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27,302 | Deep Head Pose Estimation from Depth Data for In-car Automotive
Applications | cs.CV | Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we tackle the problem of head pose
estimation through a Convolutional Neural Network (CNN). Differently from other
proposals in the literature, the described system is able to work directly and
based ... | computer science |
27,303 | Mesh-to-raster based non-rigid registration of multi-modal images | cs.CV | Region of interest (ROI) alignment in medical images plays a crucial role in
diagnostics, procedure planning, treatment, and follow-up. Frequently, a model
is represented as triangulated mesh while the patient data is provided from CAT
scanners as pixel or voxel data. Previously, we presented a 2D method for
curve-to-p... | computer science |
27,304 | Incorporating the Knowledge of Dermatologists to Convolutional Neural
Networks for the Diagnosis of Skin Lesions | cs.CV | This report describes our submission to the ISIC 2017 Challenge in Skin
Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3:
Lesion Classification with a system for automatic diagnosis of nevus, melanoma
and seborrheic keratosis. Our approach aims to incorporate the expert knowledge
of dermat... | computer science |
27,305 | Auto-context Convolutional Neural Network (Auto-Net) for Brain
Extraction in Magnetic Resonance Imaging | cs.CV | Brain extraction or whole brain segmentation is an important first step in
many of the neuroimage analysis pipelines. The accuracy and robustness of brain
extraction, therefore, is crucial for the accuracy of the entire brain analysis
process. With the aim of designing a learning-based, geometry-independent and
registr... | computer science |
27,306 | An optimal hierarchical clustering approach to segmentation of mobile
LiDAR point clouds | cs.CV | This paper proposes a hierarchical clustering approach for the segmentation
of mobile LiDAR point clouds. We perform the hierarchical clustering on
unorganized point clouds based on a proximity matrix. The dissimilarity measure
in the proximity matrix is calculated by the Euclidean distances between
clusters and the di... | computer science |
27,307 | Deep View Morphing | cs.CV | Recently, convolutional neural networks (CNN) have been successfully applied
to view synthesis problems. However, such CNN-based methods can suffer from
lack of texture details, shape distortions, or high computational complexity.
In this paper, we propose a novel CNN architecture for view synthesis called
"Deep View M... | computer science |
27,308 | Sharing Residual Units Through Collective Tensor Factorization in Deep
Neural Networks | cs.CV | Residual units are wildly used for alleviating optimization difficulties when
building deep neural networks. However, the performance gain does not well
compensate the model size increase, indicating low parameter efficiency in
these residual units. In this work, we first revisit the residual function in
several variat... | computer science |
27,309 | Using Deep Learning Method for Classification: A Proposed Algorithm for
the ISIC 2017 Skin Lesion Classification Challenge | cs.CV | Skin cancer, the most common human malignancy, is primarily diagnosed
visually by physicians [1]. Classification with an automated method like CNN
[2, 3] shows potential for challenging tasks [1]. By now, the deep
convolutional neural networks are on par with human dermatologist [1]. This
abstract is dedicated on devel... | computer science |
27,310 | Removal of Salt and Pepper noise from Gray-Scale and Color Images: An
Adaptive Approach | cs.CV | An efficient adaptive algorithm for the removal of Salt and Pepper noise from
gray scale and color image is presented in this paper. In this proposed method
first a 3X3 window is taken and the central pixel of the window is considered
as the processing pixel. If the processing pixel is found as uncorrupted, then
it is ... | computer science |
27,311 | Shape DNA: Basic Generating Functions for Geometric Moment Invariants | cs.CV | Geometric moment invariants (GMIs) have been widely used as basic tool in
shape analysis and information retrieval. Their structure and characteristics
determine efficiency and effectiveness. Two fundamental building blocks or
generating functions (GFs) for invariants are discovered, which are dot product
and vector pr... | computer science |
27,312 | SRN: Side-output Residual Network for Object Symmetry Detection in the
Wild | cs.CV | In this paper, we establish a baseline for object symmetry detection in
complex backgrounds by presenting a new benchmark and an end-to-end deep
learning approach, opening up a promising direction for symmetry detection in
the wild. The new benchmark, named Sym-PASCAL, spans challenges including
object diversity, multi... | computer science |
27,313 | X-ray Astronomical Point Sources Recognition Using Granular Binary-tree
SVM | cs.CV | The study on point sources in astronomical images is of special importance,
since most energetic celestial objects in the Universe exhibit a point-like
appearance. An approach to recognize the point sources (PS) in the X-ray
astronomical images using our newly designed granular binary-tree support
vector machine (GBT-S... | computer science |
27,314 | Deep Learning based Large Scale Visual Recommendation and Search for
E-Commerce | cs.CV | In this paper, we present a unified end-to-end approach to build a large
scale Visual Search and Recommendation system for e-commerce. Previous works
have targeted these problems in isolation. We believe a more effective and
