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27,002 | Pruned non-local means | cs.CV | In Non-Local Means (NLM), each pixel is denoised by performing a weighted
averaging of its neighboring pixels, where the weights are computed using image
patches. We demonstrate that the denoising performance of NLM can be improved
by pruning the neighboring pixels, namely, by rejecting neighboring pixels
whose weights... | computer science |
27,003 | Face Detection using Deep Learning: An Improved Faster RCNN Approach | cs.CV | In this report, we present a new face detection scheme using deep learning
and achieve the state-of-the-art detection performance on the well-known FDDB
face detetion benchmark evaluation. In particular, we improve the
state-of-the-art faster RCNN framework by combining a number of strategies,
including feature concate... | computer science |
27,004 | Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted
Representations | cs.CV | We propose a novel tree classification system called Treelogy, that fuses
deep representations with hand-crafted features obtained from leaf images to
perform leaf-based plant classification. Key to this system are segmentation of
the leaf from an untextured background, using convolutional neural networks
(CNNs) for le... | computer science |
27,005 | Pooling Facial Segments to Face: The Shallow and Deep Ends | cs.CV | Generic face detection algorithms do not perform very well in the mobile
domain due to significant presence of occluded and partially visible faces. One
promising technique to handle the challenge of partial faces is to design face
detectors based on facial segments. In this paper two such face detectors
namely, SegFac... | computer science |
27,006 | Supervised Deep Sparse Coding Networks | cs.CV | In this paper, we describe the deep sparse coding network (SCN), a novel deep
network that encodes intermediate representations with nonnegative sparse
coding. The SCN is built upon a number of cascading bottleneck modules, where
each module consists of two sparse coding layers with relatively wide and slim
dictionarie... | computer science |
27,007 | VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning
Problem | cs.CV | In this paper we present an on-manifold sequence-to-sequence learning
approach to motion estimation using visual and inertial sensors. It is to the
best of our knowledge the first end-to-end trainable method for visual-inertial
odometry which performs fusion of the data at an intermediate
feature-representation level. ... | computer science |
27,008 | MSCM-LiFe: Multi-scale cross modal linear feature for horizon detection
in maritime images | cs.CV | This paper proposes a new method for horizon detection called the multi-scale
cross modal linear feature. This method integrates three different concepts
related to the presence of horizon in maritime images to increase the accuracy
of horizon detection. Specifically it uses the persistence of horizon in
multi-scale me... | computer science |
27,009 | The HASYv2 dataset | cs.CV | This paper describes the HASYv2 dataset. HASY is a publicly available, free
of charge dataset of single symbols similar to MNIST. It contains 168233
instances of 369 classes. HASY contains two challenges: A classification
challenge with 10 pre-defined folds for 10-fold cross-validation and a
verification challenge. | computer science |
27,010 | Faceness-Net: Face Detection through Deep Facial Part Responses | cs.CV | We propose a deep convolutional neural network (CNN) for face detection
leveraging on facial attributes based supervision. We observe a phenomenon that
part detectors emerge within CNN trained to classify attributes from uncropped
face images, without any explicit part supervision. The observation motivates a
new metho... | computer science |
27,011 | Re-ranking Person Re-identification with k-reciprocal Encoding | cs.CV | When considering person re-identification (re-ID) as a retrieval process,
re-ranking is a critical step to improve its accuracy. Yet in the re-ID
community, limited effort has been devoted to re-ranking, especially those
fully automatic, unsupervised solutions. In this paper, we propose a
k-reciprocal encoding method t... | computer science |
27,012 | CNN as Guided Multi-layer RECOS Transform | cs.CV | There is a resurging interest in developing a neural-network-based solution
to the supervised machine learning problem. The convolutional neural network
(CNN) will be studied in this note. To begin with, we introduce a RECOS
transform as a basic building block of CNNs. The "RECOS" is an acronym for
"REctified-COrrelati... | computer science |
27,013 | A Survey of Structure from Motion | cs.CV | The structure from motion (SfM) problem in computer vision is the problem of
recovering the three-dimensional ($3$D) structure of a stationary scene from a
set of projective measurements, represented as a collection of two-dimensional
($2$D) images, via estimation of motion of the cameras corresponding to these
images.... | computer science |
27,014 | Language Independent Single Document Image Super-Resolution using CNN
for improved recognition | cs.CV | Recognition of document images have important applications in restoring old
and classical texts. The problem involves quality improvement before passing it
to a properly trained OCR to get accurate recognition of the text. The image
enhancement and quality improvement constitute important steps as subsequent
recognitio... | computer science |
