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29,902 | Towards Effective Low-bitwidth Convolutional Neural Networks | cs.CV | This paper tackles the problem of training a deep convolutional neural
network with both low-precision weights and low-bitwidth activations.
Optimizing a low-precision network is very challenging since the training
process can easily get trapped in a poor local minima, which results in
substantial accuracy loss. To mit... | computer science |
29,903 | 3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D
Object Detection | cs.CV | This paper aims at developing a faster and a more accurate solution to the
amodal 3D object detection problem for indoor scenes. It is achieved through a
novel neural network that takes a pair of RGB-D images as the input and
delivers oriented 3D bounding boxes as the output. The network, named 3D-SSD,
composed of two ... | computer science |
29,904 | Adversarial Learning of Structure-Aware Fully Convolutional Networks for
Landmark Localization | cs.CV | Landmark/pose estimation in single monocular images have received much effort
in computer vision due to its important applications. It remains a challenging
task when input images severe occlusions caused by, e.g., adverse camera views.
Under such circumstances, biologically implausible pose predictions may be
produced... | computer science |
29,905 | Multi-View Data Generation Without View Supervision | cs.CV | The development of high-dimensional generative models has recently gained a
great surge of interest with the introduction of variational auto-encoders and
generative adversarial neural networks. Different variants have been proposed
where the underlying latent space is structured, for example, based on
attributes descr... | computer science |
29,906 | Robust Saliency Detection via Fusing Foreground and Background Priors | cs.CV | Automatic Salient object detection has received tremendous attention from
research community and has been an increasingly important tool in many computer
vision tasks. This paper proposes a novel bottom-up salient object detection
framework which considers both foreground and background cues. First, A series
of backgro... | computer science |
29,907 | Automatic calcium scoring in low-dose chest CT using deep neural
networks with dilated convolutions | cs.CV | Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans
acquired in screening enable quantification of atherosclerotic calcifications
and thus enable identification of subjects at increased cardiovascular risk.
This paper presen... | computer science |
29,908 | Complex-valued image denosing based on group-wise complex-domain
sparsity | cs.CV | Phase imaging and wavefront reconstruction from noisy observations of complex
exponent is a topic of this paper. It is a highly non-linear problem because
the exponent is a 2{\pi}-periodic function of phase. The reconstruction of
phase and amplitude is difficult. Even with an additive Gaussian noise in
observations dis... | computer science |
29,909 | Data, Depth, and Design: Learning Reliable Models for Melanoma Screening | cs.CV | State of the art on melanoma screening evolved rapidly in the last two years,
with the adoption of deep learning. Those models, however, pose challenges of
their own, as they are expensive to train and difficult to parameterize.
Objective: We investigate the methodological issues for designing and
evaluating deep learn... | computer science |
29,910 | Almost instant brain atlas segmentation for large-scale studies | cs.CV | Large scale studies of group differences in healthy controls and patients and
screenings for early stage disease prevention programs require processing and
analysis of extensive multisubject datasets. Complexity of the task increases
even further when segmenting structural MRI of the brain into an atlas with
more than ... | computer science |
29,911 | Widening siamese architectures for stereo matching | cs.CV | Computational stereo is one of the classical problems in computer vision.
