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26,102 | A Study of Vision based Human Motion Recognition and Analysis | cs.CV | Vision based human motion recognition has fascinated many researchers due to
its critical challenges and a variety of applications. The applications range
from simple gesture recognition to complicated behaviour understanding in
surveillance system. This leads to major development in the techniques related
to human mot... | computer science |
26,103 | In the Saddle: Chasing Fast and Repeatable Features | cs.CV | A novel similarity-covariant feature detector that extracts points whose
neighbourhoods, when treated as a 3D intensity surface, have a saddle-like
intensity profile. The saddle condition is verified efficiently by intensity
comparisons on two concentric rings that must have exactly two dark-to-bright
and two bright-to... | computer science |
26,104 | Absolute Pose Estimation from Line Correspondences using Direct Linear
Transformation | cs.CV | This work is concerned with camera pose estimation from correspondences of
3D/2D lines, i. e. with the Perspective-n-Line (PnL) problem. We focus on large
line sets, which can be efficiently solved by methods using linear formulation
of PnL. We propose a novel method "DLT-Combined-Lines" based on the Direct
Linear Tran... | computer science |
26,105 | A 4D Light-Field Dataset and CNN Architectures for Material Recognition | cs.CV | We introduce a new light-field dataset of materials, and take advantage of
the recent success of deep learning to perform material recognition on the 4D
light-field. Our dataset contains 12 material categories, each with 100 images
taken with a Lytro Illum, from which we extract about 30,000 patches in total.
To the be... | computer science |
26,106 | Ambient Sound Provides Supervision for Visual Learning | cs.CV | The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a stat... | computer science |
26,107 | Sympathy for the Details: Dense Trajectories and Hybrid Classification
Architectures for Action Recognition | cs.CV | Action recognition in videos is a challenging task due to the complexity of
the spatio-temporal patterns to model and the difficulty to acquire and learn
on large quantities of video data. Deep learning, although a breakthrough for
image classification and showing promise for videos, has still not clearly
superseded ac... | computer science |
26,108 | Modeling and Propagating CNNs in a Tree Structure for Visual Tracking | cs.CV | We present an online visual tracking algorithm by managing multiple target
appearance models in a tree structure. The proposed algorithm employs
Convolutional Neural Networks (CNNs) to represent target appearances, where
multiple CNNs collaborate to estimate target states and determine the desirable
paths for online mo... | computer science |
26,109 | Fast Trajectory Simplification Algorithm for Natural User Interfaces in
Robot Programming by Demonstration | cs.CV | Trajectory simplification is a problem encountered in areas like Robot
programming by demonstration, CAD/CAM, computer vision, and in GPS-based
applications like traffic analysis. This problem entails reduction of the
points in a given trajectory while keeping the relevant points which preserve
important information. T... | computer science |
26,110 | Scalable Compression of Deep Neural Networks | cs.CV | Deep neural networks generally involve some layers with mil- lions of
parameters, making them difficult to be deployed and updated on devices with
limited resources such as mobile phones and other smart embedded systems. In
this paper, we propose a scalable representation of the network parameters, so
that different ap... | computer science |
26,111 | An Octree-Based Approach towards Efficient Variational Range Data Fusion | cs.CV | Volume-based reconstruction is usually expensive both in terms of memory
consumption and runtime. Especially for sparse geometric structures, volumetric
representations produce a huge computational overhead. We present an efficient
way to fuse range data via a variational Octree-based minimization approach by
taking th... | computer science |
26,112 | Mean Deviation Similarity Index: Efficient and Reliable Full-Reference
Image Quality Evaluator | cs.CV | Applications of perceptual image quality assessment (IQA) in image and video
processing, such as image acquisition, image compression, image restoration and
multimedia communication, have led to the development of many IQA metrics. In
this paper, a reliable full reference IQA model is proposed that utilize
gradient sim... | computer science |
26,113 | Who Leads the Clothing Fashion: Style, Color, or Texture? A
Computational Study | cs.CV | It is well known that clothing fashion is a distinctive and often habitual
trend in the style in which a person dresses. Clothing fashions are usually
expressed with visual stimuli such as style, color, and texture. However, it is
not clear which visual stimulus places higher/lower influence on the updating
of clothing... | computer science |
26,114 | Fine Hand Segmentation using Convolutional Neural Networks | cs.CV | We propose a method for extracting very accurate masks of hands in egocentric
views. Our method is based on a novel Deep Learning architecture: In contrast
with current Deep Learning methods, we do not use upscaling layers applied to a
low-dimensional representation of the input image. Instead, we extract features
with... | computer science |
26,115 | Mitosis Detection in Intestinal Crypt Images with Hough Forest and
Conditional Random Fields | cs.CV | Intestinal enteroendocrine cells secrete hormones that are vital for the
regulation of glucose metabolism but their differentiation from intestinal stem
cells is not fully understood. Asymmetric stem cell divisions have been linked
to intestinal stem cell homeostasis and secretory fate commitment. We monitored
cell div... | computer science |
26,116 | Spatio-temporal Aware Non-negative Component Representation for Action
Recognition | cs.CV | This paper presents a novel mid-level representation for action recognition,
named spatio-temporal aware non-negative component representation (STANNCR).
