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29,602 | Fast Shadow Detection from a Single Image Using a Patched Convolutional
Neural Network | cs.CV | In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a t... | computer science |
29,603 | Dynamic Label Graph Matching for Unsupervised Video Re-Identification | cs.CV | Label estimation is an important component in an unsupervised person
re-identification (re-ID) system. This paper focuses on cross-camera label
estimation, which can be subsequently used in feature learning to learn robust
re-ID models. Specifically, we propose to construct a graph for samples in each
camera, and then ... | computer science |
29,604 | Effective Image Retrieval via Multilinear Multi-index Fusion | cs.CV | Multi-index fusion has demonstrated impressive performances in retrieval task
by integrating different visual representations in a unified framework.
However, previous works mainly consider propagating similarities via neighbor
structure, ignoring the high order information among different visual
representations. In th... | computer science |
29,605 | Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data,
Applied to Object Detection under Ego-motion | cs.CV | In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overc... | computer science |
29,606 | Signature Verification Approach using Fusion of Hybrid Texture Features | cs.CV | In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class... | computer science |
29,607 | Generative Adversarial Networks with Inverse Transformation Unit | cs.CV | In this paper we introduce a new structure to Generative Adversarial Networks
by adding an inverse transformation unit behind the generator. We present two
theorems to claim the convergence of the model, and two conjectures to nonideal
situations when the transformation is not bijection. A general survey on models
with... | computer science |
29,608 | Human Detection for Night Surveillance using Adaptive Background
Subtracted Image | cs.CV | Surveillance based on Computer Vision has become a major necessity in current
era. Most of the surveillance systems operate on visible light imaging, but
performance based on visible light imaging is limited due to some factors like
variation in light intensity during the daytime. The matter of concern lies in
the need... | computer science |
29,609 | Light field super resolution through controlled micro-shifts of light
field sensor | cs.CV | Light field cameras presents new capabilities, such as post-capture
refocusing and aperture control, through capturing directional and spatial
distribution of light rays in space. Among different light field camera
implementations, micro-lens array based light field cameras is a cost-effective
and compact approach to c... | computer science |
29,610 | Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label
Prediction | cs.CV | In this paper, we present a method to learn a visual representation adapted
for e-commerce products. Based on weakly supervised learning, our model learns
from noisy datasets crawled on e-commerce website catalogs and does not require
any manual labeling. We show that our representation can be used for downward
classif... | computer science |
29,611 | FoodNet: Recognizing Foods Using Ensemble of Deep Networks | cs.CV | In this work we propose a methodology for an automatic food classification
system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN)
architecture that takes advantages of the features from other deep networks and
improves the effic... | computer science |
29,612 | Fast Convolutional Sparse Coding in the Dual Domain | cs.CV | Convolutional sparse coding (CSC) is an important building block of many
computer vision applications ranging from image and video compression to deep
learning. We present two contributions to the state of the art in CSC. First,
we significantly speed up the computation by proposing a new optimization
framework that ta... | computer science |
29,613 | Hierarchical Scene Parsing by Weakly Supervised Learning with Image
Descriptions | cs.CV | This paper investigates a fundamental problem of scene understanding: how to
parse a scene image into a structured configuration (i.e., a semantic object
hierarchy with object interaction relations). We propose a deep architecture
consisting of two networks: i) a convolutional neural network (CNN) extracting
the image ... | computer science |
29,614 | Drought Stress Classification using 3D Plant Models | cs.CV | Quantification of physiological changes in plants can capture different
drought mechanisms and assist in selection of tolerant varieties in a high
throughput manner. In this context, an accurate 3D model of plant canopy
provides a reliable representation for drought stress characterization in
contrast to using 2D image... | computer science |
29,615 | Modeling the Resource Requirements of Convolutional Neural Networks on
Mobile Devices | cs.CV | Convolutional Neural Networks (CNNs) have revolutionized the research in
computer vision, due to their ability to capture complex patterns, resulting in