27,015 | 3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking
(IF/SR) | cs.CV | A novel solution for the content-based 3D shape retrieval problem using an
unsupervised clustering approach, which does not need any label information of
3D shapes, is presented in this work. The proposed shape retrieval system
consists of two modules in cascade: the irrelevance filtering (IF) module and
the similarity... | computer science |
27,016 | Feature Selection based on PCA and PSO for Multimodal Medical Image
Fusion using DTCWT | cs.CV | Multimodal medical image fusion helps to increase efficiency in medical
diagnosis. This paper presents multimodal medical image fusion by selecting
relevant features using Principle Component Analysis (PCA) and Particle Swarm
Optimization techniques (PSO). DTCWT is used for decomposition of the images
into low and high... | computer science |
27,017 | Co-segmentation for Space-Time Co-located Collections | cs.CV | We present a co-segmentation technique for space-time co-located image
collections. These prevalent collections capture various dynamic events,
usually by multiple photographers, and may contain multiple co-occurring
objects which are not necessarily part of the intended foreground object,
resulting in ambiguities for ... | computer science |
27,018 | Supervised Learning in Automatic Channel Selection for Epileptic Seizure
Detection | cs.CV | Detecting seizure using brain neuroactivations recorded by intracranial
electroencephalogram (iEEG) has been widely used for monitoring, diagnosing,
and closed-loop therapy of epileptic patients, however, computational
efficiency gains are needed if state-of-the-art methods are to be implemented
in implanted devices. W... | computer science |
27,019 | Deep Multitask Architecture for Integrated 2D and 3D Human Sensing | cs.CV | We propose a deep multitask architecture for \emph{fully automatic 2d and 3d
human sensing} (DMHS), including \emph{recognition and reconstruction}, in
\emph{monocular images}. The system computes the figure-ground segmentation,
semantically identifies the human body parts at pixel level, and estimates the
2d and 3d po... | computer science |
27,020 | A novel method for automatic localization of joint area on knee plain
radiographs | cs.CV | Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed
from radiographic assessment after clinical examination. However, a visual
evaluation made by a practitioner suffers from subjectivity and is highly
dependent on the experience. Computer-aided diagnostics (CAD) could improve the
objectivity o... | computer science |
27,021 | A New Method for Removing the Moire' Pattern from Images | cs.CV | During the last decades, denoising methods have attracted much attention of
researchers. The conventional method for removing the Moire' pattern from
images is using notch filters in the Frequency-domain. In this paper a new
method is proposed that can achieve a better performance in comparison with the
traditional met... | computer science |
27,022 | DeepNav: Learning to Navigate Large Cities | cs.CV | We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for
navigating large cities using locally visible street-view images. The DeepNav
agent learns to reach its destination quickly by making the correct navigation
decisions at intersections. We collect a large-scale dataset of street-view
images org... | computer science |
27,023 | Spatial Aggregation of Holistically-Nested Convolutional Neural Networks
for Automated Pancreas Localization and Segmentation | cs.CV | Accurate and automatic organ segmentation from 3D radiological scans is an
important yet challenging problem for medical image analysis. Specifically, the
pancreas demonstrates very high inter-patient anatomical variability in both
its shape and volume. In this paper, we present an automated system using 3D
computed to... | computer science |
27,024 | Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow | cs.CV | Small flying robots can perform landing maneuvers using bio-inspired optical
flow by maintaining a constant divergence. However, optical flow is typically
estimated from frame sequences recorded by standard miniature cameras. This
requires processing full images on-board, limiting the update rate of
divergence measurem... | computer science |
27,025 | Denoising Hyperspectral Image with Non-i.i.d. Noise Structure | cs.CV | Hyperspectral image (HSI) denoising has been attracting much research
attention in remote sensing area due to its importance in improving the HSI
qualities. The existing HSI denoising methods mainly focus on specific spectral
and spatial prior knowledge in HSIs, and share a common underlying assumption
that the embedde... | computer science |
27,026 | High Order Stochastic Graphlet Embedding for Graph-Based Pattern
Recognition | cs.CV | Graph-based methods are known to be successful for pattern description and
comparison purpose. However, a lot of mathematical tools are unavailable in
graph domain, thus restricting the generic graph-based techniques to be
applicable within the machine learning framework. A way to tackle this problem
is graph embedding... | computer science |
27,027 | Design, Analysis and Application of A Volumetric Convolutional Neural
Network | cs.CV | The design, analysis and application of a volumetric convolutional neural
network (VCNN) are studied in this work. Although many CNNs have been proposed
in the literature, their design is empirical. In the design of the VCNN, we
propose a feed-forward K-means clustering algorithm to determine the filter