The proposed STANNCR is based on action component and incorporates the
spatial-temporal information. We first introduce a spatial-temporal
distribution vector (STDV) ... | computer science |
26,117 | Multi-Path Feedback Recurrent Neural Network for Scene Parsing | cs.CV | In this paper, we consider the scene parsing problem and propose a novel
Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene
images. MPF-RNN can enhance the capability of RNNs in modeling long-range
context information at multiple levels and better distinguish pixels that are
easy to confuse. Diffe... | computer science |
26,118 | 3D Object Proposals using Stereo Imagery for Accurate Object Class
Detection | cs.CV | The goal of this paper is to perform 3D object detection in the context of
autonomous driving. Our method first aims at generating a set of high-quality
3D object proposals by exploiting stereo imagery. We formulate the problem as
minimizing an energy function that encodes object size priors, placement of
objects on th... | computer science |
26,119 | Learning Temporal Transformations From Time-Lapse Videos | cs.CV | Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In par... | computer science |
26,120 | Cast and Self Shadow Segmentation in Video Sequences using Interval
based Eigen Value Representation | cs.CV | Tracking of motion objects in the surveillance videos is useful for the
monitoring and analysis. The performance of the surveillance system will
deteriorate when shadows are detected as moving objects. Therefore, shadow
detection and elimination usually benefits the next stages. To overcome this
issue, a method for det... | computer science |
26,121 | Total variation reconstruction for compressive sensing using nonlocal
Lagrangian multiplier | cs.CV | Total variation has proved its effectiveness in solving inverse problems for
compressive sensing. Besides, the nonlocal means filter used as regularization
preserves texture better for recovered images, but it is quite complex to
implement. In this paper, based on existence of both noise and image
information in the La... | computer science |
26,122 | Using k-nearest neighbors to construct cancelable minutiae templates | cs.CV | Fingerprint is widely used in a variety of applications. Security measures
have to be taken to protect the privacy of fingerprint data. Cancelable
biometrics is proposed as an effective mechanism of using and protecting
biometrics. In this paper we propose a new method of constructing cancelable
fingerprint template by... | computer science |
26,123 | Approaching the Computational Color Constancy as a Classification
Problem through Deep Learning | cs.CV | Computational color constancy refers to the problem of computing the
illuminant color so that the images of a scene under varying illumination can
be normalized to an image under the canonical illumination. In this paper, we
adopt a deep learning framework for the illumination estimation problem. The
proposed method wo... | computer science |
26,124 | Linking Image and Text with 2-Way Nets | cs.CV | Linking two data sources is a basic building block in numerous computer
vision problems. Canonical Correlation Analysis (CCA) achieves this by
utilizing a linear optimizer in order to maximize the correlation between the
two views. Recent work makes use of non-linear models, including deep learning
techniques, that opt... | computer science |
26,125 | Correspondence Insertion for As-Projective-As-Possible Image Stitching | cs.CV | Spatially varying warps are increasingly popular for image alignment. In
particular, as-projective-as-possible (APAP) warps have been proven effective
for accurate panoramic stitching, especially in cases with significant depth
parallax that defeat standard homographic warps. However, estimating spatially
varying warps... | computer science |
26,126 | PVANET: Deep but Lightweight Neural Networks for Real-time Object
Detection | cs.CV | This paper presents how we can achieve the state-of-the-art accuracy in
multi-category object detection task while minimizing the computational cost by
adapting and combining recent technical innovations. Following the common
pipeline of "CNN feature extraction + region proposal + RoI classification", we
mainly redesig... | computer science |
26,127 | Edge Preserving and Multi-Scale Contextual Neural Network for Salient
Object Detection | cs.CV | In this paper, we propose a novel edge preserving and multi-scale contextual
neural network for salient object detection. The proposed framework is aiming
to address two limits of the existing CNN based methods. First, region-based
CNN methods lack sufficient context to accurately locate salient object since
they deal ... | computer science |
26,128 | Curvature Integration in a 5D Kernel for Extracting Vessel Connections
in Retinal Images | cs.CV | Tree-like structures such as retinal images are widely studied in
