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29,302 | Gaussian Filter in CRF Based Semantic Segmentation | cs.CV | Artificial intelligence is making great changes in academy and industry with
the fast development of deep learning, which is a branch of machine learning
and statistical learning. Fully convolutional network [1] is the standard model
for semantic segmentation. Conditional random fields coded as CNN [2] or RNN
[3] and c... | computer science |
29,303 | Facial 3D Model Registration Under Occlusions With SensiblePoints-based
Reinforced Hypothesis Refinement | cs.CV | Registering a 3D facial model to a 2D image under occlusion is difficult.
First, not all of the detected facial landmarks are accurate under occlusions.
Second, the number of reliable landmarks may not be enough to constrain the
problem. We propose a method to synthesize additional points (SensiblePoints)
to create pos... | computer science |
29,304 | Learning Dense Facial Correspondences in Unconstrained Images | cs.CV | We present a minimalistic but effective neural network that computes dense
facial correspondences in highly unconstrained RGB images. Our network learns a
per-pixel flow and a matchability mask between 2D input photographs of a person
and the projection of a textured 3D face model. To train such a network, we
generate ... | computer science |
29,305 | Detection of Moving Object in Dynamic Background Using Gaussian
Max-Pooling and Segmentation Constrained RPCA | cs.CV | Due to its efficiency and stability, Robust Principal Component Analysis
(RPCA) has been emerging as a promising tool for moving object detection.
Unfortunately, existing RPCA based methods assume static or quasi-static
background, and thereby they may have trouble in coping with the background
scenes that exhibit a pe... | computer science |
29,306 | A Generative Model For Zero Shot Learning Using Conditional Variational
Autoencoders | cs.CV | Zero shot learning in Image Classification refers to the setting where images
from some novel classes are absent in the training data but other information
such as natural language descriptions or attribute vectors of the classes are
available. This setting is important in the real world since one may not be
able to ob... | computer science |
29,307 | Unsupervised feature learning with discriminative encoder | cs.CV | In recent years, deep discriminative models have achieved extraordinary
performance on supervised learning tasks, significantly outperforming their
generative counterparts. However, their success relies on the presence of a
large amount of labeled data. How can one use the same discriminative models
for learning useful... | computer science |
29,308 | Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor
Fusion | cs.CV | The use of 3D and stereo imaging is rapidly increasing. Compression,
transmission, and processing could degrade the quality of stereo images.
Quality assessment of such images is different than their 2D counterparts.
Metrics that represent 3D perception by human visual system (HVS) are expected
to assess stereoscopic q... | computer science |
29,309 | Human Detection and Tracking for Video Surveillance A Cognitive Science
Approach | cs.CV | With crimes on the rise all around the world, video surveillance is becoming
more important day by day. Due to the lack of human resources to monitor this
increasing number of cameras manually new computer vision algorithms to perform
lower and higher level tasks are being developed. We have developed a new
method inco... | computer science |
29,310 | Hand Gesture Real Time Paint Tool - Box | cs.CV | With current development universally in computing, now a days user
interaction approaches with mouse, keyboard, touch-pens etc. are not
sufficient. Directly using of hands or hand gestures as an input device is a
method to attract people with providing the applications, through Machine
Learning and Computer Vision. Hum... | computer science |
29,311 | Sushi Dish - Object detection and classification from real images | cs.CV | In conveyor belt sushi restaurants, billing is a burdened job because one has
to manually count the number of dishes and identify the color of them to
calculate the price. In a busy situation, there can be a mistake that customers
are overcharged or under-charged. To deal with this problem, we developed a
method that a... | computer science |
29,312 | Compressed Sensing MRI Reconstruction using a Generative Adversarial
Network with a Cyclic Loss | cs.CV | Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon
which the time-consuming MRI acquisition process can be accelerated. However,
it primarily relies on iterative numerical solvers which still hinders their
adaptation in time-critical applications. In addition, recent advances in deep
neural netwo... | computer science |
29,313 | Machine learning methods for histopathological image analysis | cs.CV | Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of... | computer science |