number and size... | computer science |
27,028 | A Kinematic Chain Space for Monocular Motion Capture | cs.CV | This paper deals with motion capture of kinematic chains (e.g. human
skeletons) from monocular image sequences taken by uncalibrated cameras. We
present a method based on projecting an observation into a kinematic chain
space (KCS). An optimization of the nuclear norm is proposed that implicitly
enforces structural pro... | computer science |
27,029 | Evolving Boxes for Fast Vehicle Detection | cs.CV | We perform fast vehicle detection from traffic surveillance cameras. A novel
deep learning framework, namely Evolving Boxes, is developed that proposes and
refines the object boxes under different feature representations. Specifically,
our framework is embedded with a light-weight proposal network to generate
initial a... | computer science |
27,030 | Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks | cs.CV | Object detection and segmentation represents the basis for many tasks in
computer and machine vision. In biometric recognition systems the detection of
the region-of-interest (ROI) is one of the most crucial steps in the overall
processing pipeline, significantly impacting the performance of the entire
recognition syst... | computer science |
27,031 | Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval | cs.CV | This paper addresses the problem of large scale image retrieval, with the aim
of accurately ranking the similarity of a large number of images to a given
query image. To achieve this, we propose a novel Siamese network. This network
consists of two computational strands, each comprising of a CNN component
followed by a... | computer science |
27,032 | Visual Saliency Prediction Using a Mixture of Deep Neural Networks | cs.CV | Visual saliency models have recently begun to incorporate deep learning to
achieve predictive capacity much greater than previous unsupervised methods.
However, most existing models predict saliency using local mechanisms limited
to the receptive field of the network. We propose a model that incorporates
global scene s... | computer science |
27,033 | Understanding trained CNNs by indexing neuron selectivity | cs.CV | The impressive performance and plasticity of convolutional neural networks to
solve different vision problems are shadowed by their black-box nature and its
consequent lack of full understanding. To reduce this gap we propose to
describe the activity of individual neurons by quantifying their inherent
selectivity to sp... | computer science |
27,034 | Product Graph-based Higher Order Contextual Similarities for Inexact
Subgraph Matching | cs.CV | Many algorithms formulate graph matching as an optimization of an objective
function of pairwise quantification of nodes and edges of two graphs to be
matched. Pairwise measurements usually consider local attributes but disregard
contextual information involved in graph structures. We address this issue by
proposing co... | computer science |
27,035 | Learning to Compose with Professional Photographs on the Web | cs.CV | Photo composition is an important factor affecting the aesthetics in
photography. However, it is a highly challenging task to model the aesthetic
properties of good compositions due to the lack of globally applicable rules to
the wide variety of photographic styles. Inspired by the thinking process of
photo taking, we ... | computer science |
27,036 | Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly
Optimizing Low-rank Matrix Completion and Integrability | cs.CV | We introduce a new, integrated approach to uncalibrated photometric stereo.
We perform 3D reconstruction of Lambertian objects using multiple images
produced by unknown, directional light sources. We show how to formulate a
single optimization that includes rank and integrability constraints, allowing
also for missing ... | computer science |
27,037 | Automating Image Analysis by Annotating Landmarks with Deep Neural
Networks | cs.CV | Image and video analysis is often a crucial step in the study of animal
behavior and kinematics. Often these analyses require that the position of one
or more animal landmarks are annotated (marked) in numerous images. The process
of annotating landmarks can require a significant amount of time and tedious
labor, which... | computer science |
27,038 | A Fast and Compact Saliency Score Regression Network Based on Fully
Convolutional Network | cs.CV | Visual saliency detection aims at identifying the most visually distinctive
parts in an image, and serves as a pre-processing step for a variety of
computer vision and image processing tasks. To this end, the saliency detection
procedure must be as fast and compact as possible and optimally processes input
images in a ... | computer science |
27,039 | Side Information in Robust Principal Component Analysis: Algorithms and
Applications | cs.CV | Robust Principal Component Analysis (RPCA) aims at recovering a low-rank
subspace from grossly corrupted high-dimensional (often visual) data and is a
cornerstone in many machine learning and computer vision applications. Even
though RPCA has been shown to be very successful in solving many rank
minimisation problems, ... | computer science |
27,040 | Learning a time-dependent master saliency map from eye-tracking data in
videos | cs.CV | To predict the most salient regions of complex natural scenes, saliency
models commonly compute several feature maps (contrast, orientation, motion...)