computer-aided diagnosis systems for large-scale screening programs. Despite
several segmentation and tracking methods proposed in the literature, there
still exist several limitations specifically when two or more curvilinear
structures cross or bifurca... | computer science |
26,129 | Temporal Activity Detection in Untrimmed Videos with Recurrent Neural
Networks | cs.CV | This thesis explore different approaches using Convolutional and Recurrent
Neural Networks to classify and temporally localize activities on videos,
furthermore an implementation to achieve it has been proposed. As the first
step, features have been extracted from video frames using an state of the art
3D Convolutional... | computer science |
26,130 | ORBSLAM-based Endoscope Tracking and 3D Reconstruction | cs.CV | We aim to track the endoscope location inside the surgical scene and provide
3D reconstruction, in real-time, from the sole input of the image sequence
captured by the monocular endoscope. This information offers new possibilities
for developing surgical navigation and augmented reality applications. The main
benefit o... | computer science |
26,131 | Tracking Completion | cs.CV | A fundamental component of modern trackers is an online learned tracking
model, which is typically modeled either globally or locally. The two kinds of
models perform differently in terms of effectiveness and robustness under
different challenging situations. This work exploits the advantages of both
models. A subspace... | computer science |
26,132 | Real-Time Visual Tracking: Promoting the Robustness of Correlation
Filter Learning | cs.CV | Correlation filtering based tracking model has received lots of attention and
achieved great success in real-time tracking, however, the lost function in
current correlation filtering paradigm could not reliably response to the
appearance changes caused by occlusion and illumination variations. This study
intends to pr... | computer science |
26,133 | Temporal Convolutional Networks: A Unified Approach to Action
Segmentation | cs.CV | The dominant paradigm for video-based action segmentation is composed of two
steps: first, for each frame, compute low-level features using Dense
Trajectories or a Convolutional Neural Network that encode spatiotemporal
information locally, and second, input these features into a classifier that
captures high-level tem... | computer science |
26,134 | Construction of Convex Sets on Quadrilateral Ordered Tiles or Graphs
with Propagation Neighborhood Operations. Dales, Concavity Structures.
Application to Gray Image Analysis of Human-Readable Shapes | cs.CV | An effort has been made to show mathematicians some new ideas applied to
image analysis. Gray images are presented as tilings. Based on topological
properties of the tiling, a number of gray convex hulls: maximal, minimal, and
oriented ones are constructed and some are proved. They are constructed with
only one operati... | computer science |
26,135 | Utilizing Large Scale Vision and Text Datasets for Image Segmentation
from Referring Expressions | cs.CV | Image segmentation from referring expressions is a joint vision and language
modeling task, where the input is an image and a textual expression describing
a particular region in the image; and the goal is to localize and segment the
specific image region based on the given expression. One major difficulty to
train suc... | computer science |
26,136 | Egocentric Meets Top-view | cs.CV | Thanks to the availability and increasing popularity of Egocentric cameras
such as GoPro cameras, glasses, and etc. we have been provided with a plethora
of videos captured from the first person perspective. Surveillance cameras and
Unmanned Aerial Vehicles(also known as drones) also offer tremendous amount of
videos, ... | computer science |
26,137 | Low-rank Multi-view Clustering in Third-Order Tensor Space | cs.CV | The plenty information from multiple views data as well as the complementary
information among different views are usually beneficial to various tasks,
e.g., clustering, classification, de-noising. Multi-view subspace clustering is
based on the fact that the multi-view data are generated from a latent
subspace. To reco... | computer science |
26,138 | Multi-Class Multi-Object Tracking using Changing Point Detection | cs.CV | This paper presents a robust multi-class multi-object tracking (MCMOT)
formulated by a Bayesian filtering framework. Multi-object tracking for
unlimited object classes is conducted by combining detection responses and
changing point detection (CPD) algorithm. The CPD model is used to observe
abrupt or abnormal changes ... | computer science |
26,139 | New Methods to Improve Large-Scale Microscopy Image Analysis with Prior
Knowledge and Uncertainty | cs.CV | Multidimensional imaging techniques provide powerful ways to examine various