29,314 | Non-rigid image registration using fully convolutional networks with
deep self-supervision | cs.CV | We propose a novel non-rigid image registration algorithm that is built upon
fully convolutional networks (FCNs) to optimize and learn spatial
transformations between pairs of images to be registered. Different from most
existing deep learning based image registration methods that learn spatial
transformations from tra... | computer science |
29,315 | Hyperspectral Light Field Stereo Matching | cs.CV | In this paper, we describe how scene depth can be extracted using a
hyperspectral light field capture (H-LF) system. Our H-LF system consists of a
5 x 6 array of cameras, with each camera sampling a different narrow band in
the visible spectrum. There are two parts to extracting scene depth. The first
part is our novel... | computer science |
29,316 | Dataset Augmentation with Synthetic Images Improves Semantic
Segmentation | cs.CV | Although Deep Convolutional Neural Networks trained with strong pixel-level
annotations have significantly pushed the performance in semantic segmentation,
annotation efforts required for the creation of training data remains a
roadblock for further improvements. We show that augmentation of the weakly
annotated traini... | computer science |
29,317 | Self-Supervised Learning for Stereo Matching with Self-Improving Ability | cs.CV | Exiting deep-learning based dense stereo matching methods often rely on
ground-truth disparity maps as the training signals, which are however not
always available in many situations. In this paper, we design a simple
convolutional neural network architecture that is able to learn to compute
dense disparity maps direct... | computer science |
29,318 | ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial
Network | cs.CV | In recent years, there has been an increasing interest in image-based plant
phenotyping, applying state-of-the-art machine learning approaches to tackle
challenging problems, such as leaf segmentation (a multi-instance problem) and
counting. Most of these algorithms need labelled data to learn a model for the
task at h... | computer science |
29,319 | A Reproducible Study on Remote Heart Rate Measurement | cs.CV | This paper studies the problem of reproducible research in remote
photoplethysmography (rPPG). Most of the work published in this domain is
assessed on privately-owned databases, making it difficult to evaluate proposed
algorithms in a standard and principled manner. As a consequence, we present a
new, publicly availab... | computer science |
29,320 | Domain-adaptive deep network compression | cs.CV | Deep Neural Networks trained on large datasets can be easily transferred to
new domains with far fewer labeled examples by a process called fine-tuning.
This has the advantage that representations learned in the large source domain
can be exploited on smaller target domains. However, networks designed to be
optimal for... | computer science |
29,321 | To Learn or Not to Learn Features for Deformable Registration? | cs.CV | Feature-based registration has been popular with a variety of features
ranging from voxel intensity to Self-Similarity Context (SSC). In this paper,
we examine the question on how features learnt using various Deep Learning (DL)
frameworks can be used for deformable registration and whether this feature
learning is nec... | computer science |
29,322 | A Nonparametric Model for Multimodal Collaborative Activities
Summarization | cs.CV | Ego-centric data streams provide a unique opportunity to reason about joint
behavior by pooling data across individuals. This is especially evident in
urban environments teeming with human activities, but which suffer from
incomplete and noisy data. Collaborative human activities exhibit common
spatial, temporal, and v... | computer science |
29,323 | WESPE: Weakly Supervised Photo Enhancer for Digital Cameras | cs.CV | Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a w... | computer science |
29,324 | A Multilayer-Based Framework for Online Background Subtraction with
Freely Moving Cameras | cs.CV | The exponentially increasing use of moving platforms for video capture
introduces the urgent need to develop the general background subtraction
algorithms with the capability to deal with the moving background. In this
paper, we propose a multilayer-based framework for online background
subtraction for videos captured ... | computer science |
29,325 | Link the head to the "beak": Zero Shot Learning from Noisy Text
Description at Part Precision | cs.CV | In this paper, we study learning visual classifiers from unstructured text
descriptions at part precision with no training images. We propose a learning
framework that is able to connect text terms to its relevant parts and suppress
connections to non-visual text terms without any part-text annotations. For
instance, t... | computer science |
29,326 | Is human face processing a feature- or pattern-based task? Evidence
