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31,102 | Shapes Characterization on Address Event Representation Using Histograms
of Oriented Events and an Extended LBP Approach | cs.CV | Address Event Representation is a thriving technology that could change
digital image processing paradigm. This paper proposes a methodology to
characterize the shape of objects using the streaming of asynchronous events. A
new descriptor that enhances spikes connectivity is associated with two
oriented histogram based... | computer science |
31,103 | A Two-Stage Method for Text Line Detection in Historical Documents | cs.CV | This work presents a two-stage text line detection method for historical
documents. In a first stage, a deep neural network called ARU-Net labels pixels
to belong to one of the three classes: baseline, separator or other. The
separator class marks beginning and end of each text line. The ARU-Net is
trainable from scrat... | computer science |
31,104 | Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with
Structural Priors for Semantic Image Segmentation | cs.CV | This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL)
network for semantic image segmentation. The proposed generative architecture
is able to train rapidly from relatively small labeled datasets using the
introduced structural priors. In addition, the number of filters in each layer
of the architec... | computer science |
31,105 | Pros and Cons of GAN Evaluation Measures | cs.CV | Generative models, in particular generative adverserial networks (GANs), have
received a lot of attention recently. A number of GAN variants have been
proposed and have been utilized in many applications. Despite large strides in
terms of theoretical progress, evaluating and comparing GANs remains a daunting
task. Whil... | computer science |
31,106 | ADC: Automated Deep Compression and Acceleration with Reinforcement
Learning | cs.CV | Model compression is an effective technique facilitating the deployment of
neural network models on mobile devices that have limited computation resources
and a tight power budget. However, conventional model compression techniques
use hand-crafted features and require domain experts to explore the large
design space t... | computer science |
31,107 | On-device Scalable Image-based Localization | cs.CV | We present the scalable design of an entire on-device system for large-scale
urban localization. The proposed design integrates compact image retrieval and
2D-3D correspondence search to estimate the camera pose in a city region of
extensive coverage. Our design is GPS agnostic and does not require the network
connecti... | computer science |
31,108 | Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land
Classification | cs.CV | We describe in this paper Hydra, an ensemble of convolutional neural networks
(CNN) for geospatial land classification. The idea behind Hydra is to create an
initial CNN that is coarsely optimized but provides a good starting pointing
for further optimization, which will serve as the Hydra's body. Then, the
obtained we... | computer science |
31,109 | Coverless information hiding based on Generative Model | cs.CV | A new coverless image information hiding method based on generative model is
proposed, we feed the secret image to the generative model database, and
generate a meaning-normal and independent image different from the secret
image, then, the generated image is transmitted to the receiver and is fed to
the generative mod... | computer science |
31,110 | Collaborative Learning for Weakly Supervised Object Detection | cs.CV | Weakly supervised object detection has recently received much attention,
since it only requires image-level labels instead of the bounding-box labels
consumed in strongly supervised learning. Nevertheless, the save in labeling
expense is usually at the cost of model accuracy. In this paper, we propose a
simple but effe... | computer science |
31,111 | Tubule segmentation of fluorescence microscopy images based on
convolutional neural networks with inhomogeneity correction | cs.CV | Fluorescence microscopy has become a widely used tool for studying various
biological structures of in vivo tissue or cells. However, quantitative
analysis of these biological structures remains a challenge due to their
complexity which is exacerbated by distortions caused by lens aberrations and
light scattering. More... | computer science |
31,112 | Joint Learning for Pulmonary Nodule Segmentation, Attributes and
Malignancy Prediction | cs.CV | Refer to the literature of lung nodule classification, many studies adopt
Convolutional Neural Networks (CNN) to directly predict the malignancy of lung
nodules with original thoracic Computed Tomography (CT) and nodule location.