and linearly combine them into a master saliency map. Since feature maps have
different spatial distribution and amplitude dynamic ranges, determining their
contributio... | computer science |
27,041 | Handwritten Recognition Using SVM, KNN and Neural Network | cs.CV | Handwritten recognition (HWR) is the ability of a computer to receive and
interpret intelligible handwritten input from source such as paper documents,
photographs, touch-screens and other devices. In this paper we will using three
(3) classification t o re cognize the handwritten which is SVM, KNN and Neural
Network. | computer science |
27,042 | Maritime situational awareness using adaptive multi-sensor management
under hazy conditions | cs.CV | This paper presents a multi-sensor architecture with an adaptive multi-sensor
management system suitable for control and navigation of autonomous maritime
vessels in hazy and poor-visibility conditions. This architecture resides in
the autonomous maritime vessels. It augments the data from on-board imaging
sensors and ... | computer science |
27,043 | YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set
for Object Detection in Video | cs.CV | We introduce a new large-scale data set of video URLs with densely-sampled
object bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data
set consists of approximately 380,000 video segments about 19s long,
automatically selected to feature objects in natural settings without editing
or post-processing,... | computer science |
27,044 | Video Salient Object Detection via Fully Convolutional Networks | cs.CV | This paper proposes a deep learning model to efficiently detect salient
regions in videos. It addresses two important issues: (1) deep video saliency
model training with the absence of sufficiently large and pixel-wise annotated
video data, and (2) fast video saliency training and detection. The proposed
deep video sal... | computer science |
27,045 | Seeded Laplaican: An Eigenfunction Solution for Scribble Based
Interactive Image Segmentation | cs.CV | In this paper, we cast the scribble-based interactive image segmentation as a
semi-supervised learning problem. Our novel approach alleviates the need to
solve an expensive generalized eigenvector problem by approximating the
eigenvectors using efficiently computed eigenfunctions. The smoothness operator
defined on fea... | computer science |
27,046 | FCSS: Fully Convolutional Self-Similarity for Dense Semantic
Correspondence | cs.CV | We present a descriptor, called fully convolutional self-similarity (FCSS),
for dense semantic correspondence. To robustly match points among different
instances within the same object class, we formulate FCSS using local
self-similarity (LSS) within a fully convolutional network. In contrast to
existing CNN-based desc... | computer science |
27,047 | A method of limiting performance loss of CNNs in noisy environments | cs.CV | Convolutional Neural Network (CNN) recognition rates drop in the presence of
noise. We demonstrate a novel method of counteracting this drop in recognition
rate by adjusting the biases of the neurons in the convolutional layers
according to the noise conditions encountered at runtime. We compare our
technique to traini... | computer science |
27,048 | An Analysis of 1-to-First Matching in Iris Recognition | cs.CV | Iris recognition systems are a mature technology that is widely used
throughout the world. In identification (as opposed to verification) mode, an
iris to be recognized is typically matched against all N enrolled irises. This
is the classic "1-to-N search". In order to improve the speed of large-scale
identification, a... | computer science |
27,049 | Large-scale Image Geo-Localization Using Dominant Sets | cs.CV | This paper presents a new approach for the challenging problem of
geo-locating an image using image matching in a structured database of
city-wide reference images with known GPS coordinates. We cast the
geo-localization as a clustering problem on local image features. Akin to
existing approaches on the problem, our fr... | computer science |
27,050 | Wide-Residual-Inception Networks for Real-time Object Detection | cs.CV | Since convolutional neural network(CNN)models emerged,several tasks in
computer vision have actively deployed CNN models for feature extraction.