kinds of scientific questions. The routinely produced datasets in the
terabyte-range, however, can hardly be analyzed manually and require an
extensive use of automated image analysis. The present thesis introduces a new
concept for the estima... | computer science |
26,140 | Multi-Person Pose Estimation with Local Joint-to-Person Associations | cs.CV | Despite of the recent success of neural networks for human pose estimation,
current approaches are limited to pose estimation of a single person and cannot
handle humans in groups or crowds. In this work, we propose a method that
estimates the poses of multiple persons in an image in which a person can be
occluded by a... | computer science |
26,141 | A statistical model of tristimulus measurements within and between OLED
displays | cs.CV | We present an empirical model for noises in color measurements from OLED
displays. According to measured data the noise is not isotropic in the XYZ
space, instead most of the noise is along an axis that is parallel to a vector
from origin to measured XYZ vector. The presented empirical model is simple and
depends only ... | computer science |
26,142 | CliqueCNN: Deep Unsupervised Exemplar Learning | cs.CV | Exemplar learning is a powerful paradigm for discovering visual similarities
in an unsupervised manner. In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential. With only a single
positive sample, a great imbalance between one positive and many negatives, and
unreliabl... | computer science |
26,143 | Spatio-Colour Asplünd 's Metric and Logarithmic Image Processing for
Colour Images (LIPC) | cs.CV | Aspl\"und 's metric, which is useful for pattern matching, consists in a
double-sided probing, i.e. the over-graph and the sub-graph of a function are
probed jointly. This paper extends the Aspl\"und 's metric we previously
defined for colour and multivariate images using a marginal approach (i.e.
component by componen... | computer science |
26,144 | Efficient Two-Stream Motion and Appearance 3D CNNs for Video
Classification | cs.CV | The video and action classification have extremely evolved by deep neural
networks specially with two stream CNN using RGB and optical flow as inputs and
they present outstanding performance in terms of video analysis. One of the
shortcoming of these methods is handling motion information extraction which is
done out s... | computer science |
26,145 | Facial Surface Analysis using Iso-Geodesic Curves in Three Dimensional
Face Recognition System | cs.CV | In this paper, we present an automatic 3D face recognition system. This
system is based on the representation of human faces surfaces as collections of
Iso-Geodesic Curves (IGC) using 3D Fast Marching algorithm. To compare two
facial surfaces, we compute a geodesic distance between a pair of facial curves
using a Riema... | computer science |
26,146 | Measuring the Quality of Exercises | cs.CV | This work explores the problem of exercise quality measurement since it is
essential for effective management of diseases like cerebral palsy (CP). This
work examines the assessment of quality of large amplitude movement (LAM)
exercises designed to treat CP in an automated fashion. Exercise data was
collected by traine... | computer science |
26,147 | Attentional Push: Augmenting Salience with Shared Attention Modeling | cs.CV | We present a novel visual attention tracking technique based on Shared
Attention modeling. Our proposed method models the viewer as a participant in
the activity occurring in the scene. We go beyond image salience and instead of
only computing the power of an image region to pull attention to it, we also
consider the s... | computer science |
26,148 | Image segmentation based on histogram of depth and an application in
driver distraction detection | cs.CV | This study proposes an approach to segment human object from a depth image
based on histogram of depth values. The region of interest is first extracted
based on a predefined threshold for histogram regions. A region growing process
is then employed to separate multiple human bodies with the same depth
interval. Our co... | computer science |
26,149 | Grid Loss: Detecting Occluded Faces | cs.CV | Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, ... | computer science |
26,150 | Weakly Supervised PatchNets: Describing and Aggregating Local Patches
for Scene Recognition | cs.CV | Traditional feature encoding scheme (e.g., Fisher vector) with local
descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are
two classes of successful methods for image recognition. In this paper, we
propose a hybrid representation, which leverages the discriminative capacity of
CNNs and the simpli... | computer science |
26,151 | Transferring Object-Scene Convolutional Neural Networks for Event
