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28,002 | Deep Spatio-temporal Manifold Network for Action Recognition | cs.CV | Visual data such as videos are often sampled from complex manifold. We
propose leveraging the manifold structure to constrain the deep action feature
learning, thereby minimizing the intra-class variations in the feature space
and alleviating the over-fitting problem. Considering that manifold can be
transferred, layer... | computer science |
28,003 | Contour Detection from Deep Patch-level Boundary Prediction | cs.CV | In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. W... | computer science |
28,004 | Efficient Structure from Motion for Oblique UAV Images Based on Maximal
Spanning Tree Expansions | cs.CV | The primary contribution of this paper is an efficient Structure from Motion
(SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an
algorithm, considering spatial relationship constrains between image
footprints, is designed for match pair selection with assistant of UAV flight
control data and obli... | computer science |
28,005 | Convolutional Dictionary Learning via Local Processing | cs.CV | Convolutional Sparse Coding (CSC) is an increasingly popular model in the
signal and image processing communities, tackling some of the limitations of
traditional patch-based sparse representations. Although several works have
addressed the dictionary learning problem under this model, these relied on an
ADMM formulati... | computer science |
28,006 | Diving Performance Assessment by means of Video Processing | cs.CV | The aim of this paper is to present a procedure for video analysis applied in
an innovative way to diving performance assessment. Sport performance analysis
is a trend that is growing exponentially for all level athletes. The technique
here shown is based on two important requirements: flexibility and low cost.
These t... | computer science |
28,007 | Large-scale, Fast and Accurate Shot Boundary Detection through
Spatio-temporal Convolutional Neural Networks | cs.CV | Shot boundary detection (SBD) is an important pre-processing step for video
manipulation. Here, each segment of frames is classified as either sharp,
gradual or no transition. Current SBD techniques analyze hand-crafted features
and attempt to optimize both detection accuracy and processing speed. However,
the heavy co... | computer science |
28,008 | READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in
Archival Documents | cs.CV | Text line detection is crucial for any application associated with Automatic
Text Recognition or Keyword Spotting. Modern algorithms perform good on
well-established datasets since they either comprise clean data or
simple/homogeneous page layouts. We have collected and annotated 2036 archival
document images from diff... | computer science |
28,009 | Deep Person Re-Identification with Improved Embedding and Efficient
Training | cs.CV | Person re-identification task has been greatly boosted by deep convolutional
neural networks (CNNs) in recent years. The core of which is to enlarge the
inter-class distinction as well as reduce the intra-class variance. However, to
achieve this, existing deep models prefer to adopt image pairs or triplets to
form veri... | computer science |
28,010 | Model Complexity-Accuracy Trade-off for a Convolutional Neural Network | cs.CV | Convolutional Neural Networks(CNN) has had a great success in the recent
past, because of the advent of faster GPUs and memory access. CNNs are really
powerful as they learn the features from data in layers such that they exhibit
the structure of the V-1 features of the human brain. A huge bottleneck, in
this case, is ... | computer science |
28,011 | Adaptive Regularization of Some Inverse Problems in Image Analysis | cs.CV | We present an adaptive regularization scheme for optimizing composite energy
functionals arising in image analysis problems. The scheme automatically trades
off data fidelity and regularization depending on the current data fit during
the iterative optimization, so that regularization is strongest initially, and
wanes ... | computer science |
28,012 | Skin lesion detection based on an ensemble of deep convolutional neural
network | cs.CV | Skin cancer is a major public health problem, with over 5 million newly
diagnosed cases in the United States each year. Melanoma is the deadliest form
of skin cancer, responsible for over 9,000 deaths each year. In this paper, we
propose an ensemble of deep convolutional neural networks to classify
dermoscopy images in... | computer science |
28,013 | Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation | cs.CV | We address the problem of localisation of objects as bounding boxes in images
with weak labels. This weakly supervised object localisation problem has been
tackled in the past using discriminative models where each object class is
localised independently from other classes. We propose a novel framework based