Numerous algorithms and solutions have been reported in recent years focusing
on developing methods for computing similarity, aggregating it to obtain
spatial support and finally optimizing an energy function to find the final
disparity. In this ... | computer science |
29,912 | Random Subspace Two-dimensional LDA for Face Recognition | cs.CV | In this paper, a novel technique named random subspace two-dimensional LDA
(RS-2DLDA) is developed for face recognition. This approach offers a number of
improvements over the random subspace two-dimensional PCA (RS2DPCA) framework
introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more
discrimi... | computer science |
29,913 | Deep Learning from Noisy Image Labels with Quality Embedding | cs.CV | There is an emerging trend to leverage noisy image datasets in many visual
recognition tasks. However, the label noise among the datasets severely
degenerates the \mbox{performance of deep} learning approaches. Recently, one
mainstream is to introduce the latent label to handle label noise, which has
shown promising im... | computer science |
29,914 | A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image
Enhancement | cs.CV | Low-light images are not conducive to human observation and computer vision
algorithms due to their low visibility. Although many image enhancement
techniques have been proposed to solve this problem, existing methods
inevitably introduce contrast under- and over-enhancement. Inspired by human
visual system, we design ... | computer science |
29,915 | Data Augmentation in Emotion Classification Using Generative Adversarial
Networks | cs.CV | It is a difficult task to classify images with multiple class labels using
only a small number of labeled examples, especially when the label (class)
distribution is imbalanced. Emotion classification is such an example of
imbalanced label distribution, because some classes of emotions like
\emph{disgusted} are relativ... | computer science |
29,916 | Development and validation of a novel dementia of Alzheimer's type (DAT)
score based on metabolism FDG-PET imaging | cs.CV | Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D
topographic brain glucose metabolism patterns from normal controls (NC) and
individuals with dementia of Alzheimer's type (DAT) are used to train a novel
multi-scale ensemble classification model. This ensemble model outputs a
FDG-PET DAT score ... | computer science |
29,917 | Understanding and Predicting The Attractiveness of Human Action Shot | cs.CV | Selecting attractive photos from a human action shot sequence is quite
challenging, because of the subjective nature of the "attractiveness", which is
mainly a combined factor of human pose in action and the background. Prior
works have actively studied high-level image attributes including
interestingness, memorabilit... | computer science |
29,918 | Statistical evaluation of visual quality metrics for image denoising | cs.CV | This paper studies the problem of full reference visual quality assessment of
denoised images with a special emphasis on images with low contrast and
noise-like texture. Denoising of such images together with noise removal often
results in image details loss or smoothing. A new test image database, FLT,
containing 75 n... | computer science |
29,919 | The Achievement of Higher Flexibility in Multiple Choice-based Tests
Using Image Classification Techniques | cs.CV | In spite of the high accuracy of the existing optical mark reading (OMR)
systems and devices, a few restrictions remain existent. In this work, we aim
to reduce the restrictions of multiple choice questions (MCQ) within tests. We
use an image registration technique to extract the answer boxes from the answer
sheets. Un... | computer science |
29,920 | AxonDeepSeg: automatic axon and myelin segmentation from microscopy data
using convolutional neural networks | cs.CV | Segmentation of axon and myelin from microscopy images of the nervous system
provides useful quantitative information about the tissue microstructure, such
as axon density and myelin thickness. This could be used for instance to
document cell morphometry across species, or to validate novel non-invasive
quantitative ma... | computer science |
29,921 | In-Bed Pose Estimation: Deep Learning with Shallow Dataset | cs.CV | Although human pose estimation for various computer vision (CV) applications
has been studied extensively in the last few decades, yet in-bed pose
estimation using camera-based vision methods has been ignored by the CV
community because it is assumed to be identical to the general purpose pose
estimation methods. Howev... | computer science |
29,922 | A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical
Supervision | cs.CV | Being inspired by child's learning experience - taught first and followed by
observation and questioning, we investigate a critically supervised learning
methodology for object detection in this work. Specifically, we propose a
taught-observe-ask (TOA) method that consists of several novel components such
as negative o... | computer science |
29,923 | Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection | cs.CV | With the development of depth cameras such as Kinect and Intel Realsense,
RGB-D based human detection receives continuous research attention due to its
usage in a variety of applications. In this paper, we propose a new
Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual
information is sequentially in... | computer science |
29,924 | Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR
Detection, Orientation, and Segmentation with Deep Neural Networks | cs.CV | Pixelwise segmentation of the left ventricular (LV) myocardium and the four
cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is
an essential preprocessing step for a wide range of analyses. Variability in
contrast, appearance, orientation, and placement of the heart between patients,
