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29,702 | DiffuserCam: Lensless Single-exposure 3D Imaging | cs.CV | We demonstrate a compact and easy-to-build computational camera for
single-shot 3D imaging. Our lensless system consists solely of a diffuser
placed in front of a standard image sensor. Every point within the volumetric
field-of-view projects a unique pseudorandom pattern of caustics on the sensor.
By using a physical ... | computer science |
29,703 | Tracking Persons-of-Interest via Unsupervised Representation Adaptation | cs.CV | Multi-face tracking in unconstrained videos is a challenging problem as faces
of one person often appear drastically different in multiple shots due to
significant variations in scale, pose, expression, illumination, and make-up.
Existing multi-target tracking methods often use low-level features which are
not sufficie... | computer science |
29,704 | Video Denoising and Enhancement via Dynamic Video Layering | cs.CV | Video denoising refers to the problem of removing "noise" from a video
sequence. Here the term "noise" is used in a broad sense to refer to any
corruption or outlier or interference that is not the quantity of interest. In
this work, we develop a novel approach to video denoising that is based on the
idea that many noi... | computer science |
29,705 | Eigen-Distortions of Hierarchical Representations | cs.CV | We develop a method for comparing hierarchical image representations in terms
of their ability to explain perceptual sensitivity in humans. Specifically, we
utilize Fisher information to establish a model-derived prediction of
sensitivity to local perturbations of an image. For a given image, we compute
the eigenvector... | computer science |
29,706 | Detecting the Moment of Completion: Temporal Models for Localising
Action Completion | cs.CV | Action completion detection is the problem of modelling the action's
progression towards localising the moment of completion - when the action's
goal is confidently considered achieved. In this work, we assess the ability of
two temporal models, namely Hidden Markov Models (HMM) and Long-Short Term
Memory (LSTM), to lo... | computer science |
29,707 | Human Pose Regression by Combining Indirect Part Detection and
Contextual Information | cs.CV | In this paper, we propose an end-to-end trainable regression approach for
human pose estimation from still images. We use the proposed Soft-argmax
function to convert feature maps directly to joint coordinates, resulting in a
fully differentiable framework. Our method is able to learn heat maps
representations indirect... | computer science |
29,708 | Contrastive Learning for Image Captioning | cs.CV | Image captioning, a popular topic in computer vision, has achieved
substantial progress in recent years. However, the distinctiveness of natural
descriptions is often overlooked in previous work. It is closely related to the
quality of captions, as distinctive captions are more likely to describe images
with their uniq... | computer science |
29,709 | CAMREP- Concordia Action and Motion Repository | cs.CV | Action recognition, motion classification, gait analysis and synthesis are
fundamental problems in a number of fields such as computer graphics,
bio-mechanics and human computer interaction that generate a large body of
research. This type of data is complex because it is inherently
multidimensional and has multiple mo... | computer science |
29,710 | Bag-Level Aggregation for Multiple Instance Active Learning in Instance
Classification Problems | cs.CV | A growing number of applications, e.g. video surveillance and medical image
analysis, require training recognition systems from large amounts of weakly
annotated data while some targeted interactions with a domain expert are
allowed to improve the training process. In such cases, active learning (AL)
can reduce labelin... | computer science |
29,711 | A Transfer-Learning Approach for Accelerated MRI using Deep Neural
Networks | cs.CV | Neural network based architectures have recently been proposed for
reconstruction of undersampled MR acquisitions. A deep network containing many
free parameters is typically trained using a relatively large set of
fully-sampled MRI data, and later used for on-line reconstruction of
undersampled data. Ideally network p... | computer science |
29,712 | Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison
for Distorted Images | cs.CV | Fast and robust image matching is a very important task with various
applications in computer vision and robotics. In this paper, we compare the
performance of three different image matching techniques, i.e., SIFT, SURF, and
ORB, against different kinds of transformations and deformations such as
scaling, rotation, noi... | computer science |
29,713 | Image Identification Using SIFT Algorithm: Performance Analysis against
Different Image Deformations | cs.CV | Image identification is one of the most challenging tasks in different areas
of computer vision. Scale-invariant feature transform is an algorithm to detect
and describe local features in images to further use them as an image matching
criteria. In this paper, the performance of the SIFT matching algorithm against
vari... | computer science |
29,714 | Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with
Small Deep-Neural-Network Architectures | cs.CV | Over the last five years Deep Neural Nets have offered more accurate
solutions to many problems in speech recognition, and computer vision, and
these solutions have surpassed a threshold of acceptability for many
applications. As a result, Deep Neural Networks have supplanted other
approaches to solving problems in the... | computer science |
29,715 | Micro-Expression Spotting: A Benchmark | cs.CV | Micro-expressions are rapid and involuntary facial expressions, which
indicate the suppressed or concealed emotions. Recently, the research on
automatic micro-expression (ME) spotting obtains increasing attention. ME
spotting is a crucial step prior to further ME analysis tasks. The spotting
results can be used as impo... | computer science |
29,716 | Gender and Ethnicity Classification of Iris Images using Deep
Class-Encoder | cs.CV | Soft biometric modalities have shown their utility in different applications
including reducing the search space significantly. This leads to improved
recognition performance, reduced computation time, and faster processing of
test samples. Some common soft biometric modalities are ethnicity, gender, age,
hair color, i... | computer science |
29,717 | On Matching Skulls to Digital Face Images: A Preliminary Approach | cs.CV | Forensic application of automatically matching skull with face images is an
important research area linking biometrics with practical applications in
forensics. It is an opportunity for biometrics and face recognition researchers
to help the law enforcement and forensic experts in giving an identity to
unidentified hum... | computer science |
29,718 | UG^2: a Video Benchmark for Assessing the Impact of Image Restoration
and Enhancement on Automatic Visual Recognition | cs.CV | Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal ... | computer science |
29,719 | Face Sketch Matching via Coupled Deep Transform Learning | cs.CV | Face sketch to digital image matching is an important challenge of face
recognition that involves matching across different domains. Current research
efforts have primarily focused on extracting domain invariant representations
or learning a mapping from one domain to the other. In this research, we
propose a novel tra... | computer science |
29,720 | Does Normalization Methods Play a Role for Hyperspectral Image
Classification? | cs.CV | For Hyperspectral image (HSI) datasets, each class have their salient feature
and classifiers classify HSI datasets according to the class's saliency
features, however, there will be different salient features when use different
normalization method. In this letter, we report the effect on classifiers by
different norm... | computer science |
29,721 | Age Group and Gender Estimation in the Wild with Deep RoR Architecture | cs.CV | Automatically predicting age group and gender from face images acquired in
unconstrained conditions is an important and challenging task in many
real-world applications. Nevertheless, the conventional methods with
manually-designed features on in-the-wild benchmarks are unsatisfactory because
of incompetency to tackle ... | computer science |
29,722 | Personalized Saliency and its Prediction | cs.CV | Almost all existing visual saliency models focus on predicting a universal
saliency map across all observers. Yet psychology studies suggest that visual
attention of different observers can vary a lot under some specific
circumstances, especially when they view scenes with multiple salient objects.