on Bayesia... | computer science |
28,014 | Cell Tracking via Proposal Generation and Selection | cs.CV | Microscopy imaging plays a vital role in understanding many biological
processes in development and disease. The recent advances in automation of
microscopes and development of methods and markers for live cell imaging has
led to rapid growth in the amount of image data being captured. To efficiently
and reliably extra... | computer science |
28,015 | Deep Projective 3D Semantic Segmentation | cs.CV | Semantic segmentation of 3D point clouds is a challenging problem with
numerous real-world applications. While deep learning has revolutionized the
field of image semantic segmentation, its impact on point cloud data has been
limited so far. Recent attempts, based on 3D deep learning approaches
(3D-CNNs), have achieved... | computer science |
28,016 | Multi-Scale Spatially Weighted Local Histograms in O(1) | cs.CV | Weighting pixel contribution considering its location is a key feature in
many fundamental image processing tasks including filtering, object modeling
and distance matching. Several techniques have been proposed that incorporate
Spatial information to increase the accuracy and boost the performance of
detection, tracki... | computer science |
28,017 | Learning RGB-D Salient Object Detection using background enclosure,
depth contrast, and top-down features | cs.CV | Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong
performance on RGB salient object detection. Although, depth information can
help improve detection results, the exploration of CNNs for RGB-D salient
object detection remains limited. Here we propose a novel deep CNN architecture
for RGB-D sal... | computer science |
28,018 | 4d isip: 4d implicit surface interest point detection | cs.CV | In this paper, we propose a new method to detect 4D spatiotemporal interest
points though an implicit surface, we refer to as the 4D-ISIP. We use a 3D
volume which has a truncated signed distance function(TSDF) for every voxel to
represent our 3D object model. The TSDF represents the distance between the
spatial points... | computer science |
28,019 | Context-aware stacked convolutional neural networks for classification
of breast carcinomas in whole-slide histopathology images | cs.CV | Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in s... | computer science |
28,020 | Efficient and Scalable View Generation from a Single Image using Fully
Convolutional Networks | cs.CV | Single-image-based view generation (SIVG) is important for producing 3D
stereoscopic content. Here, handling different spatial resolutions as input and
optimizing both reconstruction accuracy and processing speed is desirable.
Latest approaches are based on convolutional neural network (CNN), and they
generate promisin... | computer science |
28,021 | Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully
Convolutional Networks | cs.CV | A major challenge in brain tumor treatment planning and quantitative
evaluation is determination of the tumor extent. The noninvasive magnetic
resonance imaging (MRI) technique has emerged as a front-line diagnostic tool
for brain tumors without ionizing radiation. Manual segmentation of brain tumor
extent from 3D MRI ... | computer science |
28,022 | Predicting the Driver's Focus of Attention: the DR(eye)VE Project | cs.CV | In this work we aim to predict the driver's focus of attention. The goal is
to estimate what a person would pay attention to while driving, and which part
of the scene around the vehicle is more critical for the task. To this end we
propose a new computer vision model based on a multi-branch deep architecture
that inte... | computer science |
28,023 | An Improved Video Analysis using Context based Extension of LSH | cs.CV | Locality Sensitive Hashing (LSH) based algorithms have already shown their
promise in finding approximate nearest neighbors in high dimen- sional data
space. However, there are certain scenarios, as in sequential data, where the
proximity of a pair of points cannot be captured without considering their
surroundings or ... | computer science |
28,024 | Learning 3D Object Categories by Looking Around Them | cs.CV | Traditional approaches for learning 3D object categories use either synthetic
data or manual supervision. In this paper, we propose a method which does not
require manual annotations and is instead cued by observing objects from a
moving vantage point. Our system builds on two innovations: a Siamese viewpoint
factoriza... | computer science |
28,025 | Distribution of degrees of freedom over structure and motion of rigid
bodies | cs.CV | This paper is concerned with recovery of motion and structure parameters from
multiframes under orthogonal projection when only points are traced. The main
question is how many points and/or how many frames are necessary for the task.
It is demonstrated that 3 frames and 3 points are the absolute minimum.