However, these studies cannot tell how the CNN works in terms of predicting the
malignancy... | computer science |
31,113 | Deep Visual Domain Adaptation: A Survey | cs.CV | Deep domain adaption has emerged as a new learning technique to address the
lack of massive amounts of labeled data. Compared to conventional methods,
which learn shared feature subspaces or reuse important source instances with
shallow representations, deep domain adaption methods leverage deep networks to
learn more ... | computer science |
31,114 | Optimize transfer learning for lung diseases in bronchoscopy using a new
concept: sequential fine-tuning | cs.CV | Bronchoscopy inspection as a follow-up procedure from the radiological
imaging plays a key role in lung disease diagnosis and determining treatment
plans for the patients. Doctors needs to make a decision whether to biopsy the
patients timely when performing bronchoscopy. However, the doctors also needs
to be very sele... | computer science |
31,115 | Unthule: An Incremental Graph Construction Process for Robust Road Map
Extraction from Aerial Images | cs.CV | The availability of highly accurate maps has become crucial due to the
increasing importance of location-based mobile applications as well as
autonomous vehicles. However, mapping roads is currently an expensive and
human-intensive process. High-resolution aerial imagery provides a promising
avenue to automatically inf... | computer science |
31,116 | FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy | cs.CV | We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and
accurate network for very limited computational budgets (e.g., 10-140 MFLOPs).
Our key idea is applying an aggressive downsampling strategy to MobileNet
framework. In FD-MobileNet, we perform 32$\times$ downsampling within 12
layers, only half the ... | computer science |
31,117 | Learning Deep Convolutional Networks for Demosaicing | cs.CV | This paper presents a comprehensive study of applying the convolutional
neural network (CNN) to solving the demosaicing problem. The paper presents two
CNN models that learn end-to-end mappings between the mosaic samples and the
original image patches with full information. In the case the Bayer color
filter array (CFA... | computer science |
31,118 | FlipDial: A Generative Model for Two-Way Visual Dialogue | cs.CV | We present FlipDial, a generative model for visual dialogue that
simultaneously plays the role of both participants in a visually-grounded
dialogue. Given context in the form of an image and an associated caption
summarising the contents of the image, FlipDial learns both to answer questions
and put forward questions, ... | computer science |
31,119 | Edge-Host Partitioning of Deep Neural Networks with Feature Space
Encoding for Resource-Constrained Internet-of-Things Platforms | cs.CV | This paper introduces partitioning an inference task of a deep neural network
between an edge and a host platform in the IoT environment. We present a DNN as
an encoding pipeline, and propose to transmit the output feature space of an
intermediate layer to the host. The lossless or lossy encoding of the feature
space i... | computer science |
31,120 | Deep feature compression for collaborative object detection | cs.CV | Recent studies have shown that the efficiency of deep neural networks in
mobile applications can be significantly improved by distributing the
computational workload between the mobile device and the cloud. This paradigm,
termed collaborative intelligence, involves communicating feature data between
the mobile and the ... | computer science |
31,121 | Object Detection with Mask-based Feature Encoding | cs.CV | Region-based Convolutional Neural Networks (R-CNNs) have achieved great
success in the field of object detection. The existing R-CNNs usually divide a
Region-of-Interest (ROI) into grids, and then localize objects by utilizing the
spatial information reflected by the relative position of each grid in the ROI.
In this p... | computer science |
31,122 | Temporal and Volumetric Denoising via Quantile Sparse Image (QuaSI)
Prior in Optical Coherence Tomography and Beyond | cs.CV | This paper introduces an universal and structure-preserving regularization
term, called quantile sparse image (QuaSI) prior. The prior is suitable for
denoising images from various medical image modalities. We demonstrate its
effectivness on volumetric optical coherence tomography (OCT) and computed
tomography (CT) dat... | computer science |
31,123 | Integration of Absolute Orientation Measurements in the KinectFusion
Reconstruction pipeline | cs.CV | In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regul... | computer science |
31,124 | Subspace Support Vector Data Description | cs.CV | This paper proposes a novel method for solving one-class classification
problems. The proposed approach, namely Subspace Support Vector Data
Description, maps the data to a subspace that is optimized for one-class
classification. In that feature space, the optimal hypersphere enclosing the
target class is then determin... | computer science |
31,125 | Blind Image Deconvolution using Deep Generative Priors | cs.CV | This paper proposes a new framework to regularize the \textit{ill-posed} and
\textit{non-linear} blind image deconvolution problem by using deep generative
priors. We employ two separate deep generative models --- one trained to
produce sharp images while the other trained to generate blur kernels from
lower-dimensiona... | computer science |
31,126 | Image-based Synthesis for Deep 3D Human Pose Estimation | cs.CV | This paper addresses the problem of 3D human pose estimation in the wild. A
significant challenge is the lack of training data, i.e., 2D images of humans
annotated with 3D poses. Such data is necessary to train state-of-the-art CNN
architectures. Here, we propose a solution to generate a large set of
photorealistic syn... | computer science |
31,127 | Image Retargetability | cs.CV | Real-world applications could benefit from the ability to automatically
retarget an image to different aspect ratios and resolutions, while preserving
its visually and semantically important content. However, not all images can be
equally well processed that way. In this work, we introduce the notion of image
retargeta... | computer science |
31,128 | Recurrent Slice Networks for 3D Segmentation on Point Clouds | cs.CV | In this paper, we present a conceptually simple and powerful framework,
Recurrent Slice Network (RSNet), for 3D semantic segmentation on point clouds.