clinical ... | computer science |
29,925 | Motion Artifact Detection in Confocal Laser Endomicroscopy Images | cs.CV | Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing
non-invasive examination of the mucosa on a (sub)cellular level, has proven to
be a valuable diagnostic tool in gastroenterology and shows promising results
in various anatomical regions including the oral cavity. Recently, the
feasibility of au... | computer science |
29,926 | End-to-end Flow Correlation Tracking with Spatial-temporal Attention | cs.CV | Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal informa... | computer science |
29,927 | Distributed Unmixing of Hyperspectral Data With Sparsity Constraint | cs.CV | Spectral unmixing (SU) is a data processing problem in hyperspectral remote
sensing. The significant challenge in the SU problem is how to identify
endmembers and their weights, accurately. For estimation of signature and
fractional abundance matrices in a blind problem, nonnegative matrix
factorization (NMF) and its d... | computer science |
29,928 | Computationally efficient cardiac views projection using 3D
Convolutional Neural Networks | cs.CV | 4D Flow is an MRI sequence which allows acquisition of 3D images of the
heart. The data is typically acquired volumetrically, so it must be reformatted
to generate cardiac long axis and short axis views for diagnostic
interpretation. These views may be generated by placing 6 landmarks: the left
and right ventricle apex... | computer science |
29,929 | An Iterative Co-Saliency Framework for RGBD Images | cs.CV | As a newly emerging and significant topic in computer vision community,
co-saliency detection aims at discovering the common salient objects in
multiple related images. The existing methods often generate the co-saliency
map through a direct forward pipeline which is based on the designed cues or
initialization, but la... | computer science |
29,930 | DDD17: End-To-End DAVIS Driving Dataset | cs.CV | Event cameras, such as dynamic vision sensors (DVS), and dynamic and
active-pixel vision sensors (DAVIS) can supplement other autonomous driving
sensors by providing a concurrent stream of standard active pixel sensor (APS)
images and DVS temporal contrast events. The APS stream is a sequence of
standard grayscale glob... | computer science |
29,931 | Attentional Pooling for Action Recognition | cs.CV | We introduce a simple yet surprisingly powerful model to incorporate
attention in action recognition and human object interaction tasks. Our
proposed attention module can be trained with or without extra supervision, and
gives a sizable boost in accuracy while keeping the network size and
computational cost nearly the ... | computer science |
29,932 | Object-Centric Photometric Bundle Adjustment with Deep Shape Prior | cs.CV | Reconstructing 3D shapes from a sequence of images has long been a problem of
interest in computer vision. Classical Structure from Motion (SfM) methods have
attempted to solve this problem through projected point displacement \& bundle
adjustment. More recently, deep methods have attempted to solve this problem by
dir... | computer science |
29,933 | Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D
Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted
Focal U-Net | cs.CV | Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently
navigated by 2D fluoroscopy which is insufficiently informative. Previously, a
semi-automatic 3D shape instantiation method was developed to instantiate the
3D shape of a main, deployed, and fenestrated stent graft from a single
fluoroscopy proj... | computer science |
29,934 | Registration and Fusion of Multi-Spectral Images Using a Novel Edge
Descriptor | cs.CV | In this paper we introduce a fully end-to-end approach for multi-spectral
image registration and fusion. Our method for fusion combines images from
different spectral channels into a single fused image by different approaches
for low and high frequency signals. A prerequisite of fusion is a stage of
geometric alignment... | computer science |
29,935 | The Local Dimension of Deep Manifold | cs.CV | Based on our observation that there exists a dramatic drop for the singular
values of the fully connected layers or a single feature map of the
convolutional layer, and that the dimension of the concatenated feature vector
almost equals the summation of the dimension on each feature map, we propose a
singular value dec... | computer science |
29,936 | Adversarial Dropout Regularization | cs.CV | We present a method for transferring neural representations from label-rich
source domains to unlabeled target domains. Recent adversarial methods proposed
for this task learn to align features across domains by fooling a special
domain critic network. However, a drawback of this approach is that the critic
simply labe... | computer science |
29,937 | Simultaneous Joint and Object Trajectory Templates for Human Activity
Recognition from 3-D Data | cs.CV | The availability of low-cost range sensors and the development of relatively
robust algorithms for the extraction of skeleton joint locations have inspired
many researchers to develop human activity recognition methods using the 3-D
data. In this paper, an effective method for the recognition of human
activities from t... | computer science |
29,938 | Spatial Pyramid Context-Aware Moving Object Detection and Tracking for
Full Motion Video and Wide Aerial Motion Imagery | cs.CV | A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collabor... | computer science |
29,939 | End-to-End Video Classification with Knowledge Graphs | cs.CV | Video understanding has attracted much research attention especially since
the recent availability of large-scale video benchmarks. In this paper, we
address the problem of multi-label video classification. We first observe that
there exists a significant knowledge gap between how machines and humans learn.