elegant solution could be obtained by tackling them together. We propose a
unified Deep Convolu... | computer science |
27,315 | Detecting Cancer Metastases on Gigapixel Pathology Images | cs.CV | Each year, the treatment decisions for more than 230,000 breast cancer
patients in the U.S. hinge on whether the cancer has metastasized away from the
breast. Metastasis detection is currently performed by pathologists reviewing
large expanses of biological tissues. This process is labor intensive and
error-prone. We p... | computer science |
27,316 | Object classification in images of Neoclassical furniture using Deep
Learning | cs.CV | This short paper outlines research results on object classification in images
of Neoclassical furniture. The motivation was to provide an object recognition
framework which is able to support the alignment of furniture images with a
symbolic level model. A data-driven bottom-up research routine in the
Neoclassica resea... | computer science |
27,317 | Deep Learning for Automated Quality Assessment of Color Fundus Images in
Diabetic Retinopathy Screening | cs.CV | Purpose To develop a computer based method for the automated assessment of
image quality in the context of diabetic retinopathy (DR) to guide the
photographer.
Methods A deep learning framework was trained to grade the images
automatically. A large representative set of 7000 color fundus images were used
for the expe... | computer science |
27,318 | Unsupervised Visual-Linguistic Reference Resolution in Instructional
Videos | cs.CV | We propose an unsupervised method for reference resolution in instructional
videos, where the goal is to temporally link an entity (e.g., "dressing") to
the action (e.g., "mix yogurt") that produced it. The key challenge is the
inevitable visual-linguistic ambiguities arising from the changes in both
visual appearance ... | computer science |
27,319 | Optical Flow Fields: Dense Correspondence Fields for Highly Accurate
Large Displacement Optical Flow Estimation | cs.CV | Modern large displacement optical flow algorithms usually use an
initialization by either sparse descriptor matching techniques or dense
approximate nearest neighbor fields. While the latter have the advantage of
being dense, they have the major disadvantage of being very outlier-prone as
they are not designed to find ... | computer science |
27,320 | Texture Classification of MR Images of the Brain in ALS using CoHOG | cs.CV | Texture analysis is a well-known research topic in computer vision and image
processing and has many applications. Gradient-based texture methods have
become popular in classification problems. For the first time we extend a
well-known gradient-based method, Co-occurrence Histograms of Oriented
Gradients (CoHOG) to ext... | computer science |
27,321 | Tree-Structured Reinforcement Learning for Sequential Object
Localization | cs.CV | Existing object proposal algorithms usually search for possible object
regions over multiple locations and scales separately, which ignore the
interdependency among different objects and deviate from the human perception
procedure. To incorporate global interdependency between objects into object
localization, we propo... | computer science |
27,322 | A Pursuit of Temporal Accuracy in General Activity Detection | cs.CV | Detecting activities in untrimmed videos is an important but challenging
task. The performance of existing methods remains unsatisfactory, e.g., they
often meet difficulties in locating the beginning and end of a long complex
action. In this paper, we propose a generic framework that can accurately
detect a wide variet... | computer science |
27,323 | Large Kernel Matters -- Improve Semantic Segmentation by Global
Convolutional Network | cs.CV | One of recent trends [30, 31, 14] in network architec- ture design is
stacking small filters (e.g., 1x1 or 3x3) in the entire network because the
stacked small filters is more ef- ficient than a large kernel, given the same
computational complexity. However, in the field of semantic segmenta- tion,
where we need to per... | computer science |
27,324 | A Linear Extrinsic Calibration of Kaleidoscopic Imaging System from
Single 3D Point | cs.CV | This paper proposes a new extrinsic calibration of kaleidoscopic imaging
system by estimating normals and distances of the mirrors. The problem to be
solved in this paper is a simultaneous estimation of all mirror parameters
consistent throughout multiple reflections. Unlike conventional methods
utilizing a pair of dir... | computer science |
27,325 | Transformation-Grounded Image Generation Network for Novel 3D View
Synthesis | cs.CV | We present a transformation-grounded image generation network for novel 3D
view synthesis from a single image. Instead of taking a 'blank slate' approach,
we first explicitly infer the parts of the geometry visible both in the input
and novel views and then re-cast the remaining synthesis problem as image
completion. S... | computer science |
27,326 | Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D
Skeletons | cs.CV | HMMs are widely used in action and gesture recognition due to their
implementation simplicity, low computational requirement, scalability and high
parallelism. They have worth performance even with a limited training set. All
these characteristics are hard to find together in other even more accurate
methods. In this p... | computer science |
27,327 | QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of
Retinal OCT Data | cs.CV | Optical coherence tomography (OCT) enables high-resolution and non-invasive
3D imaging of the human retina but is inherently impaired by speckle noise.