Performing 3D segmentation on point clouds is computationally efficient. And it
is free of the quantitation artifact problems which exists in other 3D data
formats such ... | computer science |
31,129 | Texture Classification in Extreme Scale Variations using GANet | cs.CV | Research in texture recognition often concentrates on recognizing textures
with intraclass variations such as illumination, rotation, viewpoint and small
scale changes. In contrast, in real-world applications a change in scale can
have a dramatic impact on texture appearance, to the point of changing
completely from on... | computer science |
31,130 | An Optimized Architecture for Unpaired Image-to-Image Translation | cs.CV | Unpaired Image-to-Image translation aims to convert the image from one domain
(input domain A) to another domain (target domain B), without providing paired
examples for the training. The state-of-the-art, Cycle-GAN demonstrated the
power of Generative Adversarial Networks with Cycle-Consistency Loss. While its
results... | computer science |
31,131 | Robust Deformation Estimation in Wood-Composite Materials using
Variational Optical Flow | cs.CV | Wood-composite materials are widely used today as they homogenize humidity
related directional deformations. Quantification of these deformations as
coefficients is important for construction and engineering and topic of current
research but still a manual process.
This work introduces a novel computer vision approac... | computer science |
31,132 | Automatic localization and decoding of honeybee markers using deep
convolutional neural networks | cs.CV | The honeybee is a fascinating model animal to investigate how collective
behavior emerges from (inter-)actions of thousands of individuals. Bees may
acquire unique memories throughout their lives. These experiences affect social
interactions even over large time frames. Tracking and identifying all bees in
the colony o... | computer science |
31,133 | Modelling of Facial Aging and Kinship: A Survey | cs.CV | Computational facial models that capture properties of facial cues related to
aging and kinship increasingly attract the attention of the research community,
enabling the development of reliable methods for age progression, age
estimation, age-invariant facial characterization, and kinship verification
from visual data... | computer science |
31,134 | Single-Perspective Warps in Natural Image Stitching | cs.CV | Results of image stitching can be perceptually divided into
single-perspective and multiple-perspective. Compared to the
multiple-perspective result, the single-perspective result excels in
perspective consistency but suffers from projective distortion. In this paper,
we propose two single-perspective warps for natural... | computer science |
31,135 | BIRNet: Brain Image Registration Using Dual-Supervised Fully
Convolutional Networks | cs.CV | In this paper, we propose a deep learning approach for image registration by
predicting deformation from image appearance. Since obtaining ground-truth
deformation fields for training can be challenging, we design a fully
convolutional network that is subject to dual-guidance: (1) Coarse guidance
using deformation fiel... | computer science |
31,136 | Joint Demosaicing and Denoising with Perceptual Optimization on a
Generative Adversarial Network | cs.CV | Image demosaicing - one of the most important early stages in digital camera
pipelines - addressed the problem of reconstructing a full-resolution image
from so-called color-filter-arrays. Despite tremendous progress made in the
pase decade, a fundamental issue that remains to be addressed is how to assure
the visual q... | computer science |
31,137 | Semantic Scene Completion Combining Colour and Depth: preliminary
experiments | cs.CV | Semantic scene completion is the task of producing a complete 3D voxel
representation of volumetric occupancy with semantic labels for a scene from a
single-view observation. We built upon the recent work of Song et al. (CVPR
2017), who proposed SSCnet, a method that performs scene completion and
semantic labelling in ... | computer science |
31,138 | Joint 3D Reconstruction of a Static Scene and Moving Objects | cs.CV | We present a technique for simultaneous 3D reconstruction of static regions
and rigidly moving objects in a scene. An RGB-D frame is represented as a
collection of features, which are points and planes. We classify the features
into static and dynamic regions and grow separate maps, static and object maps,
for each of ... | computer science |
31,139 | Deep Predictive Coding Network for Object Recognition | cs.CV | Inspired by predictive coding in neuroscience, we designed a bi-directional
and recurrent neural net, namely deep predictive coding networks (PCN). It uses
convolutional layers in both feedforward and feedback networks, and recurrent
connections within each layer. Feedback connections from a higher layer carry
the pred... | computer science |
31,140 | Satellite Image Forgery Detection and Localization Using GAN and
One-Class Classifier | cs.CV | Current satellite imaging technology enables shooting high-resolution
pictures of the ground. As any other kind of digital images, overhead pictures
can also be easily forged. However, common image forensic techniques are often
developed for consumer camera images, which strongly differ in their nature
from satellite o... | computer science |
31,141 | Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A