high inference accuracies. However, the increasingly complex nature of these
neural networks means that they are particularly suited for server computers
with powerful... | computer science |
29,616 | Local Directional Relation Pattern for Unconstrained and Robust Face
Retrieval | cs.CV | Face recognition is still a very demanding area of research. This problem
becomes more challenging in unconstrained environment and in the presence of
several variations like pose, illumination, expression, etc. Local descriptors
are widely used for this task. The existing local descriptors are not able to
utilize the ... | computer science |
29,617 | A New Multifocus Image Fusion Method Using Contourlet Transform | cs.CV | A new multifocus image fusion approach is presented in this paper. First the
contourlet transform is used to decompose the source images into different
components. Then, some salient features are extracted from components. In order
to extract salient features, spatial frequency is used. Subsequently, the best
coefficie... | computer science |
29,618 | Scale Adaptive Clustering of Multiple Structures | cs.CV | We propose the segmentation of noisy datasets into Multiple Inlier Structures
with a new Robust Estimator (MISRE). The scale of each individual structure is
estimated adaptively from the input data and refined by mean shift, without
tuning any parameter in the process, or manually specifying thresholds for
different es... | computer science |
29,619 | ANSAC: Adaptive Non-minimal Sample and Consensus | cs.CV | While RANSAC-based methods are robust to incorrect image correspondences
(outliers), their hypothesis generators are not robust to correct image
correspondences (inliers) with positional error (noise). This slows down their
convergence because hypotheses drawn from a minimal set of noisy inliers can
deviate significant... | computer science |
29,620 | Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images | cs.CV | Multi-atlas segmentation approach is one of the most widely-used image
segmentation techniques in biomedical applications. There are two major
challenges in this category of methods, i.e., atlas selection and label fusion.
In this paper, we propose a novel multi-atlas segmentation method that
formulates multi-atlas seg... | computer science |
29,621 | Combining Real-Valued and Binary Gabor-Radon Features for Classification
and Search in Medical Imaging Archives | cs.CV | Content-based image retrieval (CBIR) of medical images in large datasets to
identify similar images when a query image is given can be very useful in
improving the diagnostic decision of the clinical experts and as well in
educational scenarios. In this paper, we used two stage classification and
retrieval approach to ... | computer science |
29,622 | Improving Dermoscopic Image Segmentation with Enhanced
Convolutional-Deconvolutional Networks | cs.CV | Automatic skin lesion segmentation on dermoscopic images is an essential step
in computer-aided diagnosis of melanoma. However, this task is challenging due
to significant variations of lesion appearances across different patients. This
challenge is further exacerbated when dealing with a large amount of image
data. In... | computer science |
29,623 | Photorealistic Style Transfer with Screened Poisson Equation | cs.CV | Recent work has shown impressive success in transferring painterly style to
images. These approaches, however, fall short of photorealistic style transfer.
Even when both the input and reference images are photographs, the output still
exhibits distortions reminiscent of a painting. In this paper we propose an
approach... | computer science |
29,624 | Soft Correspondences in Multimodal Scene Parsing | cs.CV | Exploiting multiple modalities for semantic scene parsing has been shown to
improve accuracy over the singlemodality scenario. However multimodal datasets
often suffer from problems such as data misalignment and label inconsistencies,
where the existing methods assume that corresponding regions in two modalities
must h... | computer science |
29,625 | Recognition of Documents in Braille | cs.CV | Visually impaired people are integral part of the society and it has been a
must to provide them with means and system through which they may communicate
with the world. In this work, I would like to address how computers can be made
useful to read the scripts in Braille. The importance of this work is to reduce
commun... | computer science |
29,626 | Efficient Convolutional Neural Network For Audio Event Detection | cs.CV | Wireless distributed systems as used in sensor networks, Internet-of-Things
and cyber-physical systems, impose high requirements on resource efficiency.
Advanced preprocessing and classification of data at the network edge can help
to decrease the communication demand and to reduce the amount of data to be
processed ce... | computer science |
29,627 | B-CNN: Branch Convolutional Neural Network for Hierarchical
Classification | cs.CV | Convolutional Neural Network (CNN) image classifiers are traditionally
designed to have sequential convolutional layers with a single output layer.