Closed-form s... | computer science |
28,026 | SCNet: Learning Semantic Correspondence | cs.CV | This paper addresses the problem of establishing semantic correspondences
between images depicting different instances of the same object or scene
category. Previous approaches focus on either combining a spatial regularizer
with hand-crafted features, or learning a correspondence model for appearance
only. We propose ... | computer science |
28,027 | Neural Style Transfer: A Review | cs.CV | The recent work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNN) in creating artistic fantastic imagery by separating and
recombing the image content and style. This process of using CNN to migrate the
semantic content of one image to different styles is referred to as Neural
Style Transfer... | computer science |
28,028 | A Generative Model of People in Clothing | cs.CV | We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the h... | computer science |
28,029 | Obstacle Avoidance Using Stereo Camera | cs.CV | In this paper we present a novel method for obstacle avoidance using the
stereo camera. The conventional obstacle avoidance methods and their
limitations are discussed. A new algorithm is developed for the real-time
obstacle avoidance which responds faster to unexpected obstacles. In this
approach the depth map is divi... | computer science |
28,030 | Probabilistic Image Colorization | cs.CV | We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale
image and... | computer science |
28,031 | Improved underwater image enhancement algorithms based on partial
differential equations (PDEs) | cs.CV | The experimental results of improved underwater image enhancement algorithms
based on partial differential equations (PDEs) are presented in this report.
This second work extends the study of previous work and incorporating several
improvements into the revised algorithm. Experiments show the evidence of the
improvemen... | computer science |
28,032 | SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering | cs.CV | Multiple scattering of an electromagnetic wave as it passes through an object
is a fundamental problem that limits the performance of current imaging
systems. In this paper, we describe a new technique-called Series Expansion
with Accelerated Gradient Descent on Lippmann-Schwinger Equation (SEAGLE)-for
robust imaging u... | computer science |
28,033 | Challenges in Monocular Visual Odometry: Photometric Calibration, Motion
Bias and Rolling Shutter Effect | cs.CV | Monocular visual odometry (VO) has seen tremendous improvements in accuracy,
robustness and efficiency, and has gained exponential popularity over recent
years. Nevertheless, no comprehensive evaluations have been performed to reveal
the influences of the three easily overlooked, yet very influential aspects:
photometr... | computer science |
28,034 | A Feature Embedding Strategy for High-level CNN representations from
Multiple ConvNets | cs.CV | Following the rapidly growing digital image usage, automatic image
categorization has become preeminent research area. It has broaden and adopted
many algorithms from time to time, whereby multi-feature (generally,
hand-engineered features) based image characterization comes handy to improve
accuracy. Recently, in mach... | computer science |
28,035 | An Optimal Dimensionality Multi-shell Sampling Scheme with Accurate and
Efficient Transforms for Diffusion MRI | cs.CV | This paper proposes a multi-shell sampling scheme and corresponding
transforms for the accurate reconstruction of the diffusion signal in diffusion
MRI by expansion in the spherical polar Fourier (SPF) basis. The sampling
scheme uses an optimal number of samples, equal to the degrees of freedom of
the band-limited diff... | computer science |
28,036 | Reconfiguring the Imaging Pipeline for Computer Vision | cs.CV | Advancements in deep learning have ignited an explosion of research on
efficient hardware for embedded computer vision. Hardware vision acceleration,
however, does not address the cost of capturing and processing the image data
that feeds these algorithms. We examine the role of the image signal processing
(ISP) pipeli... | computer science |
28,037 | Object-Level Context Modeling For Scene Classification with Context-CNN | cs.CV | Convolutional Neural Networks (CNNs) have been used extensively for computer
vision tasks and produce rich feature representation for objects or parts of an
image. But reasoning about scenes requires integration between the low-level
feature representations and the high-level semantic information. We propose a
deep net... | computer science |
28,038 | Transfer Learning for Cross-Dataset Recognition: A Survey | cs.CV | This paper summarises and analyses the cross-dataset recognition transfer
learning techniques with the emphasis on what kinds of methods can be used when
the available source and target data are presented in different forms for
boosting the target task. This paper for the first time summarises several
transferring crit... | computer science |
28,039 | Negative Results in Computer Vision: A Perspective | cs.CV | A negative result is when the outcome of an experiment or a model is not what
is expected or when a hypothesis does not hold. Despite being often overlooked
in the scientific community, negative results are results and they carry value.