Survey | cs.CV | Osteoarthritis (OA) is one of the major health issues among the elderly
population. MRI is the most popular technology to observe and evaluate the
progress of OA course. However, the extreme labor cost of MRI analysis makes
the process inefficient and expensive. Also, due to human error and subjective
nature, the inter... | computer science |
31,142 | Web-Scale Responsive Visual Search at Bing | cs.CV | In this paper, we introduce a web-scale general visual search system deployed
in Microsoft Bing. The system accommodates tens of billions of images in the
index, with thousands of features for each image, and can respond in less than
200 ms. In order to overcome the challenges in relevance, latency, and
scalability in ... | computer science |
31,143 | Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks | cs.CV | Human behavior understanding is arguably one of the most important mid-level
components in artificial intelligence. In order to efficiently make use of
data, multi-task learning has been studied in diverse computer vision tasks
including human behavior understanding. However, multi-task learning relies on
task specific... | computer science |
31,144 | Paraphrasing Complex Network: Network Compression via Factor Transfer | cs.CV | Deep neural networks (DNN) have recently shown promising performances in
various areas. Although DNNs are very powerful, a large number of network
parameters requires substantial storage and memory bandwidth which hinders them
from being applied to actual embedded systems. Many researchers have sought
ways of model com... | computer science |
31,145 | M4CD: A Robust Change Detection Method for Intelligent Visual
Surveillance | cs.CV | In this paper, we propose a robust change detection method for intelligent
visual surveillance. This method, named M4CD, includes three major steps.
Firstly, a sample-based background model that integrates color and texture cues
is built and updated over time. Secondly, multiple heterogeneous features
(including bright... | computer science |
31,146 | Recursive Chaining of Reversible Image-to-image Translators For Face
Aging | cs.CV | This paper addresses the modeling and simulation of progressive changes over
time, such as human face aging. By treating the age phases as a sequence of
image domains, we construct a chain of transformers that map images from one
age domain to the next. Leveraging recent adversarial image translation
methods, our appro... | computer science |
31,147 | The Multiscale Bowler-Hat Transform for Vessel Enhancement in 3D
Biomedical Images | cs.CV | Enhancement and detection of 3D vessel-like structures has long been an open
problem as most existing image processing methods fail in many aspects,
including a lack of uniform enhancement between vessels of different radii and
a lack of enhancement at the junctions.
Here, we propose a method based on mathematical mo... | computer science |
31,148 | Two Is Harder To Recognize Than Tom: the Challenge of Visual Numerosity
for Deep Learning | cs.CV | In the spirit of Turing test, we design and conduct a set of visual
numerosity experiments with deep neural networks. We train DCNNs with a large
number of sample images that are varied visual representations of small natural
numbers, towards the objective of learning numerosity perception. Numerosity
perception, or th... | computer science |
31,149 | Sampling Superquadric Point Clouds with Normals | cs.CV | Superquadrics provide a compact representation of common shapes and have been
used both for object/surface modelling in computer graphics and as object-part
representation in computer vision and robotics. Superquadrics refer to a family
of shapes: here we deal with the superellipsoids and superparaboloids. Due to
the s... | computer science |
31,150 | Fully Convolutional Network Ensembles for White Matter Hyperintensities
Segmentation in MR Images | cs.CV | White matter hyperintensities (WMH) are commonly found in the brains of
healthy elderly individuals and have been associated with various neurological
and geriatric disorders. In this paper, we present a study using deep fully
convolutional network and ensemble models to automatically detect such WMH
using fluid attenu... | computer science |
31,151 | AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation | cs.CV | We introduce a method for learning to generate the surface of 3D shapes. Our
approach represents a 3D shape as a collection of parametric surface elements
and, in contrast to methods generating voxel grids or point clouds, naturally
infers a surface representation of the shape. Beyond its novelty, our new shape
generat... | computer science |
31,152 | Learning from a Handful Volumes: MRI Resolution Enhancement with
Volumetric Super-Resolution Forests | cs.CV | Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical
structures. However, the acquisition of MR volumes with high spatial resolution
leads to long scan times. To this end, we propose volumetric super-resolution
forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a
locally linear ma... | computer science |
31,153 | Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using
3D Geometric Constraints | cs.CV | We present a novel approach for unsupervised learning of depth and ego-motion
from monocular video. Unsupervised learning removes the need for separate
supervisory signals (depth or ego-motion ground truth, or multi-view video).