This is based on the assumption that all target classes should be treated
equally and exclusively. However, some classes can be more difficult to
distinguish than others, a... | computer science |
29,628 | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | cs.CV | Pedestrian analysis plays a vital role in intelligent video surveillance and
is a key component for security-centric computer vision systems. Despite that
the convolutional neural networks are remarkable in learning discriminative
features from images, the learning of comprehensive features of pedestrians for
fine-grai... | computer science |
29,629 | Recognition of feature curves on 3D shapes using an algebraic approach
to Hough transforms | cs.CV | Feature curves are largely adopted to highlight shape features, such as sharp
lines, or to divide surfaces into meaningful segments, like convex or concave
regions. Extracting these curves is not sufficient to convey prominent and
meaningful information about a shape. We have first to separate the curves
belonging to f... | computer science |
29,630 | Possibilistic Fuzzy Local Information C-Means for Sonar Image
Segmentation | cs.CV | Side-look synthetic aperture sonar (SAS) can produce very high quality images
of the sea-floor. When viewing this imagery, a human observer can often easily
identify various sea-floor textures such as sand ripple, hard-packed sand, sea
grass and rock. In this paper, we present the Possibilistic Fuzzy Local
Information ... | computer science |
29,631 | Unified Deep Supervised Domain Adaptation and Generalization | cs.CV | This work provides a unified framework for addressing the problem of visual
supervised domain adaptation and generalization with deep models. The main idea
is to exploit the Siamese architecture to learn an embedding subspace that is
discriminative, and where mapped visual domains are semantically aligned and
yet maxim... | computer science |
29,632 | Fast Barcode Retrieval for Consensus Contouring | cs.CV | Marking tumors and organs is a challenging task suffering from both inter-
and intra-observer variability. The literature quantifies observer variability
by generating consensus among multiple experts when they mark the same image.
Automatically building consensus contours to establish quality assurance for
image segme... | computer science |
29,633 | Light Cascaded Convolutional Neural Networks for Accurate Player
Detection | cs.CV | Vision based player detection is important in sports applications. Accuracy,
efficiency, and low memory consumption are desirable for real-time tasks such
as intelligent broadcasting and automatic event classification. In this paper,
we present a cascaded convolutional neural network (CNN) that satisfies all
three of t... | computer science |
29,634 | Deep Competitive Pathway Networks | cs.CV | In the design of deep neural architectures, recent studies have demonstrated
the benefits of grouping subnetworks into a larger network. For examples, the
Inception architecture integrates multi-scale subnetworks and the residual
network can be regarded that a residual unit combines a residual subnetwork
with an identi... | computer science |
29,635 | A Variational Approach to Shape-from-shading Under Natural Illumination | cs.CV | A numerical solution to shape-from-shading under natural illumination is
presented. It builds upon an augmented Lagrangian approach for solving a
generic PDE-based shape-from-shading model which handles directional or
spherical harmonic lighting, orthographic or perspective projection, and
greylevel or multi-channel im... | computer science |
29,636 | Optimisation of photometric stereo methods by non-convex variational
minimisation | cs.CV | Estimating shape and appearance of a three dimensional object from a given
set of images is a classic research topic that is still actively pursued. Among
the various techniques available, PS is distinguished by the assumption that
the underlying input images are taken from the same point of view but under
different li... | computer science |
29,637 | A Gaussian mixture model representation of endmember variability in
hyperspectral unmixing | cs.CV | Hyperspectral unmixing while considering endmember variability is usually
performed by the normal compositional model (NCM), where the endmembers for
each pixel are assumed to be sampled from unimodal Gaussian distributions.