While this topic has been extensively discussed in other fields such as social
scie... | computer science |
28,040 | View-Invariant Template Matching Using Homography Constraints | cs.CV | Change in viewpoint is one of the major factors for variation in object
appearance across different images. Thus, view-invariant object recognition is
a challenging and important image understanding task. In this paper, we propose
a method that can match objects in images taken under different viewpoints.
Unlike most m... | computer science |
28,041 | Adaptive Feature Representation for Visual Tracking | cs.CV | Robust feature representation plays significant role in visual tracking.
However, it remains a challenging issue, since many factors may affect the
experimental performance. The existing method which combine different features
by setting them equally with the fixed weight could hardly solve the issues,
due to the diffe... | computer science |
28,042 | Using Satellite Imagery for Good: Detecting Communities in Desert and
Mapping Vaccination Activities | cs.CV | Deep convolutional neural networks (CNNs) have outperformed existing object
recognition and detection algorithms. On the other hand satellite imagery
captures scenes that are diverse. This paper describes a deep learning approach
that analyzes a geo referenced satellite image and efficiently detects built
structures in... | computer science |
28,043 | Learning to Refine Object Contours with a Top-Down Fully Convolutional
Encoder-Decoder Network | cs.CV | We develop a novel deep contour detection algorithm with a top-down fully
convolutional encoder-decoder network. Our proposed method, named TD-CEDN,
solves two important issues in this low-level vision problem: (1) learning
multi-scale and multi-level features; and (2) applying an effective top-down
refined approach in... | computer science |
28,044 | TraX: The visual Tracking eXchange Protocol and Library | cs.CV | In this paper we address the problem of developing on-line visual tracking
algorithms. We present a specialized communication protocol that serves as a
bridge between a tracker implementation and utilizing application. It decouples
development of algorithms and application, encouraging re-usability. The
primary use cas... | computer science |
28,045 | External Prior Guided Internal Prior Learning for Real Noisy Image
Denoising | cs.CV | Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corr... | computer science |
28,046 | Spatial-Temporal Recurrent Neural Network for Emotion Recognition | cs.CV | Emotion analysis is a crucial problem to endow artifact machines with real
intelligence in many large potential applications. As external appearances of
human emotions, electroencephalogram (EEG) signals and video face signals are
widely used to track and analyze human's affective information. According to
their common... | computer science |
28,047 | Detection of irregular QRS complexes using Hermite Transform and Support
Vector Machine | cs.CV | Computer based recognition and detection of abnormalities in ECG signals is
proposed. For this purpose, the Support Vector Machines (SVM) are combined with
the advantages of Hermite transform representation. SVM represent a special
type of classification techniques commonly used in medical applications.
Automatic class... | computer science |
28,048 | Self-Committee Approach for Image Restoration Problems using
Convolutional Neural Network | cs.CV | There have been many discriminative learning methods using convolutional
neural networks (CNN) for several image restoration problems, which learn the
mapping function from a degraded input to the clean output. In this letter, we
propose a self-committee method that can find enhanced restoration results from
the multip... | computer science |
28,049 | Towards a Principled Integration of Multi-Camera Re-Identification and
Tracking through Optimal Bayes Filters | cs.CV | With the rise of end-to-end learning through deep learning, person detectors
and re-identification (ReID) models have recently become very strong.