Prior work in unsupervised depth learning uses pixel-wise or gradient-based
losses, which o... | computer science |
31,154 | Towards End-to-End Lane Detection: an Instance Segmentation Approach | cs.CV | Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lan... | computer science |
31,155 | 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer
Learning from a 2D Trained Network | cs.CV | Low-dose computed tomography (CT) has attracted a major attention in the
medical imaging field, since CT-associated x-ray radiation carries health risks
for patients. The reduction of CT radiation dose, however, compromises the
signal-to-noise ratio, and may compromise the image quality and the diagnostic
performance. ... | computer science |
31,156 | Inverting The Generator Of A Generative Adversarial Network (II) | cs.CV | Generative adversarial networks (GANs) learn a deep generative model that is
able to synthesise novel, high-dimensional data samples. New data samples are
synthesised by passing latent samples, drawn from a chosen prior distribution,
through the generative model. Once trained, the latent space exhibits
interesting prop... | computer science |
31,157 | Image Transformer | cs.CV | Image generation has been successfully cast as an autoregressive sequence
generation or transformation problem. Recent work has shown that self-attention
is an effective way of modeling textual sequences. In this work, we generalize
a recently proposed model architecture based on self-attention, the
Transformer, to a s... | computer science |
31,158 | Detecting Anomalous Faces with 'No Peeking' Autoencoders | cs.CV | Detecting anomalous faces has important applications. For example, a system
might tell when a train driver is incapacitated by a medical event, and assist
in adopting a safe recovery strategy. These applications are demanding, because
they require accurate detection of rare anomalies that may be seen only at
runtime. S... | computer science |
31,159 | ISEC: Iterative over-Segmentation via Edge Clustering | cs.CV | Several image pattern recognition tasks rely on superpixel generation as a
fundamental step. Image analysis based on superpixels facilitates
domain-specific applications, also speeding up the overall processing time of
the task. Recent superpixel methods have been designed to fit boundary
adherence, usually regulating ... | computer science |
31,160 | SpaRTA - Tracking across occlusions via global partitioning of 3D clouds
of points | cs.CV | Any 3D tracking algorithm has to deal with occlusions: multiple targets get
so close to each other that the loss of their identities becomes likely. In the
best case scenario, trajectories are interrupted, thus curbing the completeness
of the data-set; in the worse case scenario, identity switches arise,
potentially af... | computer science |
31,161 | Training Deep Face Recognition Systems with Synthetic Data | cs.CV | Recent advances in deep learning have significantly increased the performance
of face recognition systems. The performance and reliability of these models
depend heavily on the amount and quality of the training data. However, the
collection of annotated large datasets does not scale well and the control over
the quali... | computer science |
31,162 | A complete hand-drawn sketch vectorization framework | cs.CV | Vectorizing hand-drawn sketches is a challenging task, which is of paramount
importance for creating CAD vectorized versions for the fashion and creative
workflows. This paper proposes a complete framework that automatically
transforms noisy and complex hand-drawn sketches with different stroke types in
a precise, reli... | computer science |
31,163 | Recognizing Cuneiform Signs Using Graph Based Methods | cs.CV | The cuneiform script constitutes one of the earliest systems of writing and
is realized by wedge-shaped marks on clay tablets. A tremendous number of
cuneiform tablets have already been discovered and are incrementally
digitalized and made available to automated processing. As reading cuneiform
script is still a manual... | computer science |
31,164 | An Image Processing based Object Counting Approach for Machine Vision
Application | cs.CV | Machine vision applications are low cost and high precision measurement
systems which are frequently used in production lines. With these systems that
provide contactless control and measurement, production facilities are able to
reach high production numbers without errors. Machine vision operations such as
product co... | computer science |
31,165 | 3D Regression Neural Network for the Quantification of Enlarged
Perivascular Spaces in Brain MRI | cs.CV | Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiolog... | computer science |
31,166 | Scenarios: A New Representation for Complex Scene Understanding | cs.CV | The ability for computational agents to reason about the high-level content
of real world scene images is important for many applications. Existing
attempts at addressing the problem of complex scene understanding lack
representational power, efficiency, and the ability to create robust