However,the conventional CNN models have a high computational cost and require
high memory capacity, which is impractical and unaffordable for commercial
applications such as ... | computer science |
27,051 | Towards Unsupervised Weed Scouting for Agricultural Robotics | cs.CV | Weed scouting is an important part of modern integrated weed management but
can be time consuming and sparse when performed manually. Automated weed
scouting and weed destruction has typically been performed using classification
systems able to classify a set group of species known a priori. This greatly
limits deploya... | computer science |
27,052 | Using Complex Wavelet Transform and Bilateral Filtering for Image
Denoising | cs.CV | The bilateral filter is a useful nonlinear filter which without smoothing
edges, it does spatial averaging. In the literature, the effectiveness of this
method for image denoising is shown. In this paper, an extension of this method
is proposed which is based on complex wavelet transform. In fact, the bilateral
filteri... | computer science |
27,053 | Gender-From-Iris or Gender-From-Mascara? | cs.CV | Predicting a person's gender based on the iris texture has been explored by
several researchers. This paper considers several dimensions of experimental
work on this problem, including person-disjoint train and test, and the effect
of cosmetics on eyelash occlusion and imperfect segmentation. We also consider
the use o... | computer science |
27,054 | Fast and easy blind deblurring using an inverse filter and PROBE | cs.CV | PROBE (Progressive Removal of Blur Residual) is a recursive framework for
blind deblurring. Using the elementary modified inverse filter at its core,
PROBE's experimental performance meets or exceeds the state of the art, both
visually and quantitatively. Remarkably, PROBE lends itself to analysis that
reveals its conv... | computer science |
27,055 | An Experimental Study of Deep Convolutional Features For Iris
Recognition | cs.CV | Iris is one of the popular biometrics that is widely used for identity
authentication. Different features have been used to perform iris recognition
in the past. Most of them are based on hand-crafted features designed by
biometrics experts. Due to tremendous success of deep learning in computer
vision problems, there ... | computer science |
27,056 | Entropy-guided Retinex anisotropic diffusion algorithm based on partial
differential equations (PDE) for illumination correction | cs.CV | This report describes the experimental results obtained using a proposed
variational Retinex algorithm for controlled illumination correction. Two
colour restoration and enhancement schemes of the algorithm are presented for
drastically improved results. The algorithm modifies the reflectance image
using global and loc... | computer science |
27,057 | Relative Camera Pose Estimation Using Convolutional Neural Networks | cs.CV | This paper presents a convolutional neural network based approach for
estimating the relative pose between two cameras. The proposed network takes
RGB images from both cameras as input and directly produces the relative
rotation and translation as output. The system is trained in an end-to-end
manner utilising transfer... | computer science |
27,058 | Robust features for facial action recognition | cs.CV | Automatic recognition of facial gestures is becoming increasingly important
as real world AI agents become a reality. In this paper, we present an
automated system that recognizes facial gestures by capturing local changes and
encoding the motion into a histogram of frequencies. We evaluate the proposed
method by demon... | computer science |
27,059 | Printed Arabic Text Recognition using Linear and Nonlinear Regression | cs.CV | Arabic language is one of the most popular languages in the world. Hundreds
of millions of people in many countries around the world speak Arabic as their
native speaking. However, due to complexity of Arabic language, recognition of
printed and handwritten Arabic text remained untouched for a very long time
compared w... | computer science |
27,060 | Attentional Network for Visual Object Detection | cs.CV | We propose augmenting deep neural networks with an attention mechanism for
the visual object detection task. As perceiving a scene, humans have the
capability of multiple fixation points, each attended to scene content at
different locations and scales. However, such a mechanism is missing in the
current state-of-the-a... | computer science |
27,061 | Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under
Natural Illumination | cs.CV | In this paper we present a novel approach for depth map enhancement from an
RGB-D video sequence. The basic idea is to exploit the shading information in
the color image. Instead of making assumption about surface albedo or
controlled object motion and lighting, we use the lighting variations
introduced by casual objec... | computer science |
27,062 | Designing Deep Convolutional Neural Networks for Continuous Object
Orientation Estimation | cs.CV | Deep Convolutional Neural Networks (DCNN) have been proven to be effective
for various computer vision problems. In this work, we demonstrate its
effectiveness on a continuous object orientation estimation task, which
requires prediction of 0 to 360 degrees orientation of the objects. We do so by
proposing and comparin... | computer science |
27,063 | Challenge of Multi-Camera Tracking | cs.CV | Multi-camera tracking is quite different from single camera tracking, and it