However, few work ex... | computer science |
29,723 | An automatic deep learning approach for coronary artery calcium
segmentation | cs.CV | Coronary artery calcium (CAC) is a significant marker of atherosclerosis and
cardiovascular events. In this work we present a system for the automatic
quantification of calcium score in ECG-triggered non-contrast enhanced cardiac
computed tomography (CT) images. The proposed system uses a supervised deep
learning algor... | computer science |
29,724 | A Sequential Thinning Algorithm For Multi-Dimensional Binary Patterns | cs.CV | Thinning is the removal of contour pixels/points of connected components in
an image to produce their skeleton with retained connectivity and structural
properties. The output requirements of a thinning procedure often vary with
application. This paper proposes a sequential algorithm that is very easy to
understand and... | computer science |
29,725 | A Bottom Up Procedure for Text Line Segmentation of Latin Script | cs.CV | In this paper we present a bottom up procedure for segmentation of text lines
written or printed in the Latin script. The proposed method uses a combination
of image morphology, feature extraction and Gaussian mixture model to perform
this task. The experimental results show the validity of the procedure. | computer science |
29,726 | Deeper, Broader and Artier Domain Generalization | cs.CV | The problem of domain generalization is to learn from multiple training
domains, and extract a domain-agnostic model that can then be applied to an
unseen domain. Domain generalization (DG) has a clear motivation in contexts
where there are target domains with distinct characteristics, yet sparse data
for training. For... | computer science |
29,727 | Handwritten digit string recognition by combination of residual network
and RNN-CTC | cs.CV | Recurrent neural network (RNN) and connectionist temporal classification
(CTC) have showed successes in many sequence labeling tasks with the strong
ability of dealing with the problems where the alignment between the inputs and
the target labels is unknown. Residual network is a new structure of
convolutional neural n... | computer science |
29,728 | Island Loss for Learning Discriminative Features in Facial Expression
Recognition | cs.CV | Over the past few years, Convolutional Neural Networks (CNNs) have shown
promise on facial expression recognition. However, the performance degrades
dramatically under real-world settings due to variations introduced by subtle
facial appearance changes, head pose variations, illumination changes, and
occlusions.
In t... | computer science |
29,729 | Person Recognition in Social Media Photos | cs.CV | People nowadays share large parts of their personal lives through social
media. Being able to automatically recognise people in personal photos may
greatly enhance user convenience by easing photo album organisation. For human
identification task, however, traditional focus of computer vision has been
face recognition ... | computer science |
29,730 | iVQA: Inverse Visual Question Answering | cs.CV | We propose the inverse problem of Visual question answering (iVQA), and
explore its suitability as a benchmark for visuo-linguistic understanding. The
iVQA task is to generate a question that corresponds to a given image and
answer pair. Since the answers are less informative than the questions, and the
questions have ... | computer science |
29,731 | Real-Time Action Detection in Video Surveillance using Sub-Action
Descriptor with Multi-CNN | cs.CV | When we say a person is texting, can you tell the person is walking or
sitting? Emphatically, no. In order to solve this incomplete representation
problem, this paper presents a sub-action descriptor for detailed action
detection. The sub-action descriptor consists of three levels: the posture, the
locomotion, and the ... | computer science |
29,732 | AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text
Recognition | cs.CV | Recognizing text in the wild is a really challenging task because of complex
backgrounds, various illuminations and diverse distortions, even with deep
neural networks (convolutional neural networks and recurrent neural networks).
In the end-to-end training procedure for scene text recognition, the outputs of
deep neur... | computer science |
29,733 | DocEmul: a Toolkit to Generate Structured Historical Documents | cs.CV | We propose a toolkit to generate structured synthetic documents emulating the
actual document production process. Synthetic documents can be used to train
systems to perform document analysis tasks. In our case we address the record
counting task on handwritten structured collections containing a limited number
of exam... | computer science |
29,734 | Automatic Streaming Segmentation of Stereo Video Using Bilateral Space | cs.CV | In this paper, we take advantage of binocular camera and propose an
unsupervised algorithm based on semi-supervised segmentation algorithm and
extracting foreground part efficiently. We creatively embed depth information
into bilateral grid in the graph cut model and achieve considerable segmenting
accuracy in the case... | computer science |
29,735 | Traffic Sign Timely Visual Recognizability Evaluation Based on 3D
Measurable Point Clouds | cs.CV | The timely provision of traffic sign information to drivers is essential for
the drivers to respond, to ensure safe driving, and to avoid traffic accidents
in a timely manner. We proposed a timely visual recognizability quantitative
evaluation method for traffic signs in large-scale transportation environments.