using a unified computational method driven by eye movements | cs.CV | Research on human face processing using eye movements has provided evidence
that we recognize face images successfully focusing our visual attention on a
few inner facial regions, mainly on the eyes, nose and mouth. To understand how
we accomplish this process of coding high-dimensional faces so efficiently,
this paper... | computer science |
29,327 | Multi-View Spectral Clustering via Structured Low-Rank Matrix
Factorization | cs.CV | Multi-view data clustering attracts more attention than their single view
counterparts due to the fact that leveraging multiple independent and
complementary information from multi-view feature spaces outperforms the single
one. Multi-view Spectral Clustering aims at yielding the data partition
agreement over their loc... | computer science |
29,328 | Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation | cs.CV | Large-scale image annotation is a challenging task in image content analysis,
which aims to annotate each image of a very large dataset with multiple class
labels. In this paper, we focus on two main issues in large-scale image
annotation: 1) how to learn stronger features for multifarious images; 2) how
to annotate an... | computer science |
29,329 | Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild | cs.CV | In order to retrieve unlabeled images by textual queries, cross-media
similarity computation is a key ingredient. Although novel methods are
continuously introduced, little has been done to evaluate these methods
together with large-scale query log analysis. Consequently, how far have these
methods brought us in answer... | computer science |
29,330 | Learning Non-Metric Visual Similarity for Image Retrieval | cs.CV | Can a neural network learn the concept of visual similarity? In this work,
this question is addressed by training a deep learning model for the specific
task of measuring the similarity between a pair of pictures in content-based
image retrieval datasets. Traditionally, content-based image retrieval systems
rely on two... | computer science |
29,331 | Visualizing and Improving Scattering Networks | cs.CV | Scattering Transforms (or ScatterNets) introduced by Mallat are a promising
start into creating a well-defined feature extractor to use for pattern
recognition and image classification tasks. They are of particular interest due
to their architectural similarity to Convolutional Neural Networks (CNNs),
while requiring n... | computer science |
29,332 | Predicting Visual Features from Text for Image and Video Caption
Retrieval | cs.CV | This paper strives to find amidst a set of sentences the one best describing
the content of a given image or video. Different from existing works, which
rely on a joint subspace for their image and video caption retrieval, we
propose to do so in a visual space exclusively. Apart from this conceptual
novelty, we contrib... | computer science |
29,333 | Towards social pattern characterization in egocentric photo-streams | cs.CV | Following the increasingly popular trend of social interaction analysis in
egocentric vision, this manuscript presents a comprehensive study for automatic
social pattern characterization of a wearable photo-camera user, by relying on
the visual analysis of egocentric photo-streams. The proposed framework
consists of th... | computer science |
29,334 | Dense Face Alignment | cs.CV | Face alignment is a classic problem in the computer vision field. Previous
works mostly focus on sparse alignment with a limited number of facial landmark
points, i.e., facial landmark detection. In this paper, for the first time, we
aim at providing a very dense 3D alignment for large-pose face images. To
achieve this... | computer science |
29,335 | The Devil is in the Tails: Fine-grained Classification in the Wild | cs.CV | The world is long-tailed. What does this mean for computer vision and visual
recognition? The main two implications are (1) the number of categories we need
to consider in applications can be very large, and (2) the number of training
examples for most categories can be very small. Current visual recognition
algorithms... | computer science |
29,336 | 6D Object Pose Estimation with Depth Images: A Seamless Approach for
Robotic Interaction and Augmented Reality | cs.CV | To determine the 3D orientation and 3D location of objects in the
surroundings of a camera mounted on a robot or mobile device, we developed two
powerful algorithms in object detection and temporal tracking that are combined
seamlessly for robotic perception and interaction as well as Augmented Reality
(AR). A separate... | computer science |
29,337 | Subspace Segmentation by Successive Approximations: A Method for
Low-Rank and High-Rank Data with Missing Entries | cs.CV | We propose a method to reconstruct and cluster incomplete high-dimensional
data lying in a union of low-dimensional subspaces. Exploring the sparse