This paper introduces a spatio-temporal denoising algorithm for OCT data on a
B-scan level using a novel quantile sparse image (QuaSI) prior. To remove
speckle noise wh... | computer science |
27,328 | Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep
Neural Network Ensemble | cs.CV | This short paper reports the method and the evaluation results of Casio and
Shinshu University joint team for the ISBI Challenge 2017 - Skin Lesion
Analysis Towards Melanoma Detection - Part 3: Lesion Classification hosted by
ISIC. Our online validation score was 0.958 with melanoma classifier AUC 0.924
and seborrheic ... | computer science |
27,329 | DeepSD: Generating High Resolution Climate Change Projections through
Single Image Super-Resolution | cs.CV | The impacts of climate change are felt by most critical systems, such as
infrastructure, ecological systems, and power-plants. However, contemporary
Earth System Models (ESM) are run at spatial resolutions too coarse for
assessing effects this localized. Local scale projections can be obtained using
statistical downsca... | computer science |
27,330 | Segmenting Dermoscopic Images | cs.CV | We propose an automatic algorithm, named SDI, for the segmentation of skin
lesions in dermoscopic images, articulated into three main steps: selection of
the image ROI, selection of the segmentation band, and segmentation. We present
extensive experimental results achieved by the SDI algorithm on the lesion
segmentatio... | computer science |
27,331 | Prior-based Hierarchical Segmentation Highlighting Structures of
Interest | cs.CV | Image segmentation is the process of partitioning an image into a set of
meaningful regions according to some criteria. Hierarchical segmentation has
emerged as a major trend in this regard as it favors the emergence of important
regions at different scales. On the other hand, many methods allow us to have
prior inform... | computer science |
27,332 | WebCaricature: a benchmark for caricature face recognition | cs.CV | Caricatures are facial drawings by artists with exaggeration on certain
facial parts. The exaggerations are often beyond realism and yet the
caricatures are still recognizable by humans. With the advent of deep learning,
recognition performances by computers on real-world faces has become comparable
to human performanc... | computer science |
27,333 | End-to-end semantic face segmentation with conditional random fields as
convolutional, recurrent and adversarial networks | cs.CV | Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and r... | computer science |
27,334 | UntrimmedNets for Weakly Supervised Action Recognition and Detection | cs.CV | Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from ... | computer science |
27,335 | A New Representation of Skeleton Sequences for 3D Action Recognition | cs.CV | This paper presents a new method for 3D action recognition with skeleton
sequences (i.e., 3D trajectories of human skeleton joints). The proposed method
first transforms each skeleton sequence into three clips each consisting of
several frames for spatial temporal feature learning using deep neural
networks. Each clip ... | computer science |
27,336 | A New Evaluation Protocol and Benchmarking Results for Extendable
Cross-media Retrieval | cs.CV | This paper proposes a new evaluation protocol for cross-media retrieval which
better fits the real-word applications. Both image-text and text-image
retrieval modes are considered. Traditionally, class labels in the training and
testing sets are identical. That is, it is usually assumed that the query falls
into some p... | computer science |
27,337 | Multi-frequency image reconstruction for radio-interferometry with
self-tuned regularization parameters | cs.CV | As the world's largest radio telescope, the Square Kilometer Array (SKA) will
provide radio interferometric data with unprecedented detail. Image
reconstruction algorithms for radio interferometry are challenged to scale well