However, in real applications, the distribution of a material is often not
Gaussian. In this pa... | computer science |
29,638 | Dense RGB-D semantic mapping with Pixel-Voxel neural network | cs.CV | For intelligent robotics applications, extending 3D mapping to 3D semantic
mapping enables robots to, not only localize themselves with respect to the
scene's geometrical features but also simultaneously understand the higher
level meaning of the scene contexts. Most previous methods focus on geometric
3D reconstructio... | computer science |
29,639 | PCANet-II: When PCANet Meets the Second Order Pooling | cs.CV | PCANet, as one noticeable shallow network, employs the histogram
representation for feature pooling. However, there are three main problems
about this kind of pooling method. First, the histogram-based pooling method
binarizes the feature maps and leads to inevitable discriminative information
loss. Second, it is diffi... | computer science |
29,640 | Unsupervised Segmentation of Action Segments in Egocentric Videos using
Gaze | cs.CV | Unsupervised segmentation of action segments in egocentric videos is a
desirable feature in tasks such as activity recognition and content-based video
retrieval. Reducing the search space into a finite set of action segments
facilitates a faster and less noisy matching. However, there exist a
substantial gap in machine... | computer science |
29,641 | Unsupervised Classification of Intrusive Igneous Rock Thin Section
Images using Edge Detection and Colour Analysis | cs.CV | Classification of rocks is one of the fundamental tasks in a geological
study. The process requires a human expert to examine sampled thin section
images under a microscope. In this study, we propose a method that uses
microscope automation, digital image acquisition, edge detection and colour
analysis (histogram). We ... | computer science |
29,642 | DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep
Learning | cs.CV | In this paper, we investigate estimating emergence and biomass traits from
color images and elevation maps of wheat field plots. We employ a
state-of-the-art deconvolutional network for segmentation and convolutional
architectures, with residual and Inception-like layers, to estimate traits via
high dimensional nonline... | computer science |
29,643 | Image Dehazing using Bilinear Composition Loss Function | cs.CV | In this paper, we introduce a bilinear composition loss function to address
the problem of image dehazing. Previous methods in image dehazing use a
two-stage approach which first estimate the transmission map followed by clear
image estimation. The drawback of a two-stage method is that it tends to boost
local image ar... | computer science |
29,644 | Pyramidal RoR for Image Classification | cs.CV | The Residual Networks of Residual Networks (RoR) exhibits excellent
performance in the image classification task, but sharply increasing the number
of feature map channels makes the characteristic information transmission
incoherent, which losses a certain of information related to classification
prediction, limiting t... | computer science |
29,645 | Margin Sample Mining Loss: A Deep Learning Based Method for Person
Re-identification | cs.CV | Person re-identification (ReID) is an important task in computer vision.
Recently, deep learning with a metric learning loss has become a common
framework for ReID. In this paper, we also propose a new metric learning loss
with hard sample mining called margin smaple mining loss (MSML) which can
achieve better accuracy... | computer science |
29,646 | Depth estimation using structured light flow -- analysis of projected
pattern flow on an object's surface -- | cs.CV | Shape reconstruction techniques using structured light have been widely
researched and developed due to their robustness, high precision, and density.
Because the techniques are based on decoding a pattern to find correspondences,
it implicitly requires that the projected patterns be clearly captured by an
image sensor... | computer science |
29,647 | Temporal shape super-resolution by intra-frame motion encoding using
high-fps structured light | cs.CV | One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. ... | computer science |
29,648 | Indirect Match Highlights Detection with Deep Convolutional Neural
Networks | cs.CV | Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve... | computer science |
29,649 | A Study of Cross-domain Generative Models applied to Cartoon Series | cs.CV | We investigate Generative Adversarial Networks (GANs) to model one particular
kind of image: frames from TV cartoons. Cartoons are particularly interesting
because their visual appearance emphasizes the important semantic information
about a scene while abstracting out the less important details, but each
cartoon serie... | computer science |
29,650 | Neural Color Transfer between Images | cs.CV | We propose a new algorithm for color transfer between images that have