representation model, we jointly estimate the missing data while imposing the
intrinsic subspace structure. Since we have a non-convex problem, we propose an
iterative meth... | computer science |
29,338 | Leveraging multiple datasets for deep leaf counting | cs.CV | The number of leaves a plant has is one of the key traits (phenotypes)
describing its development and growth. Here, we propose an automated, deep
learning based approach for counting leaves in model rosette plants. While
state-of-the-art results on leaf counting with deep learning methods have
recently been reported, t... | computer science |
29,339 | Squeeze-and-Excitation Networks | cs.CV | Convolutional neural networks are built upon the convolution operation, which
extracts informative features by fusing spatial and channel-wise information
together within local receptive fields. In order to boost the representational
power of a network, much existing work has shown the benefits of enhancing
spatial enc... | computer science |
29,340 | Improving Landmark Localization with Semi-Supervised Learning | cs.CV | We present two techniques to improve landmark localization from partially
annotated datasets. Our primary goal is to leverage the common situation where
precise landmark locations are only provided for a small data subset, but where
class labels for classification tasks related to the landmarks are more
abundantly avai... | computer science |
29,341 | Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease
using Hippocampal MRI data | cs.CV | Increasing effort in brain image analysis has been dedicated to early
diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing
studies have been focusing on binary classification problems, e.g.,
distinguishing AD patients from normal control (NC) elderly or mild cognitive
impairment (MCI) individ... | computer science |
29,342 | Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for
Multi-label Segmentation of Medical Images with Lesions | cs.CV | We introduce a dynamic multiscale tree (DMT) architecture that learns how to
leverage the strengths of different existing classifiers for supervised
multi-label image segmentation. Unlike previous works that simply aggregate or
cascade classifiers for addressing image segmentation and labeling tasks, we
propose to embe... | computer science |
29,343 | PageNet: Page Boundary Extraction in Historical Handwritten Documents | cs.CV | When digitizing a document into an image, it is common to include a
surrounding border region to visually indicate that the entire document is
present in the image. However, this border should be removed prior to automated
processing. In this work, we present a deep learning based system, PageNet,
which identifies the ... | computer science |
29,344 | Exploring and Exploiting Diversity for Image Segmentation | cs.CV | Semantic image segmentation is an important computer vision task that is
difficult because it consists of both recognition and segmentation. The task is
often cast as a structured output problem on an exponentially large
output-space, which is typically modeled by a discrete probabilistic model. The
best segmentation i... | computer science |
29,345 | Using Cross-Model EgoSupervision to Learn Cooperative Basketball
Intention | cs.CV | We present a first-person method for cooperative basketball intention
prediction: we predict with whom the camera wearer will cooperate in the near
future from unlabeled first-person images. This is a challenging task that
requires inferring the camera wearer's visual attention, and decoding the
social cues of other pl... | computer science |
29,346 | Deep Convolutional Neural Network for Age Estimation based on VGG-Face
Model | cs.CV | Automatic age estimation from real-world and unconstrained face images is
rapidly gaining importance. In our proposed work, a deep CNN model that was
trained on a database for face recognition task is used to estimate the age
information on the Adience database. This paper has three significant
contributions in this fi... | computer science |
29,347 | Group-level Emotion Recognition using Transfer Learning from Face
Identification | cs.CV | In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather... | computer science |
29,348 | A Compact Kernel Approximation for 3D Action Recognition | cs.CV | 3D action recognition was shown to benefit from a covariance representation
of the input data (joint 3D positions). A kernel machine feed with such feature
is an effective paradigm for 3D action recognition, yielding state-of-the-art
results. Yet, the whole framework is affected by the well-known scalability
issue. In ... | computer science |
29,349 | Blind image deblurring using class-adapted image priors | cs.CV | Blind image deblurring (BID) is an ill-posed inverse problem, usually
addressed by imposing prior knowledge on the (unknown) image and on the
blurring filter. Most of the work on BID has focused on natural images, using
image priors based on statistical properties of generic natural images.