That is, wh... | computer science |
29,940 | Active Learning for Visual Question Answering: An Empirical Study | cs.CV | We present an empirical study of active learning for Visual Question
Answering, where a deep VQA model selects informative question-image pairs from
a pool and queries an oracle for answers to maximally improve its performance
under a limited query budget. Drawing analogies from human learning, we explore
cramming (ent... | computer science |
29,941 | HyperNetworks with statistical filtering for defending adversarial
examples | cs.CV | Deep learning algorithms have been known to be vulnerable to adversarial
perturbations in various tasks such as image classification. This problem was
addressed by employing several defense methods for detection and rejection of
particular types of attacks. However, training and manipulating networks
according to parti... | computer science |
29,942 | PersonRank: Detecting Important People in Images | cs.CV | Always, some individuals in images are more important/attractive than others
in some events such as presentation, basketball game or speech. However, it is
challenging to find important people among all individuals in images directly
based on their spatial or appearance information due to the existence of
diverse varia... | computer science |
29,943 | Mitigating Adversarial Effects Through Randomization | cs.CV | Convolutional neural networks have demonstrated high accuracy on various
tasks in recent years. However, they are extremely vulnerable to adversarial
examples. For example, imperceptible perturbations added to clean images can
cause convolutional neural networks to fail. In this paper, we propose to
utilize randomizati... | computer science |
29,944 | Artificial Generation of Big Data for Improving Image Classification: A
Generative Adversarial Network Approach on SAR Data | cs.CV | Very High Spatial Resolution (VHSR) large-scale SAR image databases are still
an unresolved issue in the Remote Sensing field. In this work, we propose such
a dataset and use it to explore patch-based classification in urban and
periurban areas, considering 7 distinct semantic classes. In this context, we
investigate t... | computer science |
29,945 | A Joint 3D-2D based Method for Free Space Detection on Roads | cs.CV | In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object ... | computer science |
29,946 | Image Segmentation of Multi-Shaped Overlapping Objects | cs.CV | In this work, we propose a new segmentation algorithm for images containing
convex objects present in multiple shapes with a high degree of overlap. The
proposed algorithm is carried out in two steps, first we identify the visible
contours, segment them using concave points and finally group the segments
belonging to t... | computer science |
29,947 | Challenges in Disentangling Independent Factors of Variation | cs.CV | We study the problem of building models that disentangle independent factors
of variation. Such models could be used to encode features that can efficiently
be used for classification and to transfer attributes between different images
in image synthesis. As data we use a weakly labeled training set. Our weak
labels in... | computer science |
29,948 | Doppler-Radar Based Hand Gesture Recognition System Using Convolutional
Neural Networks | cs.CV | Hand gesture recognition has long been a hot topic in human computer
interaction. Traditional camera-based hand gesture recognition systems cannot
work properly under dark circumstances. In this paper, a Doppler Radar based
hand gesture recognition system using convolutional neural networks is
proposed. A cost-effectiv... | computer science |
29,949 | GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep
Multitask Networks | cs.CV | Deep multitask networks, in which one neural network produces multiple
predictive outputs, are more scalable and often better regularized than their
single-task counterparts. Such advantages can potentially lead to gains in both
speed and performance, but multitask networks are also difficult to train
without finding t... | computer science |
29,950 | Can Maxout Units Downsize Restoration Networks? - Single Image
Super-Resolution Using Lightweight CNN with Maxout Units | cs.CV | Rectified linear units (ReLU) are well-known to be helpful in obtaining
faster convergence and thus higher performance for many deep-learning-based
applications. However, networks with ReLU tend to perform poorly when the
number of filter parameters is constrained to a small number. To overcome it,
in this paper, we pr... | computer science |
29,951 | An EEG-based Image Annotation System | cs.CV | The success of deep learning in computer vision has greatly increased the
need for annotated image datasets. We propose an EEG
(Electroencephalogram)-based image annotation system. While humans can
recognize objects in 20-200 milliseconds, the need to manually label images
results in a low annotation throughput. Our sy... | computer science |
29,952 | Unconstrained Scene Text and Video Text Recognition for Arabic Script | cs.CV | Building robust recognizers for Arabic has always been challenging. We
demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid
architecture in recognizing Arabic text in videos and natural scenes. We
outperform previous state-of-the-art on two publicly available video text
datasets - ALIF and ACTIV. For... | computer science |
29,953 | MSR-net:Low-light Image Enhancement Using Deep Convolutional Network | cs.CV | Images captured in low-light conditions usually suffer from very low
contrast, which increases the difficulty of subsequent computer vision tasks in
a great extent. In this paper, a low-light image enhancement model based on
convolutional neural network and Retinex theory is proposed. Firstly, we show
that multi-scale ... | computer science |
29,954 | Fine-tuning CNN Image Retrieval with No Human Annotation | cs.CV | Image descriptors based on activations of Convolutional Neural Networks
(CNNs) have become dominant in image retrieval due to their discriminative
power, compactness of the representation, and the efficiency of search.