perceptually similar semantic structures. We aim to achieve a more accurate
color transfer that leverages semantically-meaningful dense correspondence
between images. To accomplish this, our algorithm uses neural representations
for matching. Additi... | computer science |
29,651 | Rethinking Feature Discrimination and Polymerization for Large-scale
Recognition | cs.CV | Feature matters. How to train a deep network to acquire discriminative
features across categories and polymerized features within classes has always
been at the core of many computer vision tasks, specially for large-scale
recognition systems where test identities are unseen during training and the
number of classes co... | computer science |
29,652 | Classification of Time-Series Images Using Deep Convolutional Neural
Networks | cs.CV | Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-se... | computer science |
29,653 | End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech | cs.CV | We present a deep learning framework for real-time speech-driven 3D facial
animation from just raw waveforms. Our deep neural network directly maps an
input sequence of speech audio to a series of micro facial action unit
activations and head rotations to drive a 3D blendshape face model. In
particular, our deep model ... | computer science |
29,654 | Fine-Grained Head Pose Estimation Without Keypoints | cs.CV | Estimating the head pose of a person is a crucial problem that has a large
amount of applications such as aiding in gaze estimation, modeling attention,
fitting 3D models to video and performing face alignment. Traditionally head
pose is computed by estimating some keypoints from the target face and solving
the 2D to 3... | computer science |
29,655 | Interpretable Convolutional Neural Networks | cs.CV | This paper proposes a method to modify traditional convolutional neural
networks (CNNs) into interpretable CNNs, in order to clarify knowledge
representations in high conv-layers of CNNs. In an interpretable CNN, each
filter in a high conv-layer represents a certain object part. We do not need
any annotations of object... | computer science |
29,656 | VIDOSAT: High-dimensional Sparsifying Transform Learning for Online
Video Denoising | cs.CV | Techniques exploiting the sparsity of images in a transform domain have been
effective for various applications in image and video processing. Transform
learning methods involve cheap computations and have been demonstrated to
perform well in applications such as image denoising and medical image
reconstruction. Recent... | computer science |
29,657 | GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks | cs.CV | Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is ma... | computer science |
29,658 | A concatenating framework of shortcut convolutional neural networks | cs.CV | It is well accepted that convolutional neural networks play an important role
in learning excellent features for image classification and recognition.
However, in tradition they only allow adjacent layers connected, limiting
integration of multi-scale information. To further improve their performance,
we present a conc... | computer science |
29,659 | Joint Person Re-identification and Camera Network Topology Inference in
Multiple Cameras | cs.CV | Person re-identification is the task of recognizing or identifying a person
across multiple views in multi-camera networks. Although there has been much
progress in person re-identification, person re-identification in large-scale
multi-camera networks still remains a challenging task because of the large
spatio-tempor... | computer science |
29,660 | Resolution limits on visual speech recognition | cs.CV | Visual-only speech recognition is dependent upon a number of factors that can
be difficult to control, such as: lighting; identity; motion; emotion and
expression. But some factors, such as video resolution are controllable, so it
is surprising that there is not yet a systematic study of the effect of
resolution on lip... | computer science |
29,661 | Some observations on computer lip-reading: moving from the dream to the
reality | cs.CV | In the quest for greater computer lip-reading performance there are a number
of tacit assumptions which are either present in the datasets (high resolution
for example) or in the methods (recognition of spoken visual units called
visemes for example). Here we review these and other assumptions and show the
surprising r... | computer science |
29,662 | Isotropic and Steerable Wavelets in N Dimensions. A multiresolution
analysis framework for ITK | cs.CV | This document describes the implementation of the external module
ITKIsotropicWavelets, a multiresolution (MRA) analysis framework using
isotropic and steerable wavelets in the frequency domain. This framework
provides the backbone for state of the art filters for denoising, feature
detection or phase analysis in N-dim... | computer science |