with TeraByte image sizes never seen before. In this work, we investigate one
such 3D image re... | computer science |
27,338 | Fast LIDAR-based Road Detection Using Fully Convolutional Neural
Networks | cs.CV | In this work, a deep learning approach has been developed to carry out road
detection using only LIDAR data. Starting from an unstructured point cloud,
top-view images encoding several basic statistics such as mean elevation and
density are generated. By considering a top-view representation, road detection
is reduced ... | computer science |
27,339 | From Depth Data to Head Pose Estimation: a Siamese approach | cs.CV | The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional met... | computer science |
27,340 | Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis | cs.CV | Dermoscopic skin images are often obtained with different imaging devices,
under varying acquisition conditions. In this work, instead of attempting to
perform intensity and color normalization, we propose to leverage computational
color constancy techniques to build an artificial data augmentation technique
suitable f... | computer science |
27,341 | Development of An Android Application for Object Detection Based on
Color, Shape, or Local Features | cs.CV | Object detection and recognition is an important task in many computer vision
applications. In this paper an Android application was developed using Eclipse
IDE and OpenCV3 Library. This application is able to detect objects in an image
that is loaded from the mobile gallery, based on its color, shape, or local
feature... | computer science |
27,342 | Depth from Monocular Images using a Semi-Parallel Deep Neural Network
(SPDNN) Hybrid Architecture | cs.CV | Convolutional Neural Network (CNN) techniques are applied to the problem of
determining the depth from a single camera image (monocular depth). Fully
connected CNN topologies preserve all details of the input images, enabling the
detection of fine details, but miss larger features; networks that employ 2x2,
4x4 and 8x8... | computer science |
27,343 | Deep Image Matting | cs.CV | Image matting is a fundamental computer vision problem and has many
applications. Previous algorithms have poor performance when an image has
similar foreground and background colors or complicated textures. The main
reasons are prior methods 1) only use low-level features and 2) lack high-level
context. In this paper,... | computer science |
27,344 | Viraliency: Pooling Local Virality | cs.CV | In our overly-connected world, the automatic recognition of virality - the
quality of an image or video to be rapidly and widely spread in social networks
- is of crucial importance, and has recently awaken the interest of the
computer vision community. Concurrently, recent progress in deep learning
architectures showe... | computer science |
27,345 | Web-based visualisation of head pose and facial expressions changes:
monitoring human activity using depth data | cs.CV | Despite significant recent advances in the field of head pose estimation and
facial expression recognition, raising the cognitive level when analysing human
activity presents serious challenges to current concepts. Motivated by the need
of generating comprehensible visual representations from different sets of
data, we... | computer science |
27,346 | Neural method for Explicit Mapping of Quasi-curvature Locally Linear
Embedding in image retrieval | cs.CV | This paper proposed a new explicit nonlinear dimensionality reduction using
neural networks for image retrieval tasks. We first proposed a Quasi-curvature
Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear
criterion in neighborhood of each sample. Then, a neural method (NM) is
proposed for out... | computer science |
27,347 | Colorization as a Proxy Task for Visual Understanding | cs.CV | We investigate and improve self-supervision as a drop-in replacement for
ImageNet pretraining, focusing on automatic colorization as the proxy task.