Recognition in Still Images | cs.CV | Event recognition in still images is an intriguing problem and has potential
for real applications. This paper addresses the problem of event recognition by
proposing a convolutional neural network that exploits knowledge of objects and
scenes for event classification (OS2E-CNN). Intuitively, it stands to reason
that t... | computer science |
26,152 | Segmentation Free Object Discovery in Video | cs.CV | In this paper we present a simple yet effective approach to extend without
supervision any object proposal from static images to videos. Unlike previous
methods, these spatio-temporal proposals, to which we refer as tracks, are
generated relying on little or no visual content by only exploiting bounding
boxes spatial c... | computer science |
26,153 | Deep Learning Human Mind for Automated Visual Classification | cs.CV | What if we could effectively read the mind and transfer human visual
capabilities to computer vision methods? In this paper, we aim at addressing
this question by developing the first visual object classifier driven by human
brain signals. In particular, we employ EEG data evoked by visual object
stimuli combined with ... | computer science |
26,154 | Autonomous driving challenge: To Infer the property of a dynamic object
based on its motion pattern using recurrent neural network | cs.CV | In autonomous driving applications a critical challenge is to identify action
to take to avoid an obstacle on collision course. For example, when a heavy
object is suddenly encountered it is critical to stop the vehicle or change the
lane even if it causes other traffic disruptions. However,there are situations
when it... | computer science |
26,155 | Built-in Foreground/Background Prior for Weakly-Supervised Semantic
Segmentation | cs.CV | Pixel-level annotations are expensive and time consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recently, CNN-based methods have proposed to fine-tune
pre-trained networks using image tags. Without additional information, this
leads to poor loc... | computer science |
26,156 | Label distribution based facial attractiveness computation by deep
residual learning | cs.CV | Two challenges lie in the facial attractiveness computation research: the
lack of true attractiveness labels (scores), and the lack of an accurate face
representation. In order to address the first challenge, this paper recasts
facial attractiveness computation as a label distribution learning (LDL)
problem rather than... | computer science |
26,157 | Stochastic Learning of Multi-Instance Dictionary for Earth Mover's
Distance based Histogram Comparison | cs.CV | Dictionary plays an important role in multi-instance data representation. It
maps bags of instances to histograms. Earth mover's distance (EMD) is the most
effective histogram distance metric for the application of multi-instance
retrieval. However, up to now, there is no existing multi-instance dictionary
learning met... | computer science |
26,158 | Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and
Ensembles | cs.CV | Are we ready to segment consumer stereo videos? The amount of this data type
is rapidly increasing and encompasses rich information of appearance, motion
and depth cues. However, the segmentation of such data is still largely
unexplored. First, we propose therefore a new benchmark: videos, annotations
and metrics to me... | computer science |
26,159 | Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly
Detection in Crowded Scenes | cs.CV | The detection of abnormal behaviours in crowded scenes has to deal with many
challenges. This paper presents an efficient method for detection and
localization of anomalies in videos. Using fully convolutional neural networks
(FCNs) and temporal data, a pre-trained supervised FCN is transferred into an
unsupervised FCN... | computer science |
26,160 | Vanishing point detection with convolutional neural networks | cs.CV | Inspired by the finding that vanishing point (road tangent) guides driver's
gaze, in our previous work we showed that vanishing point attracts gaze during
free viewing of natural scenes as well as in visual search (Borji et al.,
Journal of Vision 2016). We have also introduced improved saliency models using
vanishing p... | computer science |
26,161 | Combining Fully Convolutional and Recurrent Neural Networks for 3D
Biomedical Image Segmentation | cs.CV | Segmentation of 3D images is a fundamental problem in biomedical image
analysis. Deep learning (DL) approaches have achieved state-of-the-art
segmentation perfor- mance. To exploit the 3D contexts using neural networks,
known DL segmentation methods, including 3D convolution, 2D convolution on
planes orthogonal to 2D i... | computer science |
26,162 | A Deep Multi-Level Network for Saliency Prediction | cs.CV | This paper presents a novel deep architecture for saliency prediction.