meta-knowledge about scenes. In ... | computer science |
31,167 | Fast, Trainable, Multiscale Denoising | cs.CV | Denoising is a fundamental imaging problem. Versatile but fast filtering has
been demanded for mobile camera systems. We present an approach to multiscale
filtering which allows real-time applications on low-powered devices. The key
idea is to learn a set of kernels that upscales, filters, and blends patches of
differe... | computer science |
31,168 | Real-Time 3D Shape of Micro-Details | cs.CV | Motivated by the growing demand for interactive environments, we propose an
accurate real-time 3D shape reconstruction technique. To provide a reliable 3D
reconstruction which is still a challenging task when dealing with real-world
applications, we integrate several components including (i) Photometric Stereo
(PS), (i... | computer science |
31,169 | Semi-supervised multi-task learning for lung cancer diagnosis | cs.CV | Early detection of lung nodules is of great importance in lung cancer
screening. Existing research recognizes the critical role played by CAD systems
in early detection and diagnosis of lung nodules. However, many CAD systems,
which are used as cancer detection tools, produce a lot of false positives (FP)
and require a... | computer science |
31,170 | HWNet v2: An Efficient Word Image Representation for Handwritten
Documents | cs.CV | We present a framework for learning efficient holistic representation for
handwritten word images. The proposed method uses a deep convolutional neural
network with traditional classification loss. The major strengths of our work
lie in: (i) the efficient usage of synthetic data to pre-train a deep network,
(ii) an ada... | computer science |
31,171 | Towards Principled Design of Deep Convolutional Networks: Introducing
SimpNet | cs.CV | Major winning Convolutional Neural Networks (CNNs), such as VGGNet, ResNet,
DenseNet, \etc, include tens to hundreds of millions of parameters, which
impose considerable computation and memory overheads. This limits their
practical usage in training and optimizing for real-world applications. On the
contrary, light-wei... | computer science |
31,172 | A New De-blurring Technique for License Plate Images with Robust Length
Estimation | cs.CV | Recognizing a license plate clearly while seeing a surveillance camera
snapshot is often important in cases where the troublemaker vehicle(s) have to
be identified. In many real world situations, these images are blurred due to
fast motion of the vehicle and cannot be recognized by the human eye. For this
kind of blurr... | computer science |
31,173 | A Collaborative Computer Aided Diagnosis (C-CAD) System with
Eye-Tracking, Sparse Attentional Model, and Deep Learning | cs.CV | There are at least two categories of errors in radiology screening that can
lead to suboptimal diagnostic decisions and interventions:(i)human fallibility
and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are
developed to help radiologists to compensate for some of these errors. However,
despit... | computer science |
31,174 | Visual-Only Recognition of Normal, Whispered and Silent Speech | cs.CV | Silent speech interfaces have been recently proposed as a way to enable
communication when the acoustic signal is not available. This introduces the
need to build visual speech recognition systems for silent and whispered
speech. However, almost all the recently proposed systems have been trained on
vocalised data only... | computer science |
31,175 | Using 3D Hahn Moments as A Computational Representation of ATS Drugs
Molecular Structure | cs.CV | The campaign against drug abuse is fought by all countries, most notably on
ATS drugs. The technical limitations of the current test kits to detect new
brand of ATS drugs present a challenge to law enforcement authorities and
forensic laboratories. Meanwhile, new molecular imaging devices which allowed
mankind to chara... | computer science |
31,176 | End-to-end Audiovisual Speech Recognition | cs.CV | Several end-to-end deep learning approaches have been recently presented
which extract either audio or visual features from the input images or audio
signals and perform speech recognition. However, research on end-to-end
audiovisual models is very limited. In this work, we present an end-to-end
audiovisual model based... | computer science |
31,177 | Fast 5DOF Needle Tracking in iOCT | cs.CV | Purpose. Intraoperative Optical Coherence Tomography (iOCT) is an
increasingly available imaging technique for ophthalmic microsurgery that
provides high-resolution cross-sectional information of the surgical scene. We
propose to build on its desirable qualities and present a method for tracking
the orientation and loc... | computer science |
31,178 | DA-GAN: Instance-level Image Translation by Deep Attention Generative
Adversarial Networks (with Supplementary Materials) | cs.CV | Unsupervised image translation, which aims in translating two independent
sets of images, is challenging in discovering the correct correspondences
without paired data. Existing works build upon Generative Adversarial Network