Self-supervised training has been shown to be more promising for utilizing
unlabeled data than other, traditional unsupervised learning methods. We build
on this success a... | computer science |
27,348 | Multi-Pose Face Recognition Using Hybrid Face Features Descriptor | cs.CV | This paper presents a multi-pose face recognition approach using hybrid face
features descriptors (HFFD). The HFFD is a face descriptor containing of rich
discriminant information that is created by fusing some frequency-based
features extracted using both wavelet and DCT analysis of several different
poses of 2D face ... | computer science |
27,349 | Local Patch Classification Based Framework for Single Image
Super-Resolution | cs.CV | Recent learning-based super-resolution (SR) methods often focus on the
dictionary learning or network training. In this paper, we detailedly discuss a
new SR framework based on local classification instead of traditional
dictionary learning. The proposed efficient and extendible SR framework is
named as local patch cla... | computer science |
27,350 | Improving Interpretability of Deep Neural Networks with Semantic
Information | cs.CV | Interpretability of deep neural networks (DNNs) is essential since it enables
users to understand the overall strengths and weaknesses of the models, conveys
an understanding of how the models will behave in the future, and how to
diagnose and correct potential problems. However, it is challenging to reason
about what ... | computer science |
27,351 | Evaluating Deep Convolutional Neural Networks for Material
Classification | cs.CV | Determining the material category of a surface from an image is a demanding
task in perception that is drawing increasing attention. Following the recent
remarkable results achieved for image classification and object detection
utilising Convolutional Neural Networks (CNNs), we empirically study material
classification... | computer science |
27,352 | Detection of Human Rights Violations in Images: Can Convolutional Neural
Networks help? | cs.CV | After setting the performance benchmarks for image, video, speech and audio
processing, deep convolutional networks have been core to the greatest advances
in image recognition tasks in recent times. This raises the question of whether
there are any benefit in targeting these remarkable deep architectures with the
unat... | computer science |
27,353 | Combining Residual Networks with LSTMs for Lipreading | cs.CV | We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size targ... | computer science |
27,354 | Co-occurrence Filter | cs.CV | Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on
the Bilateral Filter (BF) but instead of using a Gaussian on the range values
to preserve edges it relies on a co-occurrence matrix. Pixel values that
co-occur frequently in the image (i.e., inside textured regions) will have a
high weight in th... | computer science |
27,355 | Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep
Convolutional Neural Networks | cs.CV | Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented
progress, achieving the accuracy close to, or even better than human-level
perception in various tasks. There is a timely need to map the latest software
DCNNs to application-specific hardware, in order to achieve orders of magnitude
improveme... | computer science |
27,356 | Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and
Deep Residual Networks | cs.CV | Malignant melanoma has one of the most rapidly increasing incidences in the
world and has a considerable mortality rate. Early diagnosis is particularly
important since melanoma can be cured with prompt excision. Dermoscopy images
play an important role in the non-invasive early detection of melanoma [1].
However, mela... | computer science |
27,357 | GUN: Gradual Upsampling Network for single image super-resolution | cs.CV | In this paper, we propose an efficient super-resolution (SR) method based on
deep convolutional neural network (CNN), namely gradual upsampling network
(GUN). Recent CNN based SR methods either preliminarily magnify the low
resolution (LR) input to high resolution (HR) and then reconstruct the HR
input, or directly rec... | computer science |
27,358 | Automatic Skin Lesion Segmentation using Semi-supervised Learning
Technique | cs.CV | Skin cancer is the most common of all cancers and each year million cases of
skin cancer are treated. Treating and curing skin cancer is easy, if it is
diagnosed and treated at an early stage. In this work we propose an automatic
technique for skin lesion segmentation in dermoscopic images which helps in
classifying th... | computer science |
27,359 | A Pitfall of Unsupervised Pre-Training | cs.CV | The point of this paper is to question typical assumptions in deep learning
and suggest alternatives. A particular contribution is to prove that even if a
Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is
not necessarily good at discriminating their classes. When using Auto-Encoders,
intuitiv... | computer science |
27,360 | A Localisation-Segmentation Approach for Multi-label Annotation of
Lumbar Vertebrae using Deep Nets | cs.CV | Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of... | computer science |
27,361 | Deep Learning for Skin Lesion Classification | cs.CV | Melanoma, a malignant form of skin cancer is very threatening to life.