Training of CNNs, either from scratch or fine-tuning, requires a large amount
of annotated data, wher... | computer science |
29,955 | Few-Shot Adversarial Domain Adaptation | cs.CV | This work provides a framework for addressing the problem of supervised
domain adaptation with deep models. The main idea is to exploit adversarial
learning to learn an embedded subspace that simultaneously maximizes the
confusion between two domains while semantically aligning their embedding. The
supervised setting b... | computer science |
29,956 | Remote Sensing Image Fusion Based on Two-stream Fusion Network | cs.CV | Remote sensing image fusion (also known as pan-sharpening) aims at generating
high resolution multi-spectral (MS) image from inputs of a high spatial
resolution single band panchromatic (PAN) image and a low spatial resolution
multi-spectral image. Inspired by the astounding achievements of convolutional
neural network... | computer science |
29,957 | Image Captioning and Classification of Dangerous Situations | cs.CV | Current robot platforms are being employed to collaborate with humans in a
wide range of domestic and industrial tasks. These environments require
autonomous systems that are able to classify and communicate anomalous
situations such as fires, injured persons, car accidents; or generally, any
potentially dangerous situ... | computer science |
29,958 | Compression-aware Training of Deep Networks | cs.CV | In recent years, great progress has been made in a variety of application
domains thanks to the development of increasingly deeper neural networks.
Unfortunately, the huge number of units of these networks makes them expensive
both computationally and memory-wise. To overcome this, exploiting the fact
that deep network... | computer science |
29,959 | Latent hypernet: Exploring all Layers from Convolutional Neural Networks | cs.CV | Since Convolutional Neural Networks (ConvNets) are able to simultaneously
learn features and classifiers to discriminate different categories of
activities, recent works have employed ConvNets approaches to perform human
activity recognition (HAR) based on wearable sensors, allowing the removal of
expensive human work ... | computer science |
29,960 | Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and
Its Application to Exploring Cultural Heritage Objects | cs.CV | Motivated by the important archaeological application of exploring cultural
heritage objects, in this paper we study the challenging problem of
automatically segmenting curve structures that are very weakly stamped or
carved on an object surface in the form of a highly noisy depth map. Different
from most classical low... | computer science |
29,961 | A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten
Chinese Character Recognition | cs.CV | The recurrent neural network (RNN) is appropriate for dealing with temporal
sequences. In this paper, we present a deep RNN with new features and apply it
for online handwritten Chinese character recognition. Compared with the
existing RNN models, three innovations are involved in the proposed system.