(GAN) such that the distribution of the translated images are indistinguishable
from the distr... | computer science |
31,179 | Structured Label Inference for Visual Understanding | cs.CV | Visual data such as images and videos contain a rich source of structured
semantic labels as well as a wide range of interacting components. Visual
content could be assigned with fine-grained labels describing major components,
coarse-grained labels depicting high level abstractions, or a set of labels
revealing attrib... | computer science |
31,180 | A Closed-form Solution to Photorealistic Image Stylization | cs.CV | Photorealistic image style transfer algorithms aim at stylizing a content
photo using the style of a reference photo with the constraint that the
stylized photo should remains photorealistic. While several methods exist for
this task, they tend to generate spatially inconsistent stylizations with
noticeable artifacts. ... | computer science |
31,181 | Image Forensics: Detecting duplication of scientific images with
manipulation-invariant image similarity | cs.CV | Manipulation and re-use of images in scientific publications is a concerning
problem that currently lacks a scalable solution. Current tools for detecting
image duplication are mostly manual or semi-automated, despite the availability
of an overwhelming target dataset for a learning-based approach. This paper
addresses... | computer science |
31,182 | Salient Object Detection by Lossless Feature Reflection | cs.CV | Salient object detection, which aims to identify and locate the most salient
pixels or regions in images, has been attracting more and more interest due to
its various real-world applications. However, this vision task is quite
challenging, especially under complex image scenes. Inspired by the intrinsic
reflection of ... | computer science |
31,183 | Weighted Linear Discriminant Analysis based on Class Saliency
Information | cs.CV | In this paper, we propose a new variant of Linear Discriminant Analysis to
overcome underlying drawbacks of traditional LDA and other LDA variants
targeting problems involving imbalanced classes. Traditional LDA sets
assumptions related to Gaussian class distribution and neglects influence of
outlier classes, that migh... | computer science |
31,184 | Deep Residual Network for Joint Demosaicing and Super-Resolution | cs.CV | In digital photography, two image restoration tasks have been studied
extensively and resolved independently: demosaicing and super-resolution. Both
these tasks are related to resolution limitations of the camera. Performing
super-resolution on a demosaiced images simply exacerbates the artifacts
introduced by demosaic... | computer science |
31,185 | Osteoarthritis Disease Detection System using Self Organizing Maps
Method based on Ossa Manus X-Ray | cs.CV | Osteoarthritis is a disease found in the world, including in Indonesia. The
purpose of this study was to detect the disease Osteoarthritis using Self
Organizing mapping (SOM), and to know the procedure of artificial intelligence
on the methods of Self Organizing Mapping (SOM). In this system, there are
several stages t... | computer science |
31,186 | Simultaneous Compression and Quantization: A Joint Approach for
Efficient Unsupervised Hashing | cs.CV | The two most important requirements for unsupervised data-dependent hashing
methods are to preserve similarity in the low-dimensional feature space and to
minimize the binary quantization loss. Even though there are many hashing
methods that have been proposed in the literature, there is room for
improvement to address... | computer science |
31,187 | Multi-task, multi-label and multi-domain learning with residual
convolutional networks for emotion recognition | cs.CV | Automated emotion recognition in the wild from facial images remains a
challenging problem. Although recent advances in Deep Learning have supposed a
significant breakthrough in this topic, strong changes in pose, orientation and
point of view severely harm current approaches. In addition, the acquisition of
labeled da... | computer science |
31,188 | Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face
Alignment | cs.CV | Heatmap regression has been used for landmark localization for quite a while
now. Most of the methods use a very deep stack of bottleneck modules for
heatmap classification stage, followed by heatmap regression to extract the
keypoints. In this paper, we present a single dendritic CNN, termed as Pose
Conditioned Dendri... | computer science |
31,189 | Learning Representative Temporal Features for Action Recognition | cs.CV | In this paper, a novel video classification methodology is presented that
aims to recognize different categories of third-person videos efficiently. The
idea is to keep track of motion in videos by following optical flow elements
over time. To classify the resulted motion time series efficiently, the idea is
letting th... | computer science |
31,190 | Online Action Detection in Untrimmed, Streaming Videos - Modeling and
Evaluation | cs.CV | The goal of Online Action Detection (OAD) is to detect action in a timely