To achi... | computer science |
29,736 | Joint Weakly and Semi-Supervised Deep Learning for Localization and
Classification of Masses in Breast Ultrasound Images | cs.CV | We propose a framework for localization and classification of masses in
breast ultrasound (BUS) images. In particular, we simultaneously use a weakly
annotated dataset and a relatively small strongly annotated dataset to train a
convolutional neural network detector. We have experimentally found that mass
detectors tra... | computer science |
29,737 | DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling
Localization via Fully Convolutional Networks for Solar Panels | cs.CV | The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs... | computer science |
29,738 | Application of Deep Learning in Neuroradiology: Automated Detection of
Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks | cs.CV | Background: Deep learning techniques have achieved high accuracy in image
classification tasks, and there is interest in applicability to neuroimaging
critical findings. This study evaluates the efficacy of 2D deep convolutional
neural networks (DCNNs) for detecting basal ganglia (BG) hemorrhage on
noncontrast head CT.... | computer science |
29,739 | Detect to Track and Track to Detect | cs.CV | Recent approaches for high accuracy detection and tracking of object
categories in video consist of complex multistage solutions that become more
cumbersome each year. In this paper we propose a ConvNet architecture that
jointly performs detection and tracking, solving the task in a simple and
effective way. Our contri... | computer science |
29,740 | FFDNet: Toward a Fast and Flexible Solution for CNN based Image
Denoising | cs.CV | Due to the fast inference and good performance, discriminative learning
methods have been widely studied in image denoising. However, these methods
mostly learn a specific model for each noise level, and require multiple models
for denoising images with different noise levels. They also lack flexibility to
deal with sp... | computer science |
29,741 | Interactive Medical Image Segmentation using Deep Learning with
Image-specific Fine-tuning | cs.CV | Convolutional neural networks (CNNs) have achieved state-of-the-art
performance for automatic medical image segmentation. However, they have not
demonstrated sufficiently accurate and robust results for clinical use. In
addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability... | computer science |
29,742 | An Innovative Salient Object Detection Using Center-Dark Channel Prior | cs.CV | Saliency detection aims to detect the most attractive objects in images,
which has been widely used as a foundation for various multimedia applications.
In this paper, we propose a novel salient object detection algorithm for RGB-D
images using center-dark channel prior. First, we generate an initial saliency
map based... | computer science |
29,743 | Local Radon Descriptors for Image Search | cs.CV | Radon transform and its inverse operation are important techniques in medical
imaging tasks. Recently, there has been renewed interest in Radon transform for
applications such as content-based medical image retrieval. However, all
studies so far have used Radon transform as a global or quasi-global image
descriptor by ... | computer science |
29,744 | Recognizing Daily Activities from Egocentric Photo-Streams | cs.CV | Wearable cameras can gather large amounts of image data that provide rich
visual information about the daily activities of the wearer. Motivated by the
large number of health applications that could be enabled by the automatic
recognition of daily activities, such as lifestyle characterization for habit
improvement, co... | computer science |
29,745 | On Data-Driven Saak Transform | cs.CV | Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere)
transform, we develop a data-driven Saak (Subspace approximation with augmented
kernels) transform in this work. The Saak transform consists of three steps: 1)
building the optimal linear subspace approximation with orthonormal bases using
the... | computer science |
29,746 | Joint Image Filtering with Deep Convolutional Networks | cs.CV | Joint image filters leverage the guidance image as a prior and transfer the
structural details from the guidance image to the target image for suppressing
noise or enhancing spatial resolution. Existing methods either rely on various
explicit filter constructions or hand-designed objective functions, thereby
making it ... | computer science |
29,747 | A Finite Element Computational Framework for Active Contours on Graphs | cs.CV | In this paper we present a new framework for the solution of active contour
models on graphs. With the use of the Finite Element Method we generalize
active contour models on graphs and reduce the problem from a partial
differential equation to the solution of a sparse non-linear system.