Multi-camera multi-target (MCMT) tracking has not fully gone through this
transformation yet. We intend to take another step in this direction by
presenting a theoretically ... | computer science |
28,050 | Single Image Action Recognition by Predicting Space-Time Saliency | cs.CV | We propose a novel approach based on deep Convolutional Neural Networks (CNN)
to recognize human actions in still images by predicting the future motion, and
detecting the shape and location of the salient parts of the image. We make the
following major contributions to this important area of research: (i) We use
the p... | computer science |
28,051 | Combination of Hidden Markov Random Field and Conjugate Gradient for
Brain Image Segmentation | cs.CV | Image segmentation is the process of partitioning the image into significant
regions easier to analyze. Nowadays, segmentation has become a necessity in
many practical medical imaging methods as locating tumors and diseases. Hidden
Markov Random Field model is one of several techniques used in image
segmentation. It pr... | computer science |
28,052 | Deep neural networks on graph signals for brain imaging analysis | cs.CV | Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal
data often degraded by complex, non-Gaussian noise. For reliable analysis of
brain imaging data, it is important to extract discriminative, low-dimensional
intrinsic representation of the recorded data. This work proposes a new method
to learn th... | computer science |
28,053 | Revisiting IM2GPS in the Deep Learning Era | cs.CV | Image geolocalization, inferring the geographic location of an image, is a
challenging computer vision problem with many potential applications. The
recent state-of-the-art approach to this problem is a deep image classification
approach in which the world is spatially divided into cells and a deep network
is trained t... | computer science |
28,054 | Spatial-Temporal Union of Subspaces for Multi-body Non-rigid
Structure-from-Motion | cs.CV | Non-rigid structure-from-motion (NRSfM) has so far been mostly studied for
recovering 3D structure of a single non-rigid/deforming object. To handle the
real world challenging multiple deforming objects scenarios, existing methods
either pre-segment different objects in the scene or treat multiple non-rigid
objects as ... | computer science |
28,055 | Discovery and visualization of structural biomarkers from MRI using
transport-based morphometry | cs.CV | Disease in the brain is often associated with subtle, spatially diffuse, or
complex tissue changes that may lie beneath the level of gross visual
inspection, even on magnetic resonance imaging (MRI). Unfortunately, current
computer-assisted approaches that examine pre-specified features, whether
anatomically-defined (i... | computer science |
28,056 | Gland Segmentation in Histopathology Images Using Random Forest Guided
Boundary Construction | cs.CV | Grading of cancer is important to know the extent of its spread. Prior to
grading, segmentation of glandular structures is important. Manual segmentation
is a time consuming process and is subject to observer bias. Hence, an
automated process is required to segment the gland structures. These glands
show a large variat... | computer science |
28,057 | A Closed-Form Model for Image-Based Distant Lighting | cs.CV | In this paper, we present a new mathematical foundation for image-based
lighting. Using a simple manipulation of the local coordinate system, we derive
a closed-form solution to the light integral equation under distant environment
illumination. We derive our solution for different BRDF's such as lambertian
and Phong-l... | computer science |
28,058 | Machine learning methods for multimedia information retrieval | cs.CV | In this thesis we examined several multimodal feature extraction and learning
methods for retrieval and classification purposes. We reread briefly some
theoretical results of learning in Section 2 and reviewed several generative
and discriminative models in Section 3 while we described the similarity kernel
in Section ... | computer science |
28,059 | Single Image Super-Resolution Using Multi-Scale Convolutional Neural
Network | cs.CV | Methods based on convolutional neural network (CNN) have demonstrated
tremendous improvements on single image super-resolution. However, the previous
methods mainly restore images from one single area in the low resolution (LR)
input, which limits the flexibility of models to infer various scales of
details for high re... | computer science |
28,060 | Learning Semantics for Image Annotation | cs.CV | Image search and retrieval engines rely heavily on textual annotation in
order to match word queries to a set of candidate images. A system that can
automatically annotate images with meaningful text can be highly beneficial for
such engines. Currently, the approaches to develop such systems try to
establish relationsh... | computer science |
28,061 | A Perceptually Weighted Rank Correlation Indicator for Objective Image
Quality Assessment | cs.CV | In the field of objective image quality assessment (IQA), the Spearman's
$\rho$ and Kendall's $\tau$ are two most popular rank correlation indicators,
which straightforwardly assign uniform weight to all quality levels and assume
each pair of images are sortable. They are successful for measuring the average
accuracy o... | computer science |
28,062 | Design of a Very Compact CNN Classifier for Online Handwritten Chinese
