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27,202 | How hard is it to cross the room? -- Training (Recurrent) Neural
Networks to steer a UAV | cs.CV | This work explores the feasibility of steering a drone with a (recurrent)
neural network, based on input from a forward looking camera, in the context of
a high-level navigation task. We set up a generic framework for training a
network to perform navigation tasks based on imitation learning. It can be
applied to both ... | computer science |
27,203 | Automatic segmentation of agricultural objects in dynamic outdoor
environments | cs.CV | Segmentation in dynamic outdoor environments can be difficult when the
illumination levels and other aspects of the scene cannot be controlled.
Specifically in agricultural contexts, a background material is often used to
shield a camera's field of view from other rows of crops. In this paper, we
describe a method that... | computer science |
27,204 | Unifying local and non-local signal processing with graph CNNs | cs.CV | This paper deals with the unification of local and non-local signal
processing on graphs within a single convolutional neural network (CNN)
framework. Building upon recent works on graph CNNs, we propose to use
convolutional layers that take as inputs two variables, a signal and a graph,
allowing the network to adapt t... | computer science |
27,205 | Video and Accelerometer-Based Motion Analysis for Automated Surgical
Skills Assessment | cs.CV | Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video ana... | computer science |
27,206 | Transfer Learning for Domain Adaptation in MRI: Application in Brain
Lesion Segmentation | cs.CV | Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysi... | computer science |
27,207 | Image Stitching by Line-guided Local Warping with Global Similarity
Constraint | cs.CV | Low-textured image stitching remains a challenging problem. It is difficult
to achieve good alignment and it is easy to break image structures due to
insufficient and unreliable point correspondences. Moreover, because of the
viewpoint variations between multiple images, the stitched images suffer from
projective disto... | computer science |
27,208 | Spatially Aware Melanoma Segmentation Using Hybrid Deep Learning
Techniques | cs.CV | In this paper, we proposed using a hybrid method that utilises deep
convolutional and recurrent neural networks for accurate delineation of skin
lesion of images supplied with ISBI 2017 lesion segmentation challenge. The
proposed method was trained using 1800 images and tested on 150 images from
ISBI 2017 challenge. | computer science |
27,209 | Seeing What Is Not There: Learning Context to Determine Where Objects
Are Missing | cs.CV | Most of computer vision focuses on what is in an image. We propose to train a
standalone object-centric context representation to perform the opposite task:
seeing what is not there. Given an image, our context model can predict where
objects should exist, even when no object instances are present. Combined with
object... | computer science |
27,210 | Building Fast and Compact Convolutional Neural Networks for Offline
Handwritten Chinese Character Recognition | cs.CV | Like other problems in computer vision, offline handwritten Chinese character
recognition (HCCR) has achieved impressive results using convolutional neural
network (CNN)-based methods. However, larger and deeper networks are needed to
deliver state-of-the-art results in this domain. Such networks intuitively
appear to ... | computer science |
27,211 | A multi-task convolutional neural network for mega-city analysis using
very high resolution satellite imagery and geospatial data | cs.CV | Mega-city analysis with very high resolution (VHR) satellite images has been
drawing increasing interest in the fields of city planning and social
investigation. It is known that accurate land-use, urban density, and
population distribution information is the key to mega-city monitoring and
environmental studies. There... | computer science |
27,212 | Bayesian Nonparametric Unmixing of Hyperspectral Images | cs.CV | Hyperspectral imaging is an important tool in remote sensing, allowing for
accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a
hyperspectral image rarely represents a single material, but rather a mixture
of different spectra. HSU aims at estimating the pure spectra present in the
scene of in... | computer science |
27,213 | Adversarial Networks for the Detection of Aggressive Prostate Cancer | cs.CV | Semantic segmentation constitutes an integral part of medical image analyses
for which breakthroughs in the field of deep learning were of high relevance.