Current state of the art models for saliency prediction employ Fully
Convolutional networks that perform a non-linear combination of features
extracted from the last convolutional layer to predict saliency maps. We
propose an architecture which, ins... | computer science |
26,163 | Deep Retinal Image Understanding | cs.CV | This paper presents Deep Retinal Image Understanding (DRIU), a unified
framework of retinal image analysis that provides both retinal vessel and optic
disc segmentation. We make use of deep Convolutional Neural Networks (CNNs),
which have proven revolutionary in other fields of computer vision such as
object detection ... | computer science |
26,164 | Towards Automated Melanoma Screening: Exploring Transfer Learning
Schemes | cs.CV | Deep learning is the current bet for image classification. Its greed for huge
amounts of annotated data limits its usage in medical imaging context. In this
scenario transfer learning appears as a prominent solution. In this report we
aim to clarify how transfer learning schemes may influence classification
results. We... | computer science |
26,165 | UnrealCV: Connecting Computer Vision to Unreal Engine | cs.CV | Computer graphics can not only generate synthetic images and ground truth but
it also offers the possibility of constructing virtual worlds in which: (i) an
agent can perceive, navigate, and take actions guided by AI algorithms, (ii)
properties of the worlds can be modified (e.g., material and reflectance),
(iii) physi... | computer science |
26,166 | Depth Reconstruction and Computer-Aided Polyp Detection in Optical
Colonoscopy Video Frames | cs.CV | We present a computer-aided detection algorithm for polyps in optical
colonoscopy images. Polyps are the precursors to colon cancer. In the US alone,
more than 14 million optical colonoscopies are performed every year, mostly to
screen for polyps. Optical colonoscopy has been shown to have an approximately
25% polyp mi... | computer science |
26,167 | Vision-based Engagement Detection in Virtual Reality | cs.CV | User engagement modeling for manipulating actions in vision-based interfaces
is one of the most important case studies of user mental state detection. In a
Virtual Reality environment that employs camera sensors to recognize human
activities, we have to know when user intends to perform an action and when
not. Without ... | computer science |
26,168 | Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban
Reconstruction | cs.CV | Airborne acquisition and on-road mobile mapping provide complementary 3D
information of an urban landscape: the former acquires roof structures, ground,
and vegetation at a large scale, but lacks the facade and street-side details,
while the latter is incomplete for higher floors and often totally misses out
on pedestr... | computer science |
26,169 | Object Specific Deep Learning Feature and Its Application to Face
Detection | cs.CV | We present a method for discovering and exploiting object specific deep
learning features and use face detection as a case study. Motivated by the
observation that certain convolutional channels of a Convolutional Neural
Network (CNN) exhibit object specific responses, we seek to discover and
exploit the convolutional ... | computer science |
26,170 | Reconstructing Articulated Rigged Models from RGB-D Videos | cs.CV | Although commercial and open-source software exist to reconstruct a static
object from a sequence recorded with an RGB-D sensor, there is a lack of tools
that build rigged models of articulated objects that deform realistically and
can be used for tracking or animation. In this work, we fill this gap and
propose a meth... | computer science |
26,171 | An Adaptive Parameter Estimation for Guided Filter based Image
Deconvolution | cs.CV | Image deconvolution is still to be a challenging ill-posed problem for
recovering a clear image from a given blurry image, when the point spread
function is known. Although competitive deconvolution methods are numerically
impressive and approach theoretical limits, they are becoming more complex,
making analysis, and ... | computer science |
26,172 | Features Fusion for Classification of Logos | cs.CV | In this paper, a logo classification system based on the appearance of logo
images is proposed. The proposed classification system makes use of global
characteristics of logo images for classification. Color, texture, and shape of
a logo wholly describe the global characteristics of logo images. The various
combination... | computer science |
26,173 | Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain
Intensity Estimation | cs.CV | In this paper, we address the Multi-Instance-Learning (MIL) problem when bag
labels are naturally represented as ordinal variables (Multi--Instance--Ordinal
Regression). Moreover, we consider the case where bags are temporal sequences
of ordinal instances. To model this, we propose the novel Multi-Instance
Dynamic Ordi... | computer science |
26,174 | Joint Alignment of Multiple Point Sets with Batch and Incremental
Expectation-Maximization | cs.CV | This paper addresses the problem of registering multiple point sets.