faces new technology and system architecture challenges. By analyzing the
corresponding characteristics and disadvantages of the existing algorithms,
problems in multi-camera tracking are summarized and some new directions for
future work are ... | computer science |
27,064 | Slice-to-volume medical image registration: a survey | cs.CV | During the last decades, the research community of medical imaging has
witnessed continuous advances in image registration methods, which pushed the
limits of the state-of-the-art and enabled the development of novel medical
procedures. A particular type of image registration problem, known as
slice-to-volume registrat... | computer science |
27,065 | Concurrent Activity Recognition with Multimodal CNN-LSTM Structure | cs.CV | We introduce a system that recognizes concurrent activities from real-world
data captured by multiple sensors of different types. The recognition is
achieved in two steps. First, we extract spatial and temporal features from the
multimodal data. We feed each datatype into a convolutional neural network that
extracts sp... | computer science |
27,066 | A Deep Convolutional Neural Network for Background Subtraction | cs.CV | In this work, we present a novel background subtraction system that uses a
deep Convolutional Neural Network (CNN) to perform the segmentation. With this
approach, feature engineering and parameter tuning become unnecessary since the
network parameters can be learned from data by training a single CNN that can
handle v... | computer science |
27,067 | A New Point-set Registration Algorithm for Fingerprint Matching | cs.CV | A novel minutia-based fingerprint matching algorithm is proposed that employs
iterative global alignment on two minutia sets. The matcher considers all
possible minutia pairings and iteratively aligns the two sets until the number
of minutia pairs does not exceed the maximum number of allowable one-to-one
pairings. The... | computer science |
27,068 | Hashing in the Zero Shot Framework with Domain Adaptation | cs.CV | Techniques to learn hash codes which can store and retrieve large dimensional
multimedia data efficiently have attracted broad research interests in the
recent years. With rapid explosion of newly emerged concepts and online data,
existing supervised hashing algorithms suffer from the problem of scarcity of
ground trut... | computer science |
27,069 | Image Reconstruction using Matched Wavelet Estimated from Data Sensed
Compressively using Partial Canonical Identity Matrix | cs.CV | This paper proposes a joint framework wherein lifting-based, separable,
image-matched wavelets are estimated from compressively sensed (CS) images and
used for the reconstruction of the same. Matched wavelet can be easily designed
if full image is available. Also matched wavelet may provide better
reconstruction result... | computer science |
27,070 | Face Aging With Conditional Generative Adversarial Networks | cs.CV | It has been recently shown that Generative Adversarial Networks (GANs) can
produce synthetic images of exceptional visual fidelity. In this work, we
propose the GAN-based method for automatic face aging. Contrary to previous
works employing GANs for altering of facial attributes, we make a particular
emphasize on prese... | computer science |
27,071 | Tracking using Numerous Anchor points | cs.CV | In this paper, an online adaptive model-free tracker is proposed to track
single objects in video sequences to deal with real-world tracking challenges
like low-resolution, object deformation, occlusion and motion blur. The novelty
lies in the construction of a strong appearance model that captures features
from the in... | computer science |
27,072 | An Implementation of Faster RCNN with Study for Region Sampling | cs.CV | We adapted the join-training scheme of Faster RCNN framework from Caffe to
TensorFlow as a baseline implementation for object detection. Our code is made
publicly available. This report documents the simplifications made to the
original pipeline, with justifications from ablation analysis on both PASCAL
VOC 2007 and CO... | computer science |
27,073 | Keyframe-Based Visual-Inertial Online SLAM with Relocalization | cs.CV | Complementing images with inertial measurements has become one of the most
popular approaches to achieve highly accurate and robust real-time camera pose
tracking. In this paper, we present a keyframe-based approach to
visual-inertial simultaneous localization and mapping (SLAM) for monocular and
stereo cameras. Our vi... | computer science |
27,074 | Automated Low-cost Terrestrial Laser Scanner for Measuring Diameters at
Breast Height and Heights of Forest Trees | cs.CV | Terrestrial laser scanner is a kind of fast, high-precision data acquisition
device, which had been more and more applied to the research areas of forest
inventory. In this study, a kind of automated low-cost terrestrial laser
scanner was designed and implemented based on a two-dimensional laser radar
sensor SICK LMS-5... | computer science |
27,075 | Guided Optical Flow Learning | cs.CV | We study the unsupervised learning of CNNs for optical flow estimation using
proxy ground truth data. Supervised CNNs, due to their immense learning
capacity, have shown superior performance on a range of computer vision
problems including optical flow prediction. They however require the ground
truth flow which is usu... | computer science |