However, in many application... | computer science |
29,350 | Detecting animals in African Savanna with UAVs and the crowds | cs.CV | Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large c... | computer science |
29,351 | Scene Text Recognition with Sliding Convolutional Character Models | cs.CV | Scene text recognition has attracted great interests from the computer vision
and pattern recognition community in recent years. State-of-the-art methods use
concolutional neural networks (CNNs), recurrent neural networks with long
short-term memory (RNN-LSTM) or the combination of them. In this paper, we
investigate t... | computer science |
29,352 | CNN-Based Projected Gradient Descent for Consistent Image Reconstruction | cs.CV | We present a new method for image reconstruction which replaces the projector
in a projected gradient descent (PGD) with a convolutional neural network
(CNN). CNNs trained as high-dimensional (image-to-image) regressors have
recently been used to efficiently solve inverse problems in imaging. However,
these approaches ... | computer science |
29,353 | Towards Automated Cadastral Boundary Delineation from UAV Data | cs.CV | Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire
land tenure data. UAVs can capture geospatial data at high quality and
resolution in a cost-effective, transparent and flexible manner, from which
visible land parcel boundaries, i.e., cadastral boundaries are delineable. This
delineation is ... | computer science |
29,354 | Soft Proposal Networks for Weakly Supervised Object Localization | cs.CV | Weakly supervised object localization remains challenging, where only image
labels instead of bounding boxes are available during training. Object proposal
is an effective component in localization, but often computationally expensive
and incapable of joint optimization with some of the remaining modules. In this
paper... | computer science |
29,355 | An inner-loop free solution to inverse problems using deep neural
networks | cs.CV | We propose a new method that uses deep learning techniques to accelerate the
popular alternating direction method of multipliers (ADMM) solution for inverse
problems. The ADMM updates consist of a proximity operator, a least squares
regression that includes a big matrix inversion, and an explicit solution for
updating ... | computer science |
29,356 | Synthetic Medical Images from Dual Generative Adversarial Networks | cs.CV | Currently there is strong interest in data-driven approaches to medical image
classification. However, medical imaging data is scarce, expensive, and fraught
with legal concerns regarding patient privacy. Typical consent forms only allow
for patient data to be used in medical journals or education, meaning the
majority... | computer science |
29,357 | Polar Transformer Networks | cs.CV | Convolutional neural networks (CNNs) are inherently equivariant to
translation. Efforts to embed other forms of equivariance have concentrated
solely on rotation. We expand the notion of equivariance in CNNs through the
Polar Transformer Network (PTN). PTN combines ideas from the Spatial
Transformer Network (STN) and c... | computer science |
29,358 | Learning Dilation Factors for Semantic Segmentation of Street Scenes | cs.CV | Contextual information is crucial for semantic segmentation. However, finding
the optimal trade-off between keeping desired fine details and at the same time
providing sufficiently large receptive fields is non trivial. This is even more
so, when objects or classes present in an image significantly vary in size.
Dilate... | computer science |
29,359 | Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face
Images | cs.CV | Lighting estimation from face images is an important task and has
applications in many areas such as image editing, intrinsic image
decomposition, and image forgery detection. We propose to train a deep
Convolutional Neural Network (CNN) to regress lighting parameters from a single
face image. Lacking massive ground tr... | computer science |
29,360 | Image Splicing Localization Using A Multi-Task Fully Convolutional
Network (MFCN) | cs.CV | In this work, we propose a technique that utilizes a fully convolutional
network (FCN) to localize image splicing attacks. We first evaluated a
single-task FCN (SFCN) trained only on the surface label. Although the SFCN is
shown to provide superior performance over existing methods, it still provides
a coarse localizat... | computer science |
29,361 | Towards high-throughput 3D insect capture for species discovery and
diagnostics | cs.CV | Digitisation of natural history collections not only preserves precious
information about biological diversity, it also enables us to share, analyse,
annotate and compare specimens to gain new insights. High-resolution,
full-colour 3D capture of biological specimens yields color and geometry
information complementary t... | computer science |
29,362 | Capturing natural-colour 3D models of insects for species discovery | cs.CV | Collections of biological specimens are fundamental to scientific
understanding and characterization of natural diversity. This paper presents a