Solutions to this problem are often approximated by repeatedly solving for
pairwise registration, which results in an uneven treatment of the sets forming
a pair: a model set and a data set. The main drawback of this strategy is that
the model set may... | computer science |
26,175 | Confidence-aware Levenberg-Marquardt optimization for joint motion
estimation and super-resolution | cs.CV | Motion estimation across low-resolution frames and the reconstruction of
high-resolution images are two coupled subproblems of multi-frame
super-resolution. This paper introduces a new joint optimization approach for
motion estimation and image reconstruction to address this interdependence. Our
method is formulated vi... | computer science |
26,176 | Best-Buddies Similarity - Robust Template Matching using Mutual Nearest
Neighbors | cs.CV | We propose a novel method for template matching in unconstrained
environments. Its essence is the Best-Buddies Similarity (BBS), a useful,
robust, and parameter-free similarity measure between two sets of points. BBS
is based on counting the number of Best-Buddies Pairs (BBPs)--pairs of points
in source and target sets... | computer science |
26,177 | Review of the Fingerprint Liveness Detection (LivDet) competition
series: 2009 to 2015 | cs.CV | A spoof attack, a subset of presentation attacks, is the use of an artificial
replica of a biometric in an attempt to circumvent a biometric sensor. Liveness
detection, or presentation attack detection, distinguishes between live and
fake biometric traits and is based on the principle that additional information
can be... | computer science |
26,178 | Improving Color Constancy by Discounting the Variation of Camera
Spectral Sensitivity | cs.CV | It is an ill-posed problem to recover the true scene colors from a color
biased image by discounting the effects of scene illuminant and camera spectral
sensitivity (CSS) at the same time. Most color constancy (CC) models have been
designed to first estimate the illuminant color, which is then removed from the
color bi... | computer science |
26,179 | Making a Case for Learning Motion Representations with Phase | cs.CV | This work advocates Eulerian motion representation learning over the current
standard Lagrangian optical flow model. Eulerian motion is well captured by
using phase, as obtained by decomposing the image through a complex-steerable
pyramid. We discuss the gain of Eulerian motion in a set of practical use
cases: (i) acti... | computer science |
26,180 | Human pose estimation via Convolutional Part Heatmap Regression | cs.CV | This paper is on human pose estimation using Convolutional Neural Networks.
Our main contribution is a CNN cascaded architecture specifically designed for
learning part relationships and spatial context, and robustly inferring pose
even for the case of severe part occlusions. To this end, we propose a
detection-followe... | computer science |
26,181 | Performance Measures and a Data Set for Multi-Target, Multi-Camera
Tracking | cs.CV | To help accelerate progress in multi-target, multi-camera tracking systems,
we present (i) a new pair of precision-recall measures of performance that
treats errors of all types uniformly and emphasizes correct identification over
sources of error; (ii) the largest fully-annotated and calibrated data set to
date with m... | computer science |
26,182 | A Boosting Method to Face Image Super-resolution | cs.CV | Recently sparse representation has gained great success in face image
super-resolution. The conventional sparsity-based methods enforce sparse coding
on face image patches and the representation fidelity is measured by
$\ell_{2}$-norm. Such a sparse coding model regularizes all facial patches
equally, which however ign... | computer science |
26,183 | Delaunay Triangulation on Skeleton of Flowers for Classification | cs.CV | In this work, we propose a Triangle based approach to classify flower images.
Initially, flowers are segmented using whorl based region merging segmentation.
Skeleton of a flower is obtained from the segmented flower using a skeleton
pruning method. The Delaunay triangulation is obtained from the endpoints and
junction... | computer science |
26,184 | Animal Classification System: A Block Based Approach | cs.CV | In this work, we propose a method for the classification of animal in images.
Initially, a graph cut based method is used to perform segmentation in order to
eliminate the background from the given image. The segmented animal images are
partitioned in to number of blocks and then the color texture moments are
extracted... | computer science |
26,185 | Guided Filter based Edge-preserving Image Non-blind Deconvolution | cs.CV | In this work, we propose a new approach for efficient edge-preserving image
deconvolution. Our algorithm is based on a novel type of explicit image filter
- guided filter. The guided filter can be used as an edge-preserving smoothing
operator like the popular bilateral filter, but has better behaviors near
edges. We pr... | computer science |
26,186 | Automatic Visual Theme Discovery from Joint Image and Text Corpora | cs.CV | A popular approach to semantic image understanding is to manually tag images
with keywords and then learn a mapping from vi- sual features to keywords.