27,076 | Multi-scale Convolutional Neural Networks for Crowd Counting | cs.CV | Crowd counting on static images is a challenging problem due to scale
variations. Recently deep neural networks have been shown to be effective in
this task. However, existing neural-networks-based methods often use the
multi-column or multi-network model to extract the scale-relevant features,
which is more complicate... | computer science |
27,077 | An Adversarial Regularisation for Semi-Supervised Training of Structured
Output Neural Networks | cs.CV | We propose a method for semi-supervised training of structured-output neural
networks. Inspired by the framework of Generative Adversarial Networks (GAN),
we train a discriminator network to capture the notion of a quality of network
output. To this end, we leverage the qualitative difference between outputs
obtained o... | computer science |
27,078 | Scene-adapted plug-and-play algorithm with convergence guarantees | cs.CV | Recent frameworks, such as the so-called plug-and-play, allow us to leverage
the developments in image denoising to tackle other, and more involved,
problems in image processing. As the name suggests, state-of-the-art denoisers
are plugged into an iterative algorithm that alternates between a denoising
step and the inv... | computer science |
27,079 | Region Ensemble Network: Improving Convolutional Network for Hand Pose
Estimation | cs.CV | Hand pose estimation from monocular depth images is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional methods is not so apparent. To promote the
performance ... | computer science |
27,080 | Computational Techniques in Multispectral Image Processing: Application
to the Syriac Galen Palimpsest | cs.CV | Multispectral and hyperspectral image analysis has experienced much
development in the last decade. The application of these methods to palimpsests
has produced significant results, enabling researchers to recover texts that
would be otherwise lost under the visible overtext, by improving the contrast
between the under... | computer science |
27,081 | Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest
Neighbour Fields | cs.CV | This paper presents a robust and efficient semi-dense visual odometry
solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline
which estimates the pose of the sensor by registering the projection of a 3D
semi-dense map of the reference frame with the 2D semi-dense region extracted
in the current frame... | computer science |
27,082 | Soft Biometrics: Gender Recognition from Unconstrained Face Images using
Local Feature Descriptor | cs.CV | Gender recognition from unconstrained face images is a challenging task due
to the high degree of misalignment, pose, expression, and illumination
variation. In previous works, the recognition of gender from unconstrained face
images is approached by utilizing image alignment, exploiting multiple samples
per individual... | computer science |
27,083 | Backpropagation Training for Fisher Vectors within Neural Networks | cs.CV | Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local
descriptors like SIFT features. They already show good performance in
combination with shallow learning architectures on visual recognitions tasks.
Current methods using FV as a feature descriptor in deep architectures assume
that all origina... | computer science |
27,084 | Semi-Supervised Deep Learning for Monocular Depth Map Prediction | cs.CV | Supervised deep learning often suffers from the lack of sufficient training
data. Specifically in the context of monocular depth map prediction, it is
barely possible to determine dense ground truth depth images in realistic
dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
present in the d... | computer science |
27,085 | Predicting Privileged Information for Height Estimation | cs.CV | In this paper, we propose a novel regression-based method for employing
privileged information to estimate the height using human metrology. The actual
values of the anthropometric measurements are difficult to estimate accurately
using state-of-the-art computer vision algorithms. Hence, we use ratios of
anthropometric... | computer science |
27,086 | Effective face landmark localization via single deep network | cs.CV | In this paper, we propose a novel face alignment method using single deep
network (SDN) on existing limited training data. Rather than using a
max-pooling layer followed one convolutional layer in typical convolutional
neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group
layer contains two co... | computer science |
27,087 | L1-regularized Reconstruction Error as Alpha Matte | cs.CV | Sampling-based alpha matting methods have traditionally followed the
compositing equation to estimate the alpha value at a pixel from a pair of
foreground (F) and background (B) samples. The (F,B) pair that produces the
least reconstruction error is selected, followed by alpha estimation. The
significance of that resid... | computer science |
27,088 | On-the-Fly Adaptation of Regression Forests for Online Camera
Relocalisation | cs.CV | Camera relocalisation is an important problem in computer vision, with
applications in simultaneous localisation and mapping, virtual/augmented
reality and navigation. Common techniques either match the current image
against keyframes with known poses coming from a tracker, or establish 2D-to-3D
correspondences between... | computer science |