29,663 | Detection of Inferior Myocardial Infarction using Shallow Convolutional
Neural Networks | cs.CV | Myocardial Infarction is one of the leading causes of death worldwide. This
paper presents a Convolutional Neural Network (CNN) architecture which takes
raw Electrocardiography (ECG) signal from lead II, III and AVF and
differentiates between inferior myocardial infarction (IMI) and healthy
signals. The performance of ... | computer science |
29,664 | Speaker-independent machine lip-reading with speaker-dependent viseme
classifiers | cs.CV | In machine lip-reading, which is identification of speech from visual-only
information, there is evidence to show that visual speech is highly dependent
upon the speaker [1]. Here, we use a phoneme-clustering method to form new
phoneme-to-viseme maps for both individual and multiple speakers. We use these
maps to exami... | computer science |
29,665 | Fast Fine-grained Image Classification via Weakly Supervised
Discriminative Localization | cs.CV | Fine-grained image classification is to recognize hundreds of subcategories
in each basic-level category. Existing methods employ discriminative
localization to find the key distinctions among subcategories. However, they
generally have two limitations: (1) Discriminative localization relies on
region proposal methods ... | computer science |
29,666 | Decoding visemes: improving machine lipreading | cs.CV | To undertake machine lip-reading, we try to recognise speech from a visual
signal. Current work often uses viseme classification supported by language
models with varying degrees of success. A few recent works suggest phoneme
classification, in the right circumstances, can outperform viseme
classification. In this work... | computer science |
29,667 | Person Re-Identification with Vision and Language | cs.CV | In this paper we propose a new approach to person re-identification using
images and natural language descriptions. We propose a joint vision and
language model based on CCA and CNN architectures to match across the two
modalities as well as to enrich visual examples for which there are no language
descriptions. We als... | computer science |
29,668 | Calligraphic Stylisation Learning with a Physiologically Plausible Model
of Movement and Recurrent Neural Networks | cs.CV | We propose a computational framework to learn stylisation patterns from
example drawings or writings, and then generate new trajectories that possess
similar stylistic qualities. We particularly focus on the generation and
stylisation of trajectories that are similar to the ones that can be seen in
calligraphy and graf... | computer science |
29,669 | The Cafe Wall Illusion: Local and Global Perception from multiple scale
to multiscale | cs.CV | Geometrical illusions are a subclass of optical illusions in which the
geometrical characteristics of patterns such as orientations and angles are
distorted and misperceived as the result of low- to high-level retinal/cortical
processing. Modelling the detection of tilt in these illusions and their
strengths as they ar... | computer science |
29,670 | Group Affect Prediction Using Multimodal Distributions | cs.CV | We describe our approach towards building an efficient predictive model to
detect emotions for a group of people in an image. We have proposed that
training a Convolutional Neural Network (CNN) model on the emotion heatmaps
extracted from the image, outperforms a CNN model trained entirely on the raw
images. The compar... | computer science |
29,671 | Wide and deep volumetric residual networks for volumetric image
classification | cs.CV | 3D shape models that directly classify objects from 3D information have
become more widely implementable. Current state of the art models rely on deep
convolutional and inception models that are resource intensive. Residual neural
networks have been demonstrated to be easier to optimize and do not suffer from
vanishing... | computer science |
29,672 | Reducing Complexity of HEVC: A Deep Learning Approach | cs.CV | High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the
proceeding H.264 standard but at the expense of extremely high encoding
complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a
large proportion of the HEVC encoding complexity, due to the bruteforce search
for rate-distort... | computer science |
29,673 | Adaptive Measurement Network for CS Image Reconstruction | cs.CV | Conventional compressive sensing (CS) reconstruction is very slow for its
characteristic of solving an optimization problem. Convolu- tional neural
network can realize fast processing while achieving compa- rable results. While
CS image recovery with high quality not only de- pends on good reconstruction
algorithms, bu... | computer science |
29,674 | Robust non-local means filter for ultrasound image denoising | cs.CV | This paper introduces a new approach to non-local means image denoising.