Character Recognition Using DropWeight and Global Pooling | cs.CV | Currently, owing to the ubiquity of mobile devices, online handwritten
Chinese character recognition (HCCR) has become one of the suitable choice for
feeding input to cell phones and tablet devices. Over the past few years,
larger and deeper convolutional neural networks (CNNs) have extensively been
employed for improv... | computer science |
28,063 | View-invariant Gait Recognition through Genetic Template Segmentation | cs.CV | Template-based model-free approach provides by far the most successful
solution to the gait recognition problem in literature. Recent work discusses
how isolating the head and leg portion of the template increase the performance
of a gait recognition system making it robust against covariates like clothing
and carrying... | computer science |
28,064 | Back to RGB: 3D tracking of hands and hand-object interactions based on
short-baseline stereo | cs.CV | We present a novel solution to the problem of 3D tracking of the articulated
motion of human hand(s), possibly in interaction with other objects. The vast
majority of contemporary relevant work capitalizes on depth information
provided by RGBD cameras. In this work, we show that accurate and efficient 3D
hand tracking ... | computer science |
28,065 | A Deep Learning Based 6 Degree-of-Freedom Localization Method for
Endoscopic Capsule Robots | cs.CV | We present a robust deep learning based 6 degrees-of-freedom (DoF)
localization system for endoscopic capsule robots. Our system mainly focuses on
localization of endoscopic capsule robots inside the GI tract using only visual
information captured by a mono camera integrated to the robot. The proposed
system is a 23-la... | computer science |
28,066 | A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic
Capsule Robots | cs.CV | In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups ... | computer science |
28,067 | Handwritten Urdu Character Recognition using 1-Dimensional BLSTM
Classifier | cs.CV | The recognition of cursive script is regarded as a subtle task in optical
character recognition due to its varied representation. Every cursive script
has different nature and associated challenges. As Urdu is one of cursive
language that is derived from Arabic script, thats why it nearly shares the
same challenges and... | computer science |
28,068 | WordFence: Text Detection in Natural Images with Border Awareness | cs.CV | In recent years, text recognition has achieved remarkable success in
recognizing scanned document text. However, word recognition in natural images
is still an open problem, which generally requires time consuming
post-processing steps. We present a novel architecture for individual word
detection in scene images based... | computer science |
28,069 | Joint Geometrical and Statistical Alignment for Visual Domain Adaptation | cs.CV | This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled project... | computer science |
28,070 | Cooperative Learning with Visual Attributes | cs.CV | Learning paradigms involving varying levels of supervision have received a
lot of interest within the computer vision and machine learning communities.
The supervisory information is typically considered to come from a human
supervisor -- a "teacher" figure. In this paper, we consider an alternate
source of supervision... | computer science |
28,071 | Intel RealSense Stereoscopic Depth Cameras | cs.CV | We present a comprehensive overview of the stereoscopic Intel RealSense RGBD
imaging systems. We discuss these systems' mode-of-operation, functional
behavior and include models of their expected performance, shortcomings, and
limitations. We provide information about the systems' optical characteristics,
their correla... | computer science |
28,072 | IAN: The Individual Aggregation Network for Person Search | cs.CV | Person search in real-world scenarios is a new challenging computer version
task with many meaningful applications. The challenge of this task mainly comes
from: (1) unavailable bounding boxes for pedestrians and the model needs to
search for the person over the whole gallery images; (2) huge variance of
visual appeara... | computer science |
28,073 | Research on Bi-mode Biometrics Based on Deep Learning | cs.CV | In view of the fact that biological characteristics have excellent
independent distinguishing characteristics,biometric identification technology
involves almost all the relevant areas of human distinction. Fingerprints,
iris, face, voice-print and other biological features have been widely used in
the public security ... | computer science |
28,074 | WebVision Challenge: Visual Learning and Understanding With Web Data | cs.CV | We present the 2017 WebVision Challenge, a public image recognition challenge
designed for deep learning based on web images without instance-level human
annotation. Following the spirit of previous vision challenges, such as ILSVRC,
Places2 and PASCAL VOC, which have played critical roles in the development of
compute... | computer science |
28,075 | Active Control of Camera Parameters for Object Detection Algorithms | cs.CV | Camera parameters not only play an important role in determining the visual
quality of perceived images, but also affect the performance of vision
algorithms, for a vision-guided robot. By quantitatively evaluating four object
detection algorithms, with respect to varying ambient illumination, shutter
speed and voltage... | computer science |