manner and to recognize its action category. Early works focused on early
action detection, which is effectively formulated as a classification problem
instead of online detection in streaming videos, because these works used
partially seen short... | computer science |
31,191 | Automated soft tissue lesion detection and segmentation in digital
mammography using a u-net deep learning network | cs.CV | Computer-aided detection or decision support systems aim to improve breast
cancer screening programs by helping radiologists to evaluate digital
mammography (DM) exams. Commonly such methods proceed in two steps: selection
of candidate regions for malignancy, and later classification as either
malignant or not. In this... | computer science |
31,192 | Machine Learning Methods for Solving Assignment Problems in Multi-Target
Tracking | cs.CV | Data association and track-to-track association, two fundamental problems in
single-sensor and multi-sensor multi-target tracking, are instances of an
NP-hard combinatorial optimization problem known as the multidimensional
assignment problem (MDAP). Over the last few years, data-driven approaches to
tackling MDAPs in ... | computer science |
31,193 | Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net)
for Medical Image Segmentation | cs.CV | Deep learning (DL) based semantic segmentation methods have been providing
state-of-the-art performance in the last few years. More specifically, these
techniques have been successfully applied to medical image classification,
segmentation, and detection tasks. One deep learning technique, U-Net, has
become one of the ... | computer science |
31,194 | Agile Amulet: Real-Time Salient Object Detection with Contextual
Attention | cs.CV | This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile
Amulet) for salient object detection. The Agile Amulet builds on previous works
to predict saliency maps using multi-level convolutional features. Compared to
previous works, Agile Amulet employs some key innovations to improve training
and t... | computer science |
31,195 | Co-occurrence matrix analysis-based semi-supervised training for object
detection | cs.CV | One of the most important factors in training object recognition networks
using convolutional neural networks (CNNs) is the provision of annotated data
accompanying human judgment. Particularly, in object detection or semantic
segmentation, the annotation process requires considerable human effort. In
this paper, we pr... | computer science |
31,196 | A survey on trajectory clustering analysis | cs.CV | This paper comprehensively surveys the development of trajectory clustering.
Considering the critical role of trajectory data mining in modern intelligent
systems for surveillance security, abnormal behavior detection, crowd behavior
analysis, and traffic control, trajectory clustering has attracted growing
attention. ... | computer science |
31,197 | Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary
Sparse Representation | cs.CV | Hyperspectral images have far more spectral bands than ordinary multispectral
images. Rich band information provides more favorable conditions for the
tremendous applications. However, significant increase in the dimensionality of
spectral bands may lead to the curse of dimensionality, especially for
classification app... | computer science |
31,198 | Fusing Video and Inertial Sensor Data for Walking Person Identification | cs.CV | An autonomous computer system (such as a robot) typically needs to identify,
locate, and track persons appearing in its sight. However, most solutions have
their limitations regarding efficiency, practicability, or environmental
constraints. In this paper, we propose an effective and practical system which
combines vid... | computer science |
31,199 | Do deep nets really need weight decay and dropout? | cs.CV | The impressive success of modern deep neural networks on computer vision
tasks has been achieved through models of very large capacity compared to the
number of available training examples. This overparameterization is often said
to be controlled with the help of different regularization techniques, mainly
weight decay... | computer science |
31,200 | Latent RANSAC | cs.CV | We present a method that can evaluate a RANSAC hypothesis in constant time,
i.e. independent of the size of the data. A key observation here is that
correct hypotheses are tightly clustered together in the latent parameter
domain. In a manner similar to the generalized Hough transform we seek to find
this cluster, only... | computer science |
31,201 | Novel View Synthesis for Large-scale Scene using Adversarial Loss | cs.CV | Novel view synthesis aims to synthesize new images from different viewpoints
of given images. Most of previous works focus on generating novel views of
certain objects with a fixed background. However, for some applications, such
as virtual reality or robotic manipulations, large changes in background may
occur due to ... | computer science |
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