Instead of using all pixels located in the search window for estimating the
value of a pixel, we identify the highly corrupted pixels and assign less
weight to these pixels. This method is called robust non-local means. Numerical
and subjective ev... | computer science |
29,675 | Learning Autoencoded Radon Projections | cs.CV | Autoencoders have been recently used for encoding medical images. In this
study, we design and validate a new framework for retrieving medical images by
classifying Radon projections, compressed in the deepest layer of an
autoencoder. As the autoencoder reduces the dimensionality, a multilayer
perceptron (MLP) can be e... | computer science |
29,676 | Skin Lesion Segmentation: U-Nets versus Clustering | cs.CV | Many automatic skin lesion diagnosis systems use segmentation as a
preprocessing step to diagnose skin conditions because skin lesion shape,
border irregularity, and size can influence the likelihood of malignancy. This
paper presents, examines and compares two different approaches to skin lesion
segmentation. The firs... | computer science |
29,677 | A Comparative Study of CNN, BoVW and LBP for Classification of
Histopathological Images | cs.CV | Despite the progress made in the field of medical imaging, it remains a large
area of open research, especially due to the variety of imaging modalities and
disease-specific characteristics. This paper is a comparative study describing
the potential of using local binary patterns (LBP), deep features and the
bag-of-vis... | computer science |
29,678 | Variational Grid Setting Network | cs.CV | We propose a new neural network architecture for automatic generation of
missing characters in a Chinese font set. We call the neural network
architecture the Variational Grid Setting Network which is based on the
variational autoencoder (VAE) with some tweaks. The neural network model is
able to generate missing chara... | computer science |
29,679 | Deep learning for source camera identification on mobile devices | cs.CV | In the present paper, we propose a source camera identification method for
mobile devices based on deep learning. Recently, convolutional neural networks
(CNNs) have shown a remarkable performance on several tasks such as image
recognition, video analysis or natural language processing. A CNN consists on a
set of layer... | computer science |
29,680 | Gaussian Three-Dimensional kernel SVM for Edge Detection Applications | cs.CV | This paper presents a novel and uniform algorithm for edge detection based on
SVM (support vector machine) with Three-dimensional Gaussian radial basis
function with kernel. Because of disadvantages in traditional edge detection
such as inaccurate edge location, rough edge and careless on detect soft edge.
The experime... | computer science |
29,681 | Spinal cord gray matter segmentation using deep dilated convolutions | cs.CV | Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In thi... | computer science |
29,682 | Decoding visemes: improving machine lipreading (PhD thesis) | cs.CV | Machine lipreading (MLR) is speech recognition from visual cues and a niche
research problem in speech processing & computer vision. Current challenges
fall into two groups: the content of the video, such as rate of speech or; the
parameters of the video recording e.g, video resolution. We show that HD video
is not nee... | computer science |
29,683 | Visual speech recognition: aligning terminologies for better
understanding | cs.CV | We are at an exciting time for machine lipreading. Traditional research
stemmed from the adaptation of audio recognition systems. But now, the computer
vision community is also participating. This joining of two previously
disparate areas with different perspectives on computer lipreading is creating
opportunities for ... | computer science |
29,684 | Visual gesture variability between talkers in continuous visual speech | cs.CV | Recent adoption of deep learning methods to the field of machine lipreading
research gives us two options to pursue to improve system performance. Either,
we develop end-to-end systems holistically or, we experiment to further our
understanding of the visual speech signal. The latter option is more difficult
but this k... | computer science |
29,685 | Understanding the visual speech signal | cs.CV | For machines to lipread, or understand speech from lip movement, they decode
lip-motions (known as visemes) into the spoken sounds. We investigate the
visual speech channel to further our understanding of visemes. This has
applications beyond machine lipreading; speech therapists, animators, and
psychologists can benef... | computer science |
29,686 | Visual Tracking via Learning Dynamic Patch-based Graph Representation | cs.CV | Existing visual tracking methods usually localize a target object with a
bounding box, in which the performance of the foreground object trackers or
detectors is often affected by the inclusion of background clutter. To handle
this problem, we learn a patch-based graph representation for visual tracking.