27,089 | Attribute-controlled face photo synthesis from simple line drawing | cs.CV | Face photo synthesis from simple line drawing is a one-to-many task as simple
line drawing merely contains the contour of human face. Previous exemplar-based
methods are over-dependent on the datasets and are hard to generalize to
complicated natural scenes. Recently, several works utilize deep neural
networks to incre... | computer science |
27,090 | EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial
Action Unit Detection | cs.CV | In this paper, we propose a deep learning based approach for facial action
unit detection by enhancing and cropping the regions of interest. The approach
is implemented by adding two novel nets (layers): the enhancing layers and the
cropping layers, to a pretrained CNN model. For the enhancing layers, we
designed an at... | computer science |
27,091 | A large comparison of feature-based approaches for buried target
classification in forward-looking ground-penetrating radar | cs.CV | Forward-looking ground-penetrating radar (FLGPR) has recently been
investigated as a remote sensing modality for buried target detection (e.g.,
landmines). In this context, raw FLGPR data is beamformed into images and then
computerized algorithms are applied to automatically detect subsurface buried
targets. Most exist... | computer science |
27,092 | A New Rank Constraint on Multi-view Fundamental Matrices, and its
Application to Camera Location Recovery | cs.CV | Accurate estimation of camera matrices is an important step in structure from
motion algorithms. In this paper we introduce a novel rank constraint on
collections of fundamental matrices in multi-view settings. We show that in
general, with the selection of proper scale factors, a matrix formed by
stacking fundamental ... | computer science |
27,093 | Reconstruction-Based Disentanglement for Pose-invariant Face Recognition | cs.CV | Deep neural networks (DNNs) trained on large-scale datasets have recently
achieved impressive improvements in face recognition. But a persistent
challenge remains to develop methods capable of handling large pose variations
that are relatively underrepresented in training data. This paper presents a
method for learning... | computer science |
27,094 | Graph Fourier Transform with Negative Edges for Depth Image Coding | cs.CV | Recent advent in graph signal processing (GSP) has led to the development of
new graph-based transforms and wavelets for image / video coding, where the
underlying graph describes inter-pixel correlations. In this paper, we develop
a new transform called signed graph Fourier transform (SGFT), where the
underlying graph... | computer science |
27,095 | Texture Characterization by Using Shape Co-occurrence Patterns | cs.CV | Texture characterization is a key problem in image understanding and pattern
recognition. In this paper, we present a flexible shape-based texture
representation using shape co-occurrence patterns. More precisely, texture
images are first represented by tree of shapes, each of which is associated
with several geometric... | computer science |
27,096 | A clustering approach to heterogeneous change detection | cs.CV | Change detection in heterogeneous multitemporal satellite images is a
challenging and still not much studied topic in remote sensing and earth
observation. This paper focuses on comparison of image pairs covering the same
geographical area and acquired by two different sensors, one optical radiometer
and one synthetic ... | computer science |
27,097 | Dual-Tree Wavelet Scattering Network with Parametric Log Transformation
for Object Classification | cs.CV | We introduce a ScatterNet that uses a parametric log transformation with
Dual-Tree complex wavelets to extract translation invariant representations
from a multi-resolution image. The parametric transformation aids the OLS
pruning algorithm by converting the skewed distributions into relatively
mean-symmetric distribut... | computer science |
27,098 | Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal
Classification | cs.CV | This paper introduces a Deep Scattering network that utilizes Dual-Tree
complex wavelets to extract translation invariant representations from an input
signal. The computationally efficient Dual-Tree wavelets decompose the input
signal into densely spaced representations over scales. Translation invariance
is introduce... | computer science |
27,099 | Enhanced Local Binary Patterns for Automatic Face Recognition | cs.CV | This paper presents a novel automatic face recognition approach based on
local binary patterns (LBP). LBP descriptor considers a local neighbourhood of
a pixel to compute the features. This method is not very robust to handle image
noise, variances and different illumination conditions. In this paper, we
address these ... | computer science |
27,100 | Reverse Classification Accuracy: Predicting Segmentation Performance in
the Absence of Ground Truth | cs.CV | When integrating computational tools such as automatic segmentation into
clinical practice, it is of utmost importance to be able to assess the level of
accuracy on new data, and in particular, to detect when an automatic method
fails. However, this is difficult to achieve due to absence of ground truth.
Segmentation a... | computer science |
27,101 | ArtGAN: Artwork Synthesis with Conditional Categorical GANs | cs.CV | This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room i... | computer science |
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