Manually tagging images is a subjective pro- cess and the same or very similar
visual contents are often tagged with different keywords. Furthermore, not all
tags have ... | computer science |
26,187 | Polysemous codes | cs.CV | This paper considers the problem of approximate nearest neighbor search in
the compressed domain. We introduce polysemous codes, which offer both the
distance estimation quality of product quantization and the efficient
comparison of binary codes with Hamming distance. Their design is inspired by
algorithms introduced ... | computer science |
26,188 | Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review | cs.CV | Video capsule endoscopy (VCE) is used widely nowadays for visualizing the
gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as
an additional monitoring mechanism and can help in identifying polyps,
bleeding, etc. To analyze the large scale video data produced by VCE exams
automatic image proce... | computer science |
26,189 | A three-dimensional approach to Visual Speech Recognition using Discrete
Cosine Transforms | cs.CV | Visual speech recognition aims to identify the sequence of phonemes from
continuous speech. Unlike the traditional approach of using 2D image feature
extraction methods to derive features of each video frame separately, this
paper proposes a new approach using a 3D (spatio-temporal) Discrete Cosine
Transform to extract... | computer science |
26,190 | Object Tracking via Dynamic Feature Selection Processes | cs.CV | DFST proposes an optimized visual tracking algorithm based on the real-time
selection of locally and temporally discriminative features. A feature
selection mechanism is embedded in the Adaptive colour Names (CN) tracking
system that adaptively selects the top-ranked discriminative features for
tracking. DFST provides ... | computer science |
26,191 | Dense Motion Estimation for Smoke | cs.CV | Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. O... | computer science |
26,192 | Visual Saliency Detection Based on Multiscale Deep CNN Features | cs.CV | Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this paper, we discover that a
high-quality visual saliency model can be learned from multiscale features
extracted using deep convolutional neural networks (CNNs), which have had many
successes in visua... | computer science |
26,193 | Clearing the Skies: A deep network architecture for single-image rain
removal | cs.CV | We introduce a deep network architecture called DerainNet for removing rain
streaks from an image. Based on the deep convolutional neural network (CNN), we
directly learn the mapping relationship between rainy and clean image detail
layers from data. Because we do not possess the ground truth corresponding to
real-worl... | computer science |
26,194 | Optimizing Codes for Source Separation in Color Image Demosaicing and
Compressive Video Recovery | cs.CV | There exist several applications in image processing (eg: video compressed
sensing [Hitomi, Y. et al, "Video from a single coded exposure photograph using
a learned overcomplete dictionary"] and color image demosaicing [Moghadam, A.
A. et al, "Compressive Framework for Demosaicing of Natural Images"]) which
require sep... | computer science |
26,195 | Automated Segmentation of Retinal Layers from Optical Coherent
Tomography Images Using Geodesic Distance | cs.CV | Optical coherence tomography (OCT) is a non-invasive imaging technique that
can produce images of the eye at the microscopic level. OCT image segmentation
to localise retinal layer boundaries is a fundamental procedure for diagnosing
and monitoring the progression of retinal and optical nerve disorders. In this
paper, ... | computer science |
26,196 | Learning Action Concept Trees and Semantic Alignment Networks from
Image-Description Data | cs.CV | Action classification in still images has been a popular research topic in
computer vision. Labelling large scale datasets for action classification
requires tremendous manual work, which is hard to scale up. Besides, the action
categories in such datasets are pre-defined and vocabularies are fixed. However
humans may ... | computer science |
26,197 | Adaptive Regularization in Convex Composite Optimization for Variational
Imaging Problems | cs.CV | We propose an adaptive regularization scheme in a variational framework where
a convex composite energy functional is optimized. We consider a number of
imaging problems including denoising, segmentation and motion estimation, which
are considered as optimal solutions of the energy functionals that mainly
consist of da... | computer science |
26,198 | Ear-to-ear Capture of Facial Intrinsics | cs.CV | We present a practical approach to capturing ear-to-ear face models
comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular
albedo). Our approach is a hybrid of geometric and photometric methods and
requires no geometric calibration. Photometric measurements made in a
lightstage are used to estimate... | computer science |
26,199 | Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep
Learning | cs.CV | Melanoma is amongst most aggressive types of cancer. However, it is highly
curable if detected in its early stages. Prescreening of suspicious moles and
lesions for malignancy is of great importance. Detection can be done by images
captured by standard cameras, which are more preferable due to low cost and
availability... | computer science |
26,200 | End-to-End Eye Movement Detection Using Convolutional Neural Networks | cs.CV | Common computational methods for automated eye movement detection - i.e. the
task of detecting different types of eye movement in a continuous stream of
gaze data - are limited in that they either involve thresholding on
hand-crafted signal features, require individual detectors each only detecting
a single movement, o... | computer science |
26,201 | Quantifying Radiographic Knee Osteoarthritis Severity using Deep
Convolutional Neural Networks | cs.CV | This paper proposes a new approach to automatically quantify the severity of
knee osteoarthritis (OA) from radiographs using deep convolutional neural
networks (CNN). Clinically, knee OA severity is assessed using Kellgren \&
Lawrence (KL) grades, a five point scale. Previous work on automatically
predicting KL grades ... | computer science |
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