system for liberating useful information from physical collections by bringing
specimens into the digital domain so they can be more readily shared, analyzed,
annotated and co... | computer science |
29,363 | Focusing Attention: Towards Accurate Text Recognition in Natural Images | cs.CV | Scene text recognition has been a hot research topic in computer vision due
to its various applications. The state of the art is the attention-based
encoder-decoder framework that learns the mapping between input images and
output sequences in a purely data-driven way. However, we observe that existing
attention-based ... | computer science |
29,364 | Deep Embedding Convolutional Neural Network for Synthesizing CT Image
from T1-Weighted MR Image | cs.CV | Recently, more and more attention is drawn to the field of medical image
synthesis across modalities. Among them, the synthesis of computed tomography
(CT) image from T1-weighted magnetic resonance (MR) image is of great
importance, although the mapping between them is highly complex due to large
gaps of appearances of... | computer science |
29,365 | An unsupervised long short-term memory neural network for event
detection in cell videos | cs.CV | We propose an automatic unsupervised cell event detection and classification
method, which expands convolutional Long Short-Term Memory (LSTM) neural
networks, for cellular events in cell video sequences. Cells in images that are
captured from various biomedical applications usually have different shapes and
motility, ... | computer science |
29,366 | Rotational Subgroup Voting and Pose Clustering for Robust 3D Object
Recognition | cs.CV | It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to hand... | computer science |
29,367 | FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction | cs.CV | Minutiae extraction is of critical importance in automated fingerprint
recognition. Previous works on rolled/slap fingerprints failed on latent
fingerprints due to noisy ridge patterns and complex background noises. In this
paper, we propose a new way to design deep convolutional network combining
domain knowledge and ... | computer science |
29,368 | Sparsity-Based Super Resolution for SEM Images | cs.CV | The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam poin... | computer science |
29,369 | Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models | cs.CV | As the population of world is increasing, and even more concentrated in urban
areas, ensuring public safety is becoming a taunting job for security personnel
and crowd managers. Mass events like sports, festivals, concerts, political
gatherings attract thousand of people in a constraint environment,therefore
adequate s... | computer science |
29,370 | Deep Galaxy: Classification of Galaxies based on Deep Convolutional
Neural Networks | cs.CV | In this paper, a deep convolutional neural network architecture for galaxies
classification is presented. The galaxy can be classified based on its features
into main three categories Elliptical, Spiral, and Irregular. The proposed deep
galaxies architecture consists of 8 layers, one main convolutional layer for
featur... | computer science |
29,371 | A Survey of Efficient Regression of General-Activity Human Poses from
Depth Images | cs.CV | This paper presents a comprehensive review on regression-based method for
human pose estimation. The problem of human pose estimation has been
intensively studied and enabled many application from entertainment to
training. Traditional methods often rely on color image only which cannot
completely ambiguity of joint 3D... | computer science |
29,372 | Complete End-To-End Low Cost Solution To a 3D Scanning System with
Integrated Turntable | cs.CV | 3D reconstruction is a technique used in computer vision which has a wide
range of applications in areas like object recognition, city modelling, virtual
reality, physical simulations, video games and special effects. Previously, to
perform a 3D reconstruction, specialized hardwares were required. Such systems
were oft... | computer science |
29,373 | Medical Image Analysis using Convolutional Neural Networks: A Review | cs.CV | Medical image analysis is the science of analyzing or solving medical
problems using different image analysis techniques for affective and efficient
extraction of information. It has emerged as one of the top research area in
the field of engineering and medicine. Recent years have witnessed rapid use of
machine learni... | computer science |
29,374 | A Geometric Approach to Harmonic Color Palette Design | cs.CV | We address the problem of finding harmonic colors, this problem has many
applications, from fashion to industrial design. In order to solve this problem
we consider that colors follow normal distributions in tone (chroma and
lightness) and hue. The proposed approach relies in the CIE standard for
representing colors an... | computer science |
29,375 | Adaptive Real-Time Removal of Impulse Noise in Medical Images | cs.CV | Noise is an important factor that degrades the quality of medical images.