Diagnosis of melanoma at an earlier stage is highly needed as it has a very
high cure rate. Benign and malignant forms of skin cancer can be detected by
analyzing the lesions present on the surface of the skin using dermoscopic
images. In this work,... | computer science |
27,362 | Randomized Iterative Reconstruction for Sparse View X-ray Computed
Tomography | cs.CV | With the availability of more powerful computers, iterative reconstruction
algorithms are the subject of an ongoing work in the design of more efficient
reconstruction algorithms for X-ray computed tomography. In this work, we show
how two analytical reconstruction algorithms can be improved by correcting the
correspon... | computer science |
27,363 | Zero-Shot Learning - The Good, the Bad and the Ugly | cs.CV | Due to the importance of zero-shot learning, the number of proposed
approaches has increased steadily recently. We argue that it is time to take a
step back and to analyze the status quo of the area. The purpose of this paper
is three-fold. First, given the fact that there is no agreed upon zero-shot
learning benchmark... | computer science |
27,364 | Improving LBP and its variants using anisotropic diffusion | cs.CV | The main purpose of this paper is to propose a new preprocessing step in
order to improve local feature descriptors and texture classification.
Preprocessing is implemented by using transformations which help highlight
salient features that play a significant role in texture recognition. We
evaluate and compare four di... | computer science |
27,365 | Detailed, accurate, human shape estimation from clothed 3D scan
sequences | cs.CV | We address the problem of estimating human pose and body shape from 3D scans
over time. Reliable estimation of 3D body shape is necessary for many
applications including virtual try-on, health monitoring, and avatar creation
for virtual reality. Scanning bodies in minimal clothing, however, presents a
practical barrier... | computer science |
27,366 | Fully Convolutional Networks to Detect Clinical Dermoscopic Features | cs.CV | We use a pretrained fully convolutional neural network to detect clinical
dermoscopic features from dermoscopy skin lesion images. We reformulate the
superpixel classification task as an image segmentation problem, and extend a
neural network architecture originally designed for image classification to
detect dermoscop... | computer science |
27,367 | Learning Background-Aware Correlation Filters for Visual Tracking | cs.CV | Correlation Filters (CFs) have recently demonstrated excellent performance in
terms of rapidly tracking objects under challenging photometric and geometric
variations. The strength of the approach comes from its ability to efficiently
learn - "on the fly" - how the object is changing over time. A fundamental
drawback t... | computer science |
27,368 | Subspace Learning in The Presence of Sparse Structured Outliers and
Noise | cs.CV | Subspace learning is an important problem, which has many applications in
image and video processing. It can be used to find a low-dimensional
representation of signals and images. But in many applications, the desired
signal is heavily distorted by outliers and noise, which negatively affect the
learned subspace. In t... | computer science |
27,369 | Recasting Residual-based Local Descriptors as Convolutional Neural
Networks: an Application to Image Forgery Detection | cs.CV | Local descriptors based on the image noise residual have proven extremely
effective for a number of forensic applications, like forgery detection and
localization. Nonetheless, motivated by promising results in computer vision,
the focus of the research community is now shifting on deep learning. In this
paper we show ... | computer science |
27,370 | A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection
and Localization | cs.CV | We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-... | computer science |
27,371 | A Framework for Dynamic Image Sampling Based on Supervised Learning
(SLADS) | cs.CV | Sparse sampling schemes have the potential to dramatically reduce image
acquisition time while simultaneously reducing radiation damage to samples.
However, for a sparse sampling scheme to be useful it is important that we are
able to reconstruct the underlying object with sufficient clarity using the
sparse measuremen... | computer science |
27,372 | A fully end-to-end deep learning approach for real-time simultaneous 3D
reconstruction and material recognition | cs.CV | This paper addresses the problem of simultaneous 3D reconstruction and
material recognition and segmentation. Enabling robots to recognise different
materials (concrete, metal etc.) in a scene is important for many tasks, e.g.