The large number of trainable parameters of deep neural networks however
renders them inherently data hungry, a characteristic that heavily challenges
the medical im... | computer science |
27,214 | 3D Scanning System for Automatic High-Resolution Plant Phenotyping | cs.CV | Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing
structures make plants difficult for three-dimensional (3D) scanning and
reconstruction -- two critical steps in automated visual phenotyping. Many
current solutions such as laser scanning, structured light, and multiview
stereo can struggle to acqu... | computer science |
27,215 | Bioplausible multiscale filtering in retino-cortical processing as a
mechanism in perceptual grouping | cs.CV | Why does our visual system fail to reconstruct reality, when we look at
certain patterns? Where do Geometrical illusions start to emerge in the visual
pathway? How far should we take computational models of vision with the same
visual ability to detect illusions as we do? This study addresses these
questions, by focusi... | computer science |
27,216 | Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray
Microtomography of Lung Pathology | cs.CV | Computational anatomy allows the quantitative analysis of organs in medical
images. However, most analysis is constrained to the millimeter scale because
of the limited resolution of clinical computed tomography (CT). X-ray
microtomography ($\mu$CT) on the other hand allows imaging of ex-vivo tissues
at a resolution of... | computer science |
27,217 | HashBox: Hash Hierarchical Segmentation exploiting Bounding Box Object
Detection | cs.CV | We propose a novel approach to address the Simultaneous Detection and
Segmentation problem. Using hierarchical structures we use an efficient and
accurate procedure that exploits the hierarchy feature information using
Locality Sensitive Hashing. We build on recent work that utilizes convolutional
neural networks to de... | computer science |
27,218 | A Dataset for Developing and Benchmarking Active Vision | cs.CV | We present a new public dataset with a focus on simulating robotic vision
tasks in everyday indoor environments using real imagery. The dataset includes
20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely
captured in 9 unique scenes. We train a fast object category detector for
instance detec... | computer science |
27,219 | Efficient Privacy Preserving Viola-Jones Type Object Detection via
Random Base Image Representation | cs.CV | A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant t... | computer science |
27,220 | Visual Translation Embedding Network for Visual Relation Detection | cs.CV | Visual relations, such as "person ride bike" and "bike next to car", offer a
comprehensive scene understanding of an image, and have already shown their
great utility in connecting computer vision and natural language. However, due
to the challenging combinatorial complexity of modeling
subject-predicate-object relatio... | computer science |
27,221 | Multi-Label Segmentation via Residual-Driven Adaptive Regularization | cs.CV | We present a variational multi-label segmentation algorithm based on a robust
Huber loss for both the data and the regularizer, minimized within a convex
optimization framework. We introduce a novel constraint on the common areas, to
bias the solution towards mutually exclusive regions. We also propose a
regularization... | computer science |
27,222 | Revealing Hidden Potentials of the q-Space Signal in Breast Cancer | cs.CV | Mammography screening for early detection of breast lesions currently suffers
from high amounts of false positive findings, which result in unnecessary
invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
of these false-positive findings prior to biopsy. Current approaches estimate
tissue prope... | computer science |
27,223 | Age Progression/Regression by Conditional Adversarial Autoencoder | cs.CV | "If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer i... | computer science |
27,224 | Skin Lesion Classification Using Hybrid Deep Neural Networks | cs.CV | Skin cancer is one of the major types of cancers and its incidence has been
increasing over the past decades. Skin lesions can arise from various
dermatologic disorders and can be classified to various types according to
their texture, structure, color and other morphological features. The accuracy
of diagnosis of skin... | computer science |
27,225 | Understanding Convolution for Semantic Segmentation | cs.CV | Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical ... | computer science |
27,226 | DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD
Models for 2.5D Recognition | cs.CV | Recent progress in computer vision has been dominated by deep neural networks