28,076 | Motion-Compensated Temporal Filtering for Critically-Sampled
Wavelet-Encoded Images | cs.CV | We propose a novel motion estimation/compensation (ME/MC) method for
wavelet-based (in-band) motion compensated temporal filtering (MCTF), with
application to low-bitrate video coding. Unlike the conventional in-band MCTF
algorithms, which require redundancy to overcome the shift-variance problem of
critically sampled ... | computer science |
28,077 | Volumetric Super-Resolution of Multispectral Data | cs.CV | Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either... | computer science |
28,078 | Learning Features for Offline Handwritten Signature Verification using
Deep Convolutional Neural Networks | cs.CV | Verifying the identity of a person using handwritten signatures is
challenging in the presence of skilled forgeries, where a forger has access to
a person's signature and deliberately attempt to imitate it. In offline
(static) signature verification, the dynamic information of the signature
writing process is lost, and... | computer science |
28,079 | What's In A Patch, I: Tensors, Differential Geometry and Statistical
Shading Analysis | cs.CV | We develop a linear algebraic framework for the shape-from-shading problem,
because tensors arise when scalar (e.g. image) and vector (e.g. surface normal)
fields are differentiated multiple times. The work is in two parts. In this
first part we investigate when image derivatives exhibit invariance to changing
illumina... | computer science |
28,080 | What's In A Patch, II: Visualizing generic surfaces | cs.CV | We continue the development of a linear algebraic framework for the
shape-from-shading problem, exploiting the manner in which tensors arise when
scalar (e.g. image) and vector (e.g. surface normal) fields are differentiated
multiple times. In this paper we apply that framework to develop Taylor
expansions of the norma... | computer science |
28,081 | LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object
Detection in Embedded Systems | cs.CV | Deep convolutional Neural Networks (CNN) are the state-of-the-art performers
for object detection task. It is well known that object detection requires more
computation and memory than image classification. Thus the consolidation of a
CNN-based object detection for an embedded system is more challenging. In this
work, ... | computer science |
28,082 | Learning a Hierarchical Latent-Variable Model of 3D Shapes | cs.CV | We propose the Variational Shape Learner (VSL), a hierarchical
latent-variable model for 3D shape learning. VSL employs an unsupervised
approach to learning and inferring the underlying structure of voxelized 3D
shapes. Through the use of skip-connections, our model can successfully learn a
latent, hierarchical represe... | computer science |
28,083 | Automatic Vertebra Labeling in Large-Scale 3D CT using Deep
Image-to-Image Network with Message Passing and Sparsity Regularization | cs.CV | Automatic localization and labeling of vertebra in 3D medical images plays an
important role in many clinical tasks, including pathological diagnosis,
surgical planning and postoperative assessment. However, the unusual conditions
of pathological cases, such as the abnormal spine curvature, bright visual
imaging artifa... | computer science |
28,084 | PaMM: Pose-aware Multi-shot Matching for Improving Person
Re-identification | cs.CV | Person re-identification is the problem of recognizing people across
different images or videos with non-overlapping views. Although there has been
much progress in person re-identification over the last decade, it remains a
challenging task because appearances of people can seem extremely different
across diverse came... | computer science |
28,085 | Robust Registration of Gaussian Mixtures for Colour Transfer | cs.CV | We present a flexible approach to colour transfer inspired by techniques
recently proposed for shape registration. Colour distributions of the palette
and target images are modelled with Gaussian Mixture Models (GMMs) that are
robustly registered to infer a non linear parametric transfer function. We show
experimentall... | computer science |
28,086 | Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule
Robot | cs.CV | A reliable, real-time simultaneous localization and mapping (SLAM) method is
crucial for the navigation of actively controlled capsule endoscopy robots.
These robots are an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a dense, n... | computer science |
28,087 | A deep level set method for image segmentation | cs.CV | This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-... | computer science |
28,088 | Deep Diagnostics: Applying Convolutional Neural Networks for Vessels
Defects Detection | cs.CV | Coronary angiography is considered to be a safe tool for the evaluation of
coronary artery disease and perform in approximately 12 million patients each
year worldwide. [1] In most cases, angiograms are manually analyzed by a
cardiologist. Actually, there are no clinical practice algorithms which could
improve and auto... | computer science |
28,089 | Bayer Demosaicking Using Optimized Mean Curvature over RGB channels | cs.CV | Color artifacts of demosaicked images are often found at contours due to
interpolation across edges and cross-channel aliasing. To tackle this problem,
we propose a novel demosaicking method to reliably reconstruct color channels
of a Bayer image based on two different optimized mean-curvature (MC) models.