robotic interventions in nuclear decommissioning. Previous work on 3D semantic
reconstruction... | computer science |
27,373 | Tracking Gaze and Visual Focus of Attention of People Involved in Social
Interaction | cs.CV | The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore th... | computer science |
27,374 | RECOD Titans at ISIC Challenge 2017 | cs.CV | This extended abstract describes the participation of RECOD Titans in parts 1
and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma
Detection" (ISBI 2017). Although our team has a long experience with melanoma
classification, the ISIC Challenge 2017 was the very first time we worked on
skin-lesion seg... | computer science |
27,375 | In Search of a Dataset for Handwritten Optical Music Recognition:
Introducing MUSCIMA++ | cs.CV | Optical Music Recognition (OMR) has long been without an adequate dataset and
ground truth for evaluating OMR systems, which has been a major problem for
establishing a state of the art in the field. Furthermore, machine learning
methods require training data. We analyze how the OMR processing pipeline can
be expressed... | computer science |
27,376 | A Proximity-Aware Hierarchical Clustering of Faces | cs.CV | In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using n... | computer science |
27,377 | Skin lesion segmentation based on preprocessing, thresholding and neural
networks | cs.CV | This abstract describes the segmentation system used to participate in the
challenge ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection. Several
preprocessing techniques have been tested for three color representations (RGB,
YCbCr and HSV) of 392 images. Results have been used to choose the better
preprocessing... | computer science |
27,378 | Face Recognition using Multi-Modal Low-Rank Dictionary Learning | cs.CV | Face recognition has been widely studied due to its importance in different
applications; however, most of the proposed methods fail when face images are
occluded or captured under illumination and pose variations. Recently several
low-rank dictionary learning methods have been proposed and achieved promising
results f... | computer science |
27,379 | Source Camera Identification Based On Content-Adaptive Fusion Network | cs.CV | Source camera identification is still a hard task in forensics community,
especially for the case of the small query image size. In this paper, we
propose a solution to identify the source camera of the small-size images:
content-adaptive fusion network. In order to learn better feature
representation from the input da... | computer science |
27,380 | Comparison of the Deep-Learning-Based Automated Segmentation Methods for
the Head Sectioned Images of the Virtual Korean Human Project | cs.CV | This paper presents an end-to-end pixelwise fully automated segmentation of
the head sectioned images of the Visible Korean Human (VKH) project based on
Deep Convolutional Neural Networks (DCNNs). By converting classification
networks into Fully Convolutional Networks (FCNs), a coarse prediction map,
with smaller size ... | computer science |
27,381 | What Uncertainties Do We Need in Bayesian Deep Learning for Computer
Vision? | cs.CV | There are two major types of uncertainty one can model. Aleatoric uncertainty
captures noise inherent in the observations. On the other hand, epistemic
uncertainty accounts for uncertainty in the model -- uncertainty which can be
explained away given enough data. Traditionally it has been difficult to model
epistemic u... | computer science |
27,382 | Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths | cs.CV | Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we ... | computer science |
27,383 | Large Margin Object Tracking with Circulant Feature Maps | cs.CV | Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
stro... | computer science |
27,384 | Learning Rank Reduced Interpolation with Principal Component Analysis | cs.CV | In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of ... | computer science |
27,385 | Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar
geometry and feature correspondences | cs.CV | Traditionally, pose estimation is considered as a two step problem. First,
feature correspondences are determined by direct comparison of image patches,
or by associating feature descriptors. In a second step, the relative pose and
the coordinates of corresponding points are estimated, most often by minimizing
the repr... | computer science |
27,386 | A Data Driven Approach for Compound Figure Separation Using
Convolutional Neural Networks | cs.CV | A key problem in automatic analysis and understanding of scientific papers is
to extract semantic information from non-textual paper components like figures,
diagrams, tables, etc. Much of this work requires a very first preprocessing
step: decomposing compound multi-part figures into individual subfigures.
Previous wo... | computer science |
27,387 | Block Compressive Sensing of Image and Video with Nonlocal Lagrangian
Multiplier and Patch-based Sparse Representation | cs.CV | Although block compressive sensing (BCS) makes it tractable to sense
large-sized images and video, its recovery performance has yet to be
significantly improved because its recovered images or video usually suffer
from blurred edges, loss of details, and high-frequency oscillatory artifacts,
especially at a low subrate... | computer science |
27,388 | Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion
Classification Challenge | cs.CV | This manuscript briefly describes an algorithm developed for the ISIC 2017
Skin Lesion Classification Competition. In this task, participants are asked to
complete two independent binary image classification tasks that involve three
unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis).
In the fi... | computer science |
27,389 | Real-Time Panoramic Tracking for Event Cameras | cs.CV | Event cameras are a paradigm shift in camera technology. Instead of full
frames, the sensor captures a sparse set of events caused by intensity changes.