trained over large amounts of labeled data. Collecting such datasets is however
a tedious, often impossible task; hence a surge in approaches relying solely on
synthetic data for their training. For depth images however, discrepancies with
re... | computer science |
27,227 | Enabling Sparse Winograd Convolution by Native Pruning | cs.CV | Sparse methods and the use of Winograd convolutions are two orthogonal
approaches, each of which significantly accelerates convolution computations in
modern CNNs. Sparse Winograd merges these two and thus has the potential to
offer a combined performance benefit. Nevertheless, training convolution layers
so that the r... | computer science |
27,228 | Parallel Structure from Motion from Local Increment to Global Averaging | cs.CV | In this paper, we tackle the accurate and consistent Structure from Motion
(SfM) problem, in particular camera registration, far exceeding the memory of a
single computer in parallel. Different from the previous methods which
drastically simplify the parameters of SfM and sacrifice the accuracy of the
final reconstruct... | computer science |
27,229 | Super-Trajectory for Video Segmentation | cs.CV | We introduce a novel semi-supervised video segmentation approach based on an
efficient video representation, called as "super-trajectory". Each
super-trajectory corresponds to a group of compact trajectories that exhibit
consistent motion patterns, similar appearance and close spatiotemporal
relationships. We generate ... | computer science |
27,230 | Selective Video Object Cutout | cs.CV | Conventional video segmentation approaches rely heavily on appearance models.
Such methods often use appearance descriptors that have limited discriminative
power under complex scenarios. To improve the segmentation performance, this
paper presents a pyramid histogram based confidence map that incorporates
structure in... | computer science |
27,231 | Boundary Flow: A Siamese Network that Predicts Boundary Motion without
Training on Motion | cs.CV | This paper addresses a new problem of joint object boundary detection and
boundary motion estimation in videos, which we named boundary flow estimation.
Boundary flow is an important mid-level visual cue as boundaries characterize
objects spatial extents, and the flow indicates objects motions and
interactions. Yet, mo... | computer science |
27,232 | Scene Flow to Action Map: A New Representation for RGB-D based Action
Recognition with Convolutional Neural Networks | cs.CV | Scene flow describes the motion of 3D objects in real world and potentially
could be the basis of a good feature for 3D action recognition. However, its
use for action recognition, especially in the context of convolutional neural
networks (ConvNets), has not been previously studied. In this paper, we propose
the extra... | computer science |
27,233 | 3D Shape Segmentation via Shape Fully Convolutional Networks | cs.CV | We propose a novel fully convolutional network architecture for shapes,
denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented
as graph structures in the SFCN architecture, based on novel graph convolution
and pooling operations, which are similar to convolution and pooling operations
used on i... | computer science |
27,234 | MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional
Networks with Privileged Information | cs.CV | Multi-instance multi-label (MIML) learning has many interesting applications
in computer visions, including multi-object recognition and automatic image
tagging. In these applications, additional information such as bounding-boxes,
image captions and descriptions is often available during training phrase,
which is refe... | computer science |
27,235 | Cascade one-vs-rest detection network for fine-grained recognition
without part annotations | cs.CV | Fine-grained recognition is a challenging task due to the small
intra-category variances. Most of top-performing fine-grained recognition
methods leverage parts of objects for better performance. Therefore, part
annotations which are extremely computationally expensive are required. In this
paper, we propose a novel ca... | computer science |
27,236 | II-FCN for skin lesion analysis towards melanoma detection | cs.CV | Dermoscopy image detection stays a tough task due to the weak distinguishable
property of the object.Although the deep convolution neural network
signifigantly boosted the performance on prevelance computer vision tasks in
recent years,there remains a room to explore more robust and precise models to
the problem of low... | computer science |
27,237 | An Extensive Technique to Detect and Analyze Melanoma: A Challenge at