Impulse noise is a common noise, which is caused by malfunctioning of sensor
elements or errors in the transmission of images. In medical images due to
presence of white foreground and black background, many pixels have intensities
similar to imp... | computer science |
29,376 | Monocular Navigation in Large Scale Dynamic Environments | cs.CV | We present a processing technique for a robust reconstruction of motion
properties for single points in large scale, dynamic environments. We assume
that the acquisition camera is moving and that there are other independently
moving agents in a large environment, like road scenarios. The separation of
direction and mag... | computer science |
29,377 | PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume | cs.CV | We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the current optical flow estimate
to warp the CNN f... | computer science |
29,378 | End-to-end Face Detection and Cast Grouping in Movies Using
Erdős-Rényi Clustering | cs.CV | We present an end-to-end system for detecting and clustering faces by
identity in full-length movies. Unlike works that start with a predefined set
of detected faces, we consider the end-to-end problem of detection and
clustering together. We make three separate contributions. First, we combine a
state-of-the-art face ... | computer science |
29,379 | Local Neighborhood Intensity Pattern: A new texture feature descriptor
for image retrieval | cs.CV | In this paper, a new texture descriptor based on the local neighborhood
intensity difference is proposed for content based image retrieval (CBIR). For
computation of texture features like Local Binary Pattern (LBP), the center
pixel in a 3*3 window of an image is compared with all the remaining neighbors,
one pixel at ... | computer science |
29,380 | Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation
Approach | cs.CV | While fine-grained object recognition is an important problem in computer
vision, current models are unlikely to accurately classify objects in the wild.
These fully supervised models need additional annotated images to classify
objects in every new scenario, a task that is infeasible. However, sources such
as e-commer... | computer science |
29,381 | Fine-Grained Car Detection for Visual Census Estimation | cs.CV | Targeted socioeconomic policies require an accurate understanding of a
country's demographic makeup. To that end, the United States spends more than 1
billion dollars a year gathering census data such as race, gender, education,
occupation and unemployment rates. Compared to the traditional method of
collecting surveys... | computer science |
29,382 | DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features
of Convolutional Neural Nets | cs.CV | A deep feature based saliency model (DeepFeat) is developed to leverage the
understanding of the prediction of human fixations. Traditional saliency models
often predict the human visual attention relying on few level image cues.
Although such models predict fixations on a variety of image complexities,
their approache... | computer science |
29,383 | Deep Subspace Clustering Networks | cs.CV | We present a novel deep neural network architecture for unsupervised subspace
clustering. This architecture is built upon deep auto-encoders, which
non-linearly map the input data into a latent space. Our key idea is to
introduce a novel self-expressive layer between the encoder and the decoder to
mimic the "self-expre... | computer science |
29,384 | Extreme Sparse Multinomial Logistic Regression: A Fast and Robust
Framework for Hyperspectral Image Classification | cs.CV | Although the sparse multinomial logistic regression (SMLR) has provided a
useful tool for sparse classification, it suffers from inefficacy in dealing
with high dimensional features and manually set initial regressor values. This
has significantly constrained its applications for hyperspectral image (HSI)
classificatio... | computer science |
29,385 | A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast
Cancer Detection | cs.CV | In this research work, a novel framework is pro- posed as an efficient
successor to traditional imaging methods for breast cancer detection in order
to decrease the computational complexity. In this framework, the breast is
devided into seg- ments in an iterative process and in each iteration, the one
having the most p... | computer science |
29,386 | Learning to Segment Breast Biopsy Whole Slide Images | cs.CV | We trained and applied an encoder-decoder model to semantically segment
breast biopsy images into biologically meaningful tissue labels. Since
conventional encoder-decoder networks cannot be applied directly on large
biopsy images and the different sized structures in biopsies present novel
challenges, we propose four ... | computer science |
29,387 | Segmentation and Classification of Cine-MR Images Using Fully
Convolutional Networks and Handcrafted Features | cs.CV | Three-dimensional cine-MRI is of crucial importance for assessing the cardiac
function. Features that describe the anatomy and function of cardiac structures
(e.g. Left Ventricle (LV), Right Ventricle (RV), and Myocardium(MC)) are known
to have significant diagnostic value and can be computed from 3D cine-MR
images. Ho... | computer science |
29,388 | Best Practices in Convolutional Networks for Forward-Looking Sonar Image
Recognition | cs.CV | Convolutional Neural Networks (CNN) have revolutionized perception for color
images, and their application to sonar images has also obtained good results.