First, a new hidd... | computer science |
29,962 | Multi-label Image Recognition by Recurrently Discovering Attentional
Regions | cs.CV | This paper proposes a novel deep architecture to address multi-label image
recognition, a fundamental and practical task towards general visual
understanding. Current solutions for this task usually rely on an extra step of
extracting hypothesis regions (i.e., region proposals), resulting in redundant
computation and s... | computer science |
29,963 | Heuristic Search for Structural Constraints in Data Association | cs.CV | The research on multi-object tracking (MOT) is essentially to solve for the
data association assignment, the core of which is to design the association
cost as discriminative as possible. Generally speaking, the match ambiguities
caused by similar appearances of objects and the moving cameras make the data
association ... | computer science |
29,964 | Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining | cs.CV | Zero-shot Hashing (ZSH) is to learn hashing models for novel/target classes
without training data, which is an important and challenging problem. Most
existing ZSH approaches exploit transfer learning via an intermediate shared
semantic representations between the seen/source classes and novel/target
classes. However, ... | computer science |
29,965 | Offline signature authenticity verification through unambiguously
connected skeleton segments | cs.CV | A method for offline signature verification is presented in this paper. It is
based on the segmentation of the signature skeleton (through standard image
skeletonization) into unambiguous sequences of points, or unambiguously
connected skeleton segments corresponding to vectorial representations of
signature portions. ... | computer science |
29,966 | Curve Reconstruction via the Global Statistics of Natural Curves | cs.CV | Reconstructing the missing parts of a curve has been the subject of much
computational research, with applications in image inpainting, object
synthesis, etc. Different approaches for solving that problem are typically
based on processes that seek visually pleasing or perceptually plausible
completions. In this work we... | computer science |
29,967 | Multi-stage Suture Detection for Robot Assisted Anastomosis based on
Deep Learning | cs.CV | In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and vary... | computer science |
29,968 | CyCADA: Cycle-Consistent Adversarial Domain Adaptation | cs.CV | Domain adaptation is critical for success in new, unseen environments.
Adversarial adaptation models applied in feature spaces discover domain
invariant representations, but are difficult to visualize and sometimes fail to
capture pixel-level and low-level domain shifts. Recent work has shown that
generative adversaria... | computer science |
29,969 | Fingerprint Orientation Refinement through Iterative Smoothing | cs.CV | We propose a new gradient-based method for the extraction of the orientation
field associated to a fingerprint, and a regularisation procedure to improve
the orientation field computed from noisy fingerprint images. The
regularisation algorithm is based on three new integral operators, introduced
and discussed in this ... | computer science |
29,970 | Predicting Scene Parsing and Motion Dynamics in the Future | cs.CV | The ability of predicting the future is important for intelligent systems,
e.g. autonomous vehicles and robots to plan early and make decisions
accordingly. Future scene parsing and optical flow estimation are two key tasks
that help agents better understand their environments as the former provides
dense semantic info... | computer science |
29,971 | Two-stream Collaborative Learning with Spatial-Temporal Attention for
Video Classification | cs.CV | Video classification is highly important with wide applications, such as
video search and intelligent surveillance. Video naturally consists of static
and motion information, which can be represented by frame and optical flow.
Recently, researchers generally adopt the deep networks to capture the static
and motion info... | computer science |
29,972 | Feed Forward and Backward Run in Deep Convolution Neural Network | cs.CV | Convolution Neural Networks (CNN), known as ConvNets are widely used in many
visual imagery application, object classification, speech recognition. After
the implementation and demonstration of the deep convolution neural network in
Imagenet classification in 2012 by krizhevsky, the architecture of deep
Convolution Neu... | computer science |
29,973 | Fast camera focus estimation for gaze-based focus control | cs.CV | Many cameras implement auto-focus functionality. However, they typically
require the user to manually identify the location to be focused on. While such
an approach works for temporally-sparse autofocusing functionality (e.g., photo
shooting), it presents extreme usability problems when the focus must be
quickly switch... | computer science |
29,974 | Frangi-Net: A Neural Network Approach to Vessel Segmentation | cs.CV | In this paper, we reformulate the conventional 2-D Frangi vesselness measure
into a pre-weighted neural network ("Frangi-Net"), and illustrate that the
Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show
that, as a neural network, Frangi-Net is trainable. We evaluate the proposed
method on a se... | computer science |
29,975 | One-pass Person Re-identification by Sketch Online Discriminant Analysis | cs.CV | Person re-identification (re-id) is to match people across disjoint camera
views in a multi-camera system, and re-id has been an important technology
applied in smart city in recent years. However, the majority of existing person
re-id methods are not designed for processing sequential data in an online way.