the International Symposium on Biomedical Imaging (ISBI) 2017 | cs.CV | An automated method to detect and analyze the melanoma is presented to
improve diagnosis which will leads to the exact treatment. Image processing
techniques such as segmentation, feature descriptors and classification models
are involved in this method. In the First phase the lesion region is segmented
using CIELAB Co... | computer science |
27,238 | Weakly- and Semi-Supervised Object Detection with
Expectation-Maximization Algorithm | cs.CV | Object detection when provided image-level labels instead of instance-level
labels (i.e., bounding boxes) during training is an important problem in
computer vision, since large scale image datasets with instance-level labels
are extremely costly to obtain. In this paper, we address this challenging
problem by developi... | computer science |
27,239 | MILD: Multi-Index hashing for Loop closure Detection | cs.CV | Loop Closure Detection (LCD) has been proved to be extremely useful in global
consistent visual Simultaneously Localization and Mapping (SLAM) and
appearance-based robot relocalization. Methods exploiting binary features in
bag of words representation have recently gained a lot of popularity for their
efficiency, but s... | computer science |
27,240 | Unsupervised Triplet Hashing for Fast Image Retrieval | cs.CV | Hashing has played a pivotal role in large-scale image retrieval. With the
development of Convolutional Neural Network (CNN), hashing learning has shown
great promise. But existing methods are mostly tuned for classification, which
are not optimized for retrieval tasks, especially for instance-level retrieval.
In this ... | computer science |
27,241 | Predicting Slice-to-Volume Transformation in Presence of Arbitrary
Subject Motion | cs.CV | This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image sli... | computer science |
27,242 | Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance
Imaging | cs.CV | 3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but
low-resolution image acquisition and highly detailed but slow image
acquisition. Fast imaging is required for targets that move to avoid motion
artefacts. This is in particular difficult for fetal MRI. Spatially independent
upsampling techniques,... | computer science |
27,243 | Deep Image Harmonization | cs.CV | Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance fe... | computer science |
27,244 | Discrete Wavelet Transform Based Algorithm for Recognition of QRS
Complexes | cs.CV | This paper proposes the application of Discrete Wavelet Transform (DWT) to
detect the QRS (ECG is characterized by a recurrent wave sequence of P, QRS and
T-wave) of an electrocardiogram (ECG) signal. Wavelet Transform provides
localization in both time and frequency. In preprocessing stage, DWT is used to
remove the b... | computer science |
27,245 | Supervised Saliency Map Driven Segmentation of the Lesions in
Dermoscopic Images | cs.CV | Lesion segmentation is the first step in the most automatic melanoma
recognition systems. There are some deficiencies and difficulties in
dermoscopic images that make the lesion segmentation an intricate task e.g.,
hair occlusion, presence of dark corners and color charts, indistinct lesion
borders, and lesions touchin... | computer science |
27,246 | Remote Sensing Image Scene Classification: Benchmark and State of the
Art | cs.CV | Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
image... | computer science |
27,247 | RGB-D Salient Object Detection Based on Discriminative Cross-modal
Transfer Learning | cs.CV | In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap bet... | computer science |
27,248 | Saliency Detection by Forward and Backward Cues in Deep-CNNs | cs.CV | As prior knowledge of objects or object features helps us make relations for
similar objects on attentional tasks, pre-trained deep convolutional neural
networks (CNNs) can be used to detect salient objects on images regardless of
the object class is in the network knowledge or not. In this paper, we propose
a top-down... | computer science |
27,249 | Saliency Fusion in Eigenvector Space with Multi-Channel Pulse Coupled
Neural Network | cs.CV | Saliency computation has become a popular research field for many
applications due to the useful information provided by saliency maps. For a
saliency map, local relations around the salient regions in multi-channel
perspective should be taken into consideration by aiming uniformity on the
region of interest as an inte... | computer science |
27,250 | Optical Flow-based 3D Human Motion Estimation from Monocular Video | cs.CV | We present a generative method to estimate 3D human motion and body shape
from monocular video. Under the assumption that starting from an initial pose