The tracked ob... | computer science |
29,687 | Content-Adaptive Sketch Portrait Generation by Decompositional
Representation Learning | cs.CV | Sketch portrait generation benefits a wide range of applications such as
digital entertainment and law enforcement. Although plenty of efforts have been
dedicated to this task, several issues still remain unsolved for generating
vivid and detail-preserving personal sketch portraits. For example, quite a few
artifacts m... | computer science |
29,688 | Learning to Segment Human by Watching YouTube | cs.CV | An intuition on human segmentation is that when a human is moving in a video,
the video-context (e.g., appearance and motion clues) may potentially infer
reasonable mask information for the whole human body. Inspired by this, based
on popular deep convolutional neural networks (CNN), we explore a very-weakly
supervised... | computer science |
29,689 | Secrets in Computing Optical Flow by Convolutional Networks | cs.CV | Convolutional neural networks (CNNs) have been widely used over many areas in
compute vision. Especially in classification. Recently, FlowNet and several
works on opti- cal estimation using CNNs shows the potential ability of CNNs in
doing per-pixel regression. We proposed several CNNs network architectures that
can es... | computer science |
29,690 | Monitoring tool usage in cataract surgery videos using boosted
convolutional and recurrent neural networks | cs.CV | With an estimated 19 million operations performed annually, cataract surgery
is the most common surgical procedure. This paper investigates the automatic
monitoring of tool usage during a cataract surgery, with potential applications
in report generation, surgical training and real-time decision support. In this
study,... | computer science |
29,691 | GraphMatch: Efficient Large-Scale Graph Construction for Structure from
Motion | cs.CV | We present GraphMatch, an approximate yet efficient method for building the
matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike
modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the
matching graph and avoid a costly brute-force search of matching image pairs,
GraphMatch doe... | computer science |
29,692 | Grader variability and the importance of reference standards for
evaluating machine learning models for diabetic retinopathy | cs.CV | Diabetic retinopathy (DR) and diabetic macular edema are common complications
of diabetes which can lead to vision loss. The grading of DR is a fairly
complex process that requires the detection of fine features such as
microaneurysms, intraretinal hemorrhages, and intraretinal microvascular
abnormalities. Because of t... | computer science |
29,693 | Semantic 3D Reconstruction with Finite Element Bases | cs.CV | We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Start... | computer science |
29,694 | Accelerating CS in Parallel Imaging Reconstructions Using an Efficient
and Effective Circulant Preconditioner | cs.CV | Purpose: Design of a preconditioner for fast and efficient parallel imaging
and compressed sensing reconstructions. Theory: Parallel imaging and compressed
sensing reconstructions become time consuming when the problem size or the
number of coils is large, due to the large linear system of equations that has
to be solv... | computer science |
29,695 | DeepLesion: Automated Deep Mining, Categorization and Detection of
Significant Radiology Image Findings using Large-Scale Clinical Lesion
Annotations | cs.CV | Extracting, harvesting and building large-scale annotated radiological image
datasets is a greatly important yet challenging problem. It is also the
bottleneck to designing more effective data-hungry computing paradigms (e.g.,
deep learning) for medical image analysis. Yet, vast amounts of clinical
annotations (usually... | computer science |
29,696 | Energy-Based Spherical Sparse Coding | cs.CV | In this paper, we explore an efficient variant of convolutional sparse coding
with unit norm code vectors where reconstruction quality is evaluated using an
inner product (cosine distance). To use these codes for discriminative
classification, we describe a model we term Energy-Based Spherical Sparse
Coding (EB-SSC) in... | computer science |
29,697 | Plane-extraction from depth-data using a Gaussian mixture regression
model | cs.CV | We propose a novel algorithm for unsupervised extraction of piecewise planar
models from depth-data. Among other applications, such models are a good way of
enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive
their surroundings and to navigate in three dimensions. We propose to do this
by fi... | computer science |
29,698 | Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid
Networks | cs.CV | Convolutional neural networks have recently demonstrated high-quality
reconstruction for single image super-resolution. However, existing methods
often require a large number of network parameters and entail heavy
computational loads at runtime for generating high-accuracy super-resolution
results. In this paper, we pr... | computer science |
29,699 | Integrating Boundary and Center Correlation Filters for Visual Tracking
with Aspect Ratio Variation | cs.CV | The aspect ratio variation frequently appears in visual tracking and has a
severe influence on performance. Although many correlation filter (CF)-based
trackers have also been suggested for scale adaptive tracking, few studies have
been given to handle the aspect ratio variation for CF trackers. In this paper,
we make ... | computer science |
29,700 | Online Photometric Calibration for Auto Exposure Video for Realtime
Visual Odometry and SLAM | cs.CV | Recent direct visual odometry and SLAM algorithms have demonstrated
impressive levels of precision. However, they require a photometric camera
calibration in order to achieve competitive results. Hence, the respective
algorithm cannot be directly applied to an off-the-shelf-camera or to a video
sequence acquired with a... | computer science |
29,701 | Multiframe Scene Flow with Piecewise Rigid Motion | cs.CV | We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formu... | computer science |
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