The missing ... | computer science |
28,090 | Optimizing and Visualizing Deep Learning for Benign/Malignant
Classification in Breast Tumors | cs.CV | Breast cancer has the highest incidence and second highest mortality rate for
women in the US. Our study aims to utilize deep learning for benign/malignant
classification of mammogram tumors using a subset of cases from the Digital
Database of Screening Mammography (DDSM). Though it was a small dataset from
the view of... | computer science |
28,091 | Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of
Generic Objects | cs.CV | Robust object tracking requires knowledge and understanding of the object
being tracked: its appearance, its motion, and how it changes over time. A
tracker must be able to modify its underlying model and adapt to new
observations. We present Re3, a real-time deep object tracker capable of
incorporating temporal inform... | computer science |
28,092 | Fashion Forward: Forecasting Visual Style in Fashion | cs.CV | What is the future of fashion? Tackling this question from a data-driven
vision perspective, we propose to forecast visual style trends before they
occur. We introduce the first approach to predict the future popularity of
styles discovered from fashion images in an unsupervised manner. Using these
styles as a basis, w... | computer science |
28,093 | Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry | cs.CV | This work proposes a visual odometry method that combines points and plane
primitives, extracted from a noisy depth camera. Depth measurement uncertainty
is modelled and propagated through the extraction of geometric primitives to
the frame-to-frame motion estimation, where pose is optimized by weighting the
residuals ... | computer science |
28,094 | A fully dense and globally consistent 3D map reconstruction approach for
GI tract to enhance therapeutic relevance of the endoscopic capsule robot | cs.CV | In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is emerging as a novel, minimally invasive diagnostic
technology for inspection of the GI tract and diagnosis of a wide range of
diseases and pathologies. Since the development of this technology, medical
device companies and many... | computer science |
28,095 | Agent-Centric Risk Assessment: Accident Anticipation and Risky Region
Localization | cs.CV | For survival, a living agent must have the ability to assess risk (1) by
temporally anticipating accidents before they occur, and (2) by spatially
localizing risky regions in the environment to move away from threats. In this
paper, we take an agent-centric approach to study the accident anticipation and
risky region l... | computer science |
28,096 | Localized LRR on Grassmann Manifolds: An Extrinsic View | cs.CV | Subspace data representation has recently become a common practice in many
computer vision tasks. It demands generalizing classical machine learning
algorithms for subspace data. Low-Rank Representation (LRR) is one of the most
successful models for clustering vectorial data according to their subspace
structures. This... | computer science |
28,097 | MUTAN: Multimodal Tucker Fusion for Visual Question Answering | cs.CV | Bilinear models provide an appealing framework for mixing and merging
information in Visual Question Answering (VQA) tasks. They help to learn high
level associations between question meaning and visual concepts in the image,
but they suffer from huge dimensionality issues. We introduce MUTAN, a
multimodal tensor-based... | computer science |
28,098 | Target-Quality Image Compression with Recurrent, Convolutional Neural
Networks | cs.CV | We introduce a stop-code tolerant (SCT) approach to training recurrent
convolutional neural networks for lossy image compression. Our methods
introduce a multi-pass training method to combine the training goals of
high-quality reconstructions in areas around stop-code masking as well as in
highly-detailed areas. These ... | computer science |
28,099 | Model-based Catheter Segmentation in MRI-images | cs.CV | Accurate and reliable segmentation of catheters in MR-gui- ded interventions
remains a challenge, and a step of critical importance in clinical workflows.
In this work, under reasonable assumptions, me- chanical model based heuristics
guide the segmentation process allows correct catheter identification rates
greater t... | computer science |
28,100 | A General Model for Robust Tensor Factorization with Unknown Noise | cs.CV | Because of the limitations of matrix factorization, such as losing spatial
structure information, the concept of low-rank tensor factorization (LRTF) has
been applied for the recovery of a low dimensional subspace from high
dimensional visual data. The low-rank tensor recovery is generally achieved by
minimizing the lo... | computer science |
28,101 | Exploring the structure of a real-time, arbitrary neural artistic
stylization network | cs.CV | In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer n... | computer science |
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