optical flow constrains subsequent human motion, we exploit flow to find
temporally coherent human poses of a motion sequence. We estimate human motion
by minimizing th... | computer science |
27,251 | Incorporating Intra-Class Variance to Fine-Grained Visual Recognition | cs.CV | Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the ... | computer science |
27,252 | Improving Object Detection with Region Similarity Learning | cs.CV | Object detection aims to identify instances of semantic objects of a certain
class in images or videos. The success of state-of-the-art approaches is
attributed to the significant progress of object proposal and convolutional
neural networks (CNNs). Most promising detectors involve multi-task learning
with an optimizat... | computer science |
27,253 | Human Eye Visual Hyperacuity: A New Paradigm for Sensing? | cs.CV | The human eye appears to be using a low number of sensors for image
capturing. Furthermore, regarding the physical dimensions of
cones-photoreceptors responsible for the sharp central vision-, we may realize
that these sensors are of a relatively small size and area. Nonetheless, the
eye is capable to obtain high resol... | computer science |
27,254 | Group Sparsity Residual Constraint for Image Denoising | cs.CV | Group-based sparse representation has shown great potential in image
denoising. However, most existing methods only consider the nonlocal
self-similarity (NSS) prior of noisy input image. That is, the similar patches
are collected only from degraded input, which makes the quality of image
denoising largely depend on th... | computer science |
27,255 | Multi-stage Neural Networks with Single-sided Classifiers for False
Positive Reduction and its Evaluation using Lung X-ray CT Images | cs.CV | Lung nodule classification is a class imbalanced problem because nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We therefore propose cascaded convolutional
neural networks... | computer science |
27,256 | Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense
Multi-Label CRFs | cs.CV | This paper proposes a novel approach for uncertainty quantification in dense
Conditional Random Fields (CRFs). The presented approach, called
Perturb-and-MPM, enables efficient, approximate sampling from dense multi-label
CRFs via random perturbations. An analytic error analysis was performed which
identified the main ... | computer science |
27,257 | Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for
Click Free Viewing | cs.CV | 360$^{\circ}$ video requires human viewers to actively control "where" to
look while watching the video. Although it provides a more immersive experience
of the visual content, it also introduces additional burden for viewers;
awkward interfaces to navigate the video lead to suboptimal viewing
experiences. Virtual cine... | computer science |
27,258 | ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection | cs.CV | Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion
Classification of the ISIC 2017 challenge. Both algorithms make use of deep
convolutional networks to achieve the challenge objective. | computer science |
27,259 | Skin cancer reorganization and classification with deep neural network | cs.CV | As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization strategy is critical for melanoma therapy.
However, well-trained dermatologists dominant the diagnostic accuracy. In order
to solve this problem, many effort focus on developing automatic image analysis
systems.... | computer science |
27,260 | Label Refinement Network for Coarse-to-Fine Semantic Segmentation | cs.CV | We consider the problem of semantic image segmentation using deep
convolutional neural networks. We propose a novel network architecture called
the label refinement network that predicts segmentation labels in a
coarse-to-fine fashion at several resolutions. The segmentation labels at a
coarse resolution are used toget... | computer science |
27,261 | Change Detection under Global Viewpoint Uncertainty | cs.CV | This paper addresses the problem of change detection from a novel perspective
of long-term map learning. We are particularly interested in designing an
approach that can scale to large maps and that can function under global
uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which
utilizes a comp... | computer science |
27,262 | A Deep Cascade of Convolutional Neural Networks for MR Image
Reconstruction | cs.CV | The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian un... | computer science |
27,263 | Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning
Network | cs.CV | Skin lesion is a severe disease in world-wide extent. Early detection of
melanoma in dermoscopy images significantly increases the survival rate.