This ignor... | computer science |
29,976 | Making a long story short: A Multi-Importance fast-forwarding egocentric
videos with the emphasis on relevant objects | cs.CV | The emergence of low-cost high-quality personal wearable cameras combined
with the increasing storage capacity of video-sharing websites have evoked a
growing interest in first-person videos, since most videos are composed of
long-running unedited streams which are usually tedious and unpleasant to
watch. State-of-the-... | computer science |
29,977 | Toward Depth Estimation Using Mask-Based Lensless Cameras | cs.CV | Recently, coded masks have been used to demonstrate a thin form-factor
lensless camera, FlatCam, in which a mask is placed immediately on top of a
bare image sensor. In this paper, we present an imaging model and algorithm to
jointly estimate depth and intensity information in the scene from a single or
multiple FlatCa... | computer science |
29,978 | Exploiting ConvNet Diversity for Flooding Identification | cs.CV | Flooding is the world's most costly type of natural disaster in terms of both
economic losses and human causalities. A first and essential procedure towards
flood monitoring is based on identifying the area most vulnerable to flooding,
which gives authorities relevant regions to focus. In this work, we propose
several ... | computer science |
29,979 | Unsupervised Learning of Geometry with Edge-aware Depth-Normal
Consistency | cs.CV | Learning to reconstruct depths in a single image by watching unlabeled videos
via deep convolutional network (DCN) is attracting significant attention in
recent years. In this paper, we introduce a surface normal representation for
unsupervised depth estimation framework. Our estimated depths are constrained
to be comp... | computer science |
29,980 | Egocentric Hand Detection Via Dynamic Region Growing | cs.CV | Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interact... | computer science |
29,981 | A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation | cs.CV | A domain adaptation method for urban scene segmentation is proposed in this
work. We develop a fully convolutional tri-branch network, where two branches
assign pseudo labels to images in the unlabeled target domain while the third
branch is trained with supervision based on images in the pseudo-labeled target
domain. ... | computer science |
29,982 | Material Classification in the Wild: Do Synthesized Training Data
Generalise Better than Real-World Training Data? | cs.CV | We question the dominant role of real-world training images in the field of
material classification by investigating whether synthesized data can
generalise more effectively than real-world data. Experimental results on three
challenging real-world material databases show that the best performing
pre-trained convolutio... | computer science |
29,983 | Longitudinal Study of Child Face Recognition | cs.CV | We present a longitudinal study of face recognition performance on Children
Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects,
in the age group [2, 18] years. Each subject has at least four face images
acquired over a time span of up to six years. Face comparison scores are
obtained from (i) ... | computer science |
29,984 | DeepKSPD: Learning Kernel-matrix-based SPD Representation for
Fine-grained Image Recognition | cs.CV | Being symmetric positive-definite (SPD), covariance matrix has traditionally
been used to represent a set of local descriptors in visual recognition. Recent
study shows that kernel matrix can give considerably better representation by
modelling the nonlinearity in the local descriptor set. Nevertheless, neither
the des... | computer science |
29,985 | CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks
for Image Super Resolution | cs.CV | We propose methodologies to train highly accurate and efficient deep
convolutional neural networks (CNNs) for image super resolution (SR). A cascade
training approach to deep learning is proposed to improve the accuracy of the
neural networks while gradually increasing the number of network layers. Next,
we explore how... | computer science |
29,986 | Going Further with Point Pair Features | cs.CV | Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our imp... | computer science |
29,987 | Deep Residual Text Detection Network for Scene Text | cs.CV | Scene text detection is a challenging problem in computer vision. In this
paper, we propose a novel text detection network based on prevalent object
detection frameworks. In order to obtain stronger semantic feature, we adopt
ResNet as feature extraction layers and exploit multi-level feature by
combining hierarchical ... | computer science |
29,988 | End-to-end Video-level Representation Learning for Action Recognition | cs.CV | From the frame/clip-level feature learning to the video-level representation
building, deep learning methods in action recognition have developed rapidly in
recent years. However, current methods suffer from the confusion caused by
partial observation training, or without end-to-end learning, or restricted to
single te... | computer science |
29,989 | 3D Randomized Connection Network with Graph-based Label Inference | cs.CV | In this paper, a novel 3D deep learning network is proposed for brain MR
image segmentation with randomized connection, which can decrease the
dependency between layers and increase the network capacity. The convolutional
LSTM and 3D convolution are employed as network units to capture the long-term
and short-term 3D p... | computer science |
29,990 | Latent Constrained Correlation Filter | cs.CV | Correlation filters are special classifiers designed for shift-invariant
object recognition, which are robust to pattern distortions. The recent
literature shows that combining a set of sub-filters trained based on a single
or a small group of images obtains the best performance. The idea is equivalent
to estimating va... | computer science |
29,991 | AON: Towards Arbitrarily-Oriented Text Recognition | cs.CV | Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular ... | computer science |
29,992 | Robust Image Registration via Empirical Mode Decomposition | cs.CV | Spatially varying intensity noise is a common source of distortion in images.