But in general CNNs are difficult to train without a large dataset, need manual
tuning of a considerable number of hyperparameters, and require many careful
decision... | computer science |
29,389 | Calibration of depth cameras using denoised depth images | cs.CV | Depth sensing devices have created various new applications in scientific and
commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing
Device) cameras. Most of these applications require the depth cameras to be
pre-calibrated. However, traditional calibration methods using a checkerboard
do not wo... | computer science |
29,390 | Locating 3D Object Proposals: A Depth-Based Online Approach | cs.CV | 2D object proposals, quickly detected regions in an image that likely contain
an object of interest, are an effective approach for improving the
computational efficiency and accuracy of object detection in color images. In
this work, we propose a novel online method that generates 3D object proposals
in a RGB-D video s... | computer science |
29,391 | Method to Detect Eye Position Noise from Video-Oculography when
Detection of Pupil or Corneal Reflection Position Fails | cs.CV | We present software to detect noise in eye position signals from video-based
eye-tracking systems that depend on accurate pupil and corneal reflection
position estimation. When such systems transiently fail to properly detect the
pupil or the corneal reflection due to occlusion from eyelids, eye lashes or
various shado... | computer science |
29,392 | Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using
Superpixels | cs.CV | Coronary artery disease (CAD) is the leading causes of death around the
world. One of the most common imaging methods for diagnosing this disease is
X-ray angiography. Diagnosing using these images is usually challenging due to
non-uniform illumination, low contrast, presence of other body tissues,
presence of catheter... | computer science |
29,393 | An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional
Networks for Semantic Segmentation | cs.CV | Deep convolutional neural networks (CNNs) have shown excellent performance in
object recognition tasks and dense classification problems such as semantic
segmentation. However, training deep neural networks on large and sparse
datasets is still challenging and can require large amounts of computation and
memory. In thi... | computer science |
29,394 | Detecting Hands in Egocentric Videos: Towards Action Recognition | cs.CV | Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extre... | computer science |
29,395 | Improving Heterogeneous Face Recognition with Conditional Adversarial
Networks | cs.CV | Heterogeneous face recognition between color image and depth image is a much
desired capacity for real world applications where shape information is looked
upon as merely involved in gallery. In this paper, we propose a cross-modal
deep learning method as an effective and efficient workaround for this
challenge. Specif... | computer science |
29,396 | Learning a Dilated Residual Network for SAR Image Despeckling | cs.CV | In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated con... | computer science |
29,397 | Graph Scaling Cut with L1-Norm for Classification of Hyperspectral
Images | cs.CV | In this paper, we propose an L1 normalized graph based dimensionality
reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC).
The underlying idea of this method is to generate the optimal projection matrix
by retaining the original distribution of the data. Though L2-norm is generally
preferred for... | computer science |
29,398 | Joint Calibration of Panoramic Camera and Lidar Based on Supervised
Learning | cs.CV | In view of contemporary panoramic camera-laser scanner system, the
traditional calibration method is not suitable for panoramic cameras whose
imaging model is extremely nonlinear. The method based on statistical
optimization has the disadvantage that the requirement of the number of laser
scanner's channels is relative... | computer science |
29,399 | Model Distillation with Knowledge Transfer from Face Classification to
Alignment and Verification | cs.CV | Knowledge distillation is a potential solution for model compression. The
idea is to make a small student network imitate the target of a large teacher
network, then the student network can be competitive to the teacher one. Most
previous studies focus on model distillation in the classification task, where
they propos... | computer science |
29,400 | How to Train Triplet Networks with 100K Identities? | cs.CV | Training triplet networks with large-scale data is challenging in face
recognition. Due to the number of possible triplets explodes with the number of
samples, previous studies adopt the online hard negative mining(OHNM) to handle
it. However, as the number of identities becomes extremely large, the training
will suffe... | computer science |
29,401 | Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation | cs.CV | Deep learning has quickly become the weapon of choice for brain lesion
segmentation. However, few existing algorithms pre-configure any biological
context of their chosen segmentation tissues, and instead rely on the neural
network's optimizer to develop such associations de novo. We present a novel
method for applying... | computer science |
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