However, the accurate recognition of melanoma is extremely challenging due to
the following reasons, e.g. low contrast between lesions and skin, visual
similarity between mel... | computer science |
27,264 | A novel image tag completion method based on convolutional neural
network | cs.CV | In the problems of image retrieval and annotation, complete textual tag lists
of images play critical roles. However, in real-world applications, the image
tags are usually incomplete, thus it is important to learn the complete tags
for images. In this paper, we study the problem of image tag complete and
proposed a no... | computer science |
27,265 | TumorNet: Lung Nodule Characterization Using Multi-View Convolutional
Neural Network with Gaussian Process | cs.CV | Characterization of lung nodules as benign or malignant is one of the most
important tasks in lung cancer diagnosis, staging and treatment planning. While
the variation in the appearance of the nodules remains large, there is a need
for a fast and robust computer aided system. In this work, we propose an
end-to-end tra... | computer science |
27,266 | BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D
Bounding Boxes in Traffic Surveillance | cs.CV | In this paper, we focus on fine-grained recognition of vehicles mainly in
traffic surveillance applications. We propose an approach that is orthogonal to
recent advancements in fine-grained recognition (automatic part discovery and
bilinear pooling). In addition, in contrast to other methods focused on
fine-grained rec... | computer science |
27,267 | Robust Spatial Filtering with Graph Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) have recently led to incredible
breakthroughs on a variety of pattern recognition problems. Banks of finite
impulse response filters are learned on a hierarchy of layers, each
contributing more abstract information than the previous layer. The simplicity
and elegance of the convolut... | computer science |
27,268 | On the Reconstruction of Deep Face Templates | cs.CV | State-of-the-art face recognition systems are based on deep (convolutional)
neural networks. Therefore, it is imperative to determine to what extent face
templates derived from deep networks can be inverted to obtain the original
face image. In this paper, we study the vulnerabilities of a state-of-the-art
face recogni... | computer science |
27,269 | Towards CNN Map Compression for camera relocalisation | cs.CV | This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate response to different
data inputs -- namely, depth, grayscale, RGB, spatial position and combinations
of these. ... | computer science |
27,270 | Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification
Challenge | cs.CV | This paper describes the participation of Araguaia Medical Vision Lab at the
International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We
describe the use of deep convolutional neural networks in attempt to classify
images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned
GoogleNet and Alex... | computer science |
27,271 | A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and
Classification | cs.CV | In this study, a multi-task deep neural network is proposed for skin lesion
analysis. The proposed multi-task learning model solves different tasks (e.g.,
lesion segmentation and two independent binary lesion classifications) at the
same time by exploiting commonalities and differences across tasks. This
results in imp... | computer science |
27,272 | Outlier Cluster Formation in Spectral Clustering | cs.CV | Outlier detection and cluster number estimation is an important issue for
clustering real data. This paper focuses on spectral clustering, a time-tested
clustering method, and reveals its important properties related to outliers.
The highlights of this paper are the following two mathematical observations:
first, spect... | computer science |
27,273 | Skin Lesion Classification using Class Activation Map | cs.CV | We proposed a two stage framework with only one network to analyze skin
lesion images, we firstly trained a convolutional network to classify these
images, and cropped the import regions which the network has the maximum
activation value. In the second stage, we retrained this CNN with the image
regions extracted from ... | computer science |
27,274 | Arbitrary-Oriented Scene Text Detection via Rotation Proposals | cs.CV | This paper introduces a novel rotation-based framework for arbitrary-oriented
text detection in natural scene images. We present the Rotation Region Proposal
Networks (RRPN), which are designed to generate inclined proposals with text
orientation angle information. The angle information is then adapted for
bounding box... | computer science |
27,275 | Deep artifact learning for compressed sensing and parallel MRI | cs.CV | Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is
one of the powerful ways to reduce the scan time of MR imaging with performance
guarantee. However, the computational costs are usually expensive. This paper
aims to propose a computationally fast and accurate deep learning algorithm for
the rec... | computer science |
27,276 | Deep Learning with Domain Adaptation for Accelerated
Projection-Reconstruction MR | cs.CV | Purpose: The radial k-space trajectory is a well-established sampling
trajectory used in conjunction with magnetic resonance imaging. However, the
radial k-space trajectory requires a large number of radial lines for
high-resolution reconstruction. Increasing the number of radial lines causes
longer acquisition time, m... | computer science |
27,277 | EmotioNet Challenge: Recognition of facial expressions of emotion in the
wild | cs.CV | This paper details the methodology and results of the EmotioNet challenge.