Bias field noise is one example of such distortion that is often present in the
magnetic resonance (MR) images. In this paper, we first show that empirical
mode decomposition (EMD) can considerably reduce the bias field noise in the MR
images... | computer science |
29,993 | Feature Enhancement Network: A Refined Scene Text Detector | cs.CV | In this paper, we propose a refined scene text detector with a \textit{novel}
Feature Enhancement Network (FEN) for Region Proposal and Text Detection
Refinement. Retrospectively, both region proposal with \textit{only} $3\times
3$ sliding-window feature and text detection refinement with \textit{single
scale} high lev... | computer science |
29,994 | Evaluation of trackers for Pan-Tilt-Zoom Scenarios | cs.CV | Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. Compared to tracking with a still camera, the
images captured with a PTZ camera are highly dynamic in nature because the
camera can perform large motion resulting in quickly changing capture
conditions. Furthermore, ... | computer science |
29,995 | Hand Gesture Recognition with Leap Motion | cs.CV | The recent introduction of depth cameras like Leap Motion Controller allows
researchers to exploit the depth information to recognize hand gesture more
robustly. This paper proposes a novel hand gesture recognition system with Leap
Motion Controller. A series of features are extracted from Leap Motion tracking
data, we... | computer science |
29,996 | Gender recognition and biometric identification using a large dataset of
hand images | cs.CV | The human hand possesses distinctive features which can reveal gender
information. In addition, the hand is considered one of the primary biometric
traits used to identify a person. In this work, we propose a large dataset of
human hand images with detailed ground-truth information for gender recognition
and biometric ... | computer science |
29,997 | Crowd counting via scale-adaptive convolutional neural network | cs.CV | The task of crowd counting is to automatically estimate the pedestrian number
in crowd images. To cope with the scale and perspective changes that commonly
exist in crowd images, state-of-the-art approaches employ multi-column CNN
architectures to regress density maps of crowd images. Multiple columns have
different re... | computer science |
29,998 | All-Transfer Learning for Deep Neural Networks and its Application to
Sepsis Classification | cs.CV | In this article, we propose a transfer learning method for deep neural
networks (DNNs). Deep learning has been widely used in many applications.
However, applying deep learning is problematic when a large amount of training
data are not available. One of the conventional methods for solving this
problem is transfer lea... | computer science |
29,999 | Visual Concepts and Compositional Voting | cs.CV | It is very attractive to formulate vision in terms of pattern theory
\cite{Mumford2010pattern}, where patterns are defined hierarchically by
compositions of elementary building blocks. But applying pattern theory to real
world images is currently less successful than discriminative methods such as
deep networks. Deep n... | computer science |
30,000 | An Automatic Diagnosis Method of Facial Acne Vulgaris Based on
Convolutional Neural Network | cs.CV | In this paper, we present a new automatic diagnosis method of facial acne
vulgaris based on convolutional neural network. This method is proposed to
overcome the shortcoming of classification types in previous methods. The core
of our method is to extract features of images based on convolutional neural
network and ach... | computer science |
30,001 | Conditional Random Field and Deep Feature Learning for Hyperspectral
Image Segmentation | cs.CV | Image segmentation is considered to be one of the critical tasks in
hyperspectral remote sensing image processing. Recently, convolutional neural
network (CNN) has established itself as a powerful model in segmentation and
classification by demonstrating excellent performances. The use of a graphical
model such as a co... | computer science |
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