Since only the changes are transferred, those cameras are able to capture quick
movements of objects in the scene or of the camera itself. In this work we
propose a no... | computer science |
27,390 | Automatic skin lesion segmentation with fully
convolutional-deconvolutional networks | cs.CV | This paper summarizes our method and validation results for the ISBI
Challenge 2017 - Skin Lesion Analysis Towards Melanoma Detection - Part I:
Lesion Segmentation | computer science |
27,391 | Learning to Discover Cross-Domain Relations with Generative Adversarial
Networks | cs.CV | While humans easily recognize relations between data from different domains
without any supervision, learning to automatically discover them is in general
very challenging and needs many ground-truth pairs that illustrate the
relations. To avoid costly pairing, we address the task of discovering
cross-domain relations ... | computer science |
27,392 | Texture segmentation with Fully Convolutional Networks | cs.CV | In the last decade, deep learning has contributed to advances in a wide range
computer vision tasks including texture analysis. This paper explores a new
approach for texture segmentation using deep convolutional neural networks,
sharing important ideas with classic filter bank based texture segmentation
methods. Sever... | computer science |
27,393 | Transfer Learning for Melanoma Detection: Participation in ISIC 2017
Skin Lesion Classification Challenge | cs.CV | This manuscript describes our participation in the International Skin Imaging
Collaboration's 2017 Skin Lesion Analysis Towards Melanoma Detection
competition. We participated in Part 3: Lesion Classification. The two stated
goals of this binary image classification challenge were to distinguish between
(a) melanoma an... | computer science |
27,394 | A Hybrid Supervised-unsupervised Method on Image Topic Visualization
with Convolutional Neural Network and LDA | cs.CV | Given the progress in image recognition with recent data driven paradigms,
it's still expensive to manually label a large training data to fit a
convolutional neural network (CNN) model. This paper proposes a hybrid
supervised-unsupervised method combining a pre-trained AlexNet with Latent
Dirichlet Allocation (LDA) to... | computer science |
27,395 | Illuminant Estimation using Ensembles of Multivariate Regression Trees | cs.CV | White balancing is a fundamental step in the image processing pipeline. The
process involves estimating the chromaticity of the illuminant or light source
and using the estimate to correct the image to remove any color cast. Given the
importance of the problem, there has been much previous work on illuminant
estimation... | computer science |
27,396 | Convolutional Low-Resolution Fine-Grained Classification | cs.CV | Successful fine-grained image classification methods learn subtle details
between visually similar (sub-)classes, but the problem becomes significantly
more challenging if the details are missing due to low resolution. Encouraged
by the recent success of Convolutional Neural Network (CNN) architectures in
image classif... | computer science |
27,397 | Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics:
Index-of-Max Hashing | cs.CV | In this paper, we propose a ranking based locality sensitive hashing inspired
two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for
biometric template protection. With externally generated random parameters, IoM
hashing transforms a real-valued biometric feature vector into discrete index
(max ranke... | computer science |
27,398 | Using Human Brain Activity to Guide Machine Learning | cs.CV | Machine learning is a field of computer science that builds algorithms that
learn. In many cases, machine learning algorithms are used to recreate a human
ability like adding a caption to a photo, driving a car, or playing a game.
While the human brain has long served as a source of inspiration for machine
learning, li... | computer science |
27,399 | Global and Local Information Based Deep Network for Skin Lesion
Segmentation | cs.CV | With a large influx of dermoscopy images and a growing shortage of
dermatologists, automatic dermoscopic image analysis plays an essential role in
skin cancer diagnosis. In this paper, a new deep fully convolutional neural
network (FCNN) is proposed to automatically segment melanoma out of skin images
by end-to-end lea... | computer science |
27,400 | Convolutional Neural Network on Three Orthogonal Planes for Dynamic
Texture Classification | cs.CV | Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote se... | computer science |
27,401 | From visual words to a visual grammar: using language modelling for
image classification | cs.CV | The Bag--of--Visual--Words (BoVW) is a visual description technique that aims
at shortening the semantic gap by partitioning a low--level feature space into
regions of the feature space that potentially correspond to visual concepts and
by giving more value to this space. In this paper we present a conceptual
analysis ... | computer science |
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