This challenge is the first to test the ability of computer vision algorithms
in the automatic analysis of a large number of images of facial expressions of
emotion in the wild. The challenge was divided into two tracks. The first track
tested t... | computer science |
27,278 | Context Aware Query Image Representation for Particular Object Retrieval | cs.CV | The current models of image representation based on Convolutional Neural
Networks (CNN) have shown tremendous performance in image retrieval. Such
models are inspired by the information flow along the visual pathway in the
human visual cortex. We propose that in the field of particular object
retrieval, the process of ... | computer science |
27,279 | Deep Collaborative Learning for Visual Recognition | cs.CV | Deep neural networks are playing an important role in state-of-the-art visual
recognition. To represent high-level visual concepts, modern networks are
equipped with large convolutional layers, which use a large number of filters
and contribute significantly to model complexity. For example, more than half
of the weigh... | computer science |
27,280 | Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery | cs.CV | One of the major challenges in Minimally Invasive Surgery (MIS) such as
laparoscopy is the lack of depth perception. In recent years, laparoscopic
scene tracking and surface reconstruction has been a focus of investigation to
provide rich additional information to aid the surgical process and compensate
for the depth p... | computer science |
27,281 | Incident Light Frequency-based Image Defogging Algorithm | cs.CV | Considering the problem of color distortion caused by the defogging algorithm
based on dark channel prior, an improved algorithm was proposed to calculate
the transmittance of all channels respectively. First, incident light
frequency's effect on the transmittance of various color channels was analyzed
according to the... | computer science |
27,282 | Instance Flow Based Online Multiple Object Tracking | cs.CV | We present a method to perform online Multiple Object Tracking (MOT) of known
object categories in monocular video data. Current Tracking-by-Detection MOT
approaches build on top of 2D bounding box detections. In contrast, we exploit
state-of-the-art instance aware semantic segmentation techniques to compute 2D
shape r... | computer science |
27,283 | Bridging Saliency Detection to Weakly Supervised Object Detection Based
on Self-paced Curriculum Learning | cs.CV | Weakly-supervised object detection (WOD) is a challenging problems in
computer vision. The key problem is to simultaneously infer the exact object
locations in the training images and train the object detectors, given only the
training images with weak image-level labels. Intuitively, by simulating the
selective attent... | computer science |
27,284 | Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT
Reconstruction | cs.CV | Limited-angle computed tomography (CT) is often used in clinical applications
such as C-arm CT for interventional imaging. However, CT images from limited
angles suffers from heavy artifacts due to incomplete projection data. Existing
iterative methods require extensive calculations but can not deliver
satisfactory res... | computer science |
27,285 | Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT
Reconstruction | cs.CV | Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally complex because of the repeated use of the forward and
backward projection. Inspired by this success of deep learning in computer
vision applications, we recently proposed a deep convolutional neural network
(CNN) for low-d... | computer science |
27,286 | Looking at Outfit to Parse Clothing | cs.CV | This paper extends fully-convolutional neural networks (FCN) for the clothing
parsing problem. Clothing parsing requires higher-level knowledge on clothing
semantics and contextual cues to disambiguate fine-grained categories. We
extend FCN architecture with a side-branch network which we refer outfit
encoder to predic... | computer science |
27,287 | Stacking-based Deep Neural Network: Deep Analytic Network on
Convolutional Spectral Histogram Features | cs.CV | Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
network (DNN) resemblance in terms of its very deep, feedforward network
architecture. The typical S-DNN aggregates a variable number of individually
learnable modules in series to assemble a DNN-alike alternative to the targeted
object recog... | computer science |
27,288 | Skin Lesion Classification Using Deep Multi-scale Convolutional Neural
Networks | cs.CV | We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images. | computer science |
27,289 | Deep Matching Prior Network: Toward Tighter Multi-oriented Text
Detection | cs.CV | Detecting incidental scene text is a challenging task because of
multi-orientation, perspective distortion, and variation of text size, color
and scale. Retrospective research has only focused on using rectangular
bounding box or horizontal sliding window to localize text, which may result in
redundant background noise... | computer science |
27,290 | Automated Top View Registration of Broadcast Football Videos | cs.CV | In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
mode... | computer science |
27,291 | Generative Compression | cs.CV | Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth p... | computer science |
27,292 | Genetic CNN | cs.CV | The deep Convolutional Neural Network (CNN) is the state-of-the-art solution
for large-scale visual recognition. Following basic principles such as
increasing the depth and constructing highway connections, researchers have
manually designed a lot of fixed network structures and verified their
effectiveness.
