed by Dubois et al.
(2007) ... | computer science |
28,279 | Dual-reference Face Retrieval | cs.CV | Face retrieval has received much attention over the past few decades, and
many efforts have been made in retrieving face images against pose,
illumination, and expression variations. However, the conventional works fail
to meet the requirements of a potential and novel task --- retrieving a
person's face image at a spe... | computer science |
28,280 | Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type
Visual Tracking | cs.CV | We propose a new framework that extends the standard Probability Hypothesis
Density (PHD) filter for multiple targets having $N$ different types where
$N\geq2$ based on Random Finite Set (RFS) theory, taking into account not only
background false positives (clutter), but also confusions among detections of
different ta... | computer science |
28,281 | Temporal Action Labeling using Action Sets | cs.CV | Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and tem... | computer science |
28,282 | A watershed-based algorithm to segment and classify cells in
fluorescence microscopy images | cs.CV | Imaging assays of cellular function, especially those using fluorescent
stains, are ubiquitous in the biological and medical sciences. Despite advances
in computer vision, such images are often analyzed using only manual or
rudimentary automated processes. Watershed-based segmentation is an effective
technique for iden... | computer science |
28,283 | One-Sided Unsupervised Domain Mapping | cs.CV | In unsupervised domain mapping, the learner is given two unmatched datasets
$A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample
in $A$ to the analog sample in $B$. Recent approaches have shown that when
learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$,
convincing mappings... | computer science |
28,284 | Multi-Class Model Fitting by Energy Minimization and Mode-Seeking | cs.CV | We propose a general formulation, called Multi-X, for multi-class
multi-instance model fitting - the problem of interpreting the input data as a
mixture of noisy observations originating from multiple instances of multiple
classes. We extend the commonly used alpha-expansion-based technique with a new
move in the label... | computer science |
28,285 | Neural Network-Based Automatic Liver Tumor Segmentation With Random
Forest-Based Candidate Filtering | cs.CV | We present a fully automatic method employing convolutional neural networks
based on the 2D U-net architecture and random forest classifier to solve the
automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor
Segmentation Challenge (LiTS). In order to constrain the ROI in which the
tumors could be loca... | computer science |
28,286 | Learning Person Trajectory Representations for Team Activity Analysis | cs.CV | Activity analysis in which multiple people interact across a large space is
challenging due to the interplay of individual actions and collective group
dynamics. We propose an end-to-end approach for learning person trajectory
representations for group activity analysis. The learned representations encode
rich spatio-t... | computer science |
28,287 | Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning
Approach | cs.CV | Face attribute estimation has many potential applications in video
surveillance, face retrieval, and social media. While a number of methods have
been proposed for face attribute estimation, most of them did not explicitly
consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal
and holistic vs. ... | computer science |
28,288 | Deep-Learning Convolutional Neural Networks for scattered shrub
detection with Google Earth Imagery | cs.CV | There is a growing demand for accurate high-resolution land cover maps in
many fields, e.g., in land-use planning and biodiversity conservation.
Developing such maps has been performed using Object-Based Image Analysis
(OBIA) methods, which usually reach good accuracies, but require a high human
supervision and the bes... | computer science |
28,289 | Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action
Recognition | cs.CV | Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-pe... | computer science |
28,290 | See, Hear, and Read: Deep Aligned Representations | cs.CV | We capitalize on large amounts of readily-available, synchronous data to
learn a deep discriminative representations shared across three major natural
modalities: vision, sound and language. By leveraging over a year of sound from
video and millions of sentences paired with images, we jointly train a deep
convolutional... | computer science |
28,291 | Graph-Cut RANSAC | cs.CV | A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in
short, is introduced. To separate inliers and outliers, it runs the graph-cut
algorithm in the local optimization (LO) step which is applied when a
so-far-the-best model is found. The proposed LO step is conceptually simple,
easy to implement, ... | computer science |
28,292 | Order embeddings and character-level convolutions for multimodal
alignment | cs.CV | With the novel and fast advances in the area of deep neural networks, several
challenging image-based tasks have been recently approached by researchers in
pattern recognition and computer vision. In this paper, we address one of these
tasks, which is to match image content with natural language descriptions,
sometimes... | computer science |
28,293 | Image Compression Based on Compressive Sensing: End-to-End Comparison
with JPEG | cs.CV | We present an end-to-end image compression system based on compressive
sensing. The presented system integrates the conventional scheme of compressive
sampling and reconstruction with quantization and entropy coding. The
compression performance, in terms of decoded image quality versus data rate, is
shown to be compara... | computer science |
28,294 | Personalized Age Progression with Bi-level Aging Dictionary Learning | cs.CV | Age progression is defined as aesthetically re-rendering the aging face at
any future age for an individual face. In this work, we aim to automatically
render aging faces in a personalized way. Basically, for each age group, we
learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles),
where the di... | computer science |
28,295 | Brain Intelligence: Go Beyond Artificial Intelligence | cs.CV | Artificial intelligence (AI) is an important technology that supports daily
social life and economic activities. It contributes greatly to the sustainable
growth of Japan's economy and solves various social problems. In recent years,
AI has attracted attention as a key for growth in developed countries such as
Europe a... | computer science |
28,296 | Face R-CNN | cs.CV | Faster R-CNN is one of the most representative and successful methods for
object detection, and has been becoming increasingly popular in various
objection detection applications. In this report, we propose a robust deep face
detection approach based on Faster R-CNN. In our approach, we exploit several
new techniques i... | computer science |
28,297 | A Random-Fern based Feature Approach for Image Matching | cs.CV | Image or object recognition is an important task in computer vision. With the
hight-speed processing power on modern platforms and the availability of mobile
phones everywhere, millions of photos are uploaded to the internet per minute,
it is critical to establish a generic framework for fast and accurate image
process... | computer science |
28,298 | Segmentation of Intracranial Arterial Calcification with Deeply
Supervised Residual Dropout Networks | cs.CV | Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segment... | computer science |
28,299 | Deep Frame Interpolation | cs.CV | This work presents a supervised learning based approach to the computer
vision problem of frame interpolation. The presented technique could also be
used in the cartoon animations since drawing each individual frame consumes a
noticeable amount of time. The most existing solutions to this problem use
unsupervised metho... | computer science |
28,300 | Binary Patterns Encoded Convolutional Neural Networks for Texture
Recognition and Remote Sensing Scene Classification | cs.CV | Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistica... | computer science |
28,301 | A Kind of Affine Weighted Moment Invariants | cs.CV | A new kind of geometric invariants is proposed in this paper, which is called
affine weighted moment invariant (AWMI). By combination of local affine
differential invariants and a framework of global integral, they can more
effectively extract features of images and help to increase the number of
low-order invariants a... | computer science |
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