In this ... | computer science |
27,293 | CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action
Localization in Untrimmed Videos | cs.CV | Temporal action localization is an important yet challenging problem. Given a
long, untrimmed video consisting of multiple action instances and complex
background contents, we need not only to recognize their action categories, but
also to localize the start time and end time of each instance. Many
state-of-the-art sys... | computer science |
27,294 | Deep-Learning for Classification of Colorectal Polyps on Whole-Slide
Images | cs.CV | Histopathological characterization of colorectal polyps is an important
principle for determining the risk of colorectal cancer and future rates of
surveillance for patients. This characterization is time-intensive, requires
years of specialized training, and suffers from significant inter-observer and
intra-observer v... | computer science |
27,295 | Face Alignment with Cascaded Semi-Parametric Deep Greedy Neural Forests | cs.CV | Face alignment is an active topic in computer vision, consisting in aligning
a shape model on the face. To this end, most modern approaches refine the shape
in a cascaded manner, starting from an initial guess. Those shape updates can
either be applied in the feature point space (\textit{i.e.} explicit updates)
or in a... | computer science |
27,296 | L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face
Contour Extraction | cs.CV | Current face alignment algorithms can robustly find a set of landmarks along
face contour. However, the landmarks are sparse and lack curve details,
especially in chin and cheek areas where a lot of concave-convex bending
information exists. In this paper, we propose a local to global seam cutting
and integrating algor... | computer science |
27,297 | Automatic Classification of Cancerous Tissue in Laserendomicroscopy
Images of the Oral Cavity using Deep Learning | cs.CV | Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral
epithelium. Despite their high impact on mortality, sufficient screening
methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are
mostly diagnosed at a late stage. Early detection and accurate outline
estimation of OSCCs would ... | computer science |
27,298 | Diversified Texture Synthesis with Feed-forward Networks | cs.CV | Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.... | computer science |
27,299 | 4-DoF Tracking for Robot Fine Manipulation Tasks | cs.CV | This paper presents two visual trackers from the different paradigms of
learning and registration based tracking and evaluates their application in
image based visual servoing. They can track object motion with four degrees of
freedom (DoF) which, as we will show here, is sufficient for many fine
manipulation tasks. On... | computer science |
27,300 | Viewpoint Selection for Photographing Architectures | cs.CV | This paper studies the problem of how to choose good viewpoints for taking
photographs of architectures. We achieve this by learning from professional
photographs of world famous landmarks that are available on the Internet.
Unlike previous efforts devoted to photo quality assessment which mainly rely
on 2D image featu... | computer science |
27,301 | All the people around me: face discovery in egocentric photo-streams | cs.CV | Given an unconstrained stream of images captured by a wearable photo-camera
(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
appearing faces into the individual identities present in these data. The
problem is challenging since images are acquired under real world conditions;
hence the vis... | computer science |
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