Additionally, we extend the pro... | computer science |
29,748 | VOIDD: automatic vessel of intervention dynamic detection in PCI
procedures | cs.CV | In this article, we present the work towards improving the overall workflow
of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the
imaging instruments to precisely monitor the steps of the procedure. In the
long term, such capabilities can be used to optimize the image acquisition to
reduce the... | computer science |
29,749 | Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic
Liver and Tumor Segmentation | cs.CV | Automatic segmentation of liver and its tumors is an essential step for
extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017
Liver Tumor Segmentation Challenge (LiTS) provides a common platform for
comparing different au... | computer science |
29,750 | Analysis of planar ornament patterns via motif asymmetry assumption and
local connections | cs.CV | Planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which
exhibit translational symmetry in two independent directions. There are exactly
$17$ distinct planar symmetry groups. We present a fully automatic method for
complete analysis of planar ornaments in $13$ of these groups, specifically,
the groups... | computer science |
29,751 | Progressive Representation Adaptation for Weakly Supervised Object
Localization | cs.CV | We address the problem of weakly supervised object localization where only
image-level annotations are available for training object detectors. Numerous
methods have been proposed to tackle this problem through mining object
proposals. However, a substantial amount of noise in object proposals causes
ambiguities for le... | computer science |
29,752 | Hyperspectral band selection using genetic algorithm and support vector
machines for early identification of charcoal rot disease in soybean | cs.CV | Charcoal rot is a fungal disease that thrives in warm dry conditions and
affects the yield of soybeans and other important agronomic crops worldwide.
There is a need for robust, automatic and consistent early detection and
quantification of disease symptoms which are important in breeding programs for
the development o... | computer science |
29,753 | Can the early human visual system compete with Deep Neural Networks? | cs.CV | We study and compare the human visual system and state-of-the-art deep neural
networks on classification of distorted images. Different from previous works,
we limit the display time to 100ms to test only the early mechanisms of the
human visual system, without allowing time for any eye movements or other
higher level ... | computer science |
29,754 | Residual Connections Encourage Iterative Inference | cs.CV | Residual networks (Resnets) have become a prominent architecture in deep
learning. However, a comprehensive understanding of Resnets is still a topic of
ongoing research.
A recent view argues that Resnets perform iterative refinement of features.
We attempt to further expose properties of this aspect. To this end, we... | computer science |
29,755 | Retinal Fluid Segmentation and Detection in Optical Coherence Tomography
Images using Fully Convolutional Neural Network | cs.CV | As a non-invasive imaging modality, optical coherence tomography (OCT) can
provide micrometer-resolution 3D images of retinal structures. Therefore it is
commonly used in the diagnosis of retinal diseases associated with edema in and
under the retinal layers. In this paper, a new framework is proposed for the
task of f... | computer science |
29,756 | Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis
of Alzheimer's Disease using structural MR and FDG-PET images | cs.CV | Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic
mild cognitive impairment (MCI) is a common first symptom before the conversion
to clinical impairment where the individual becomes unable to perform
activities of daily living independently. Although there is currently no
treatment available... | computer science |
29,757 | Retinal Vasculature Segmentation Using Local Saliency Maps and
Generative Adversarial Networks For Image Super Resolution | cs.CV | We propose an image super resolution(ISR) method using generative adversarial
networks (GANs) that takes a low resolution input fundus image and generates a
high resolution super resolved (SR) image upto scaling factor of $16$. This
facilitates more accurate automated image analysis, especially for small or
blurred lan... | computer science |
29,758 | VGR-Net: A View Invariant Gait Recognition Network | cs.CV | Biometric identification systems have become immensely popular and important
because of their high reliability and efficiency. However person identification
at a distance, still remains a challenging problem. Gait can be seen as an
essential biometric feature for human recognition and identification. It can be
easily a... | computer science |
29,759 | WeText: Scene Text Detection under Weak Supervision | cs.CV | The requiring of large amounts of annotated training data has become a common
constraint on various deep learning systems. In this paper, we propose a weakly
supervised scene text detection method (WeText) that trains robust and accurate
scene text detection models by learning from unannotated or weakly annotated
data.... | computer science |
29,760 | Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional
Generative Adversarial Nets | cs.CV | In this paper, we propose a method for cloud removal from visible light RGB
satellite images by extending the conditional Generative Adversarial Networks
(cGANs) from RGB images to multispectral images. Satellite images have been
widely utilized for various purposes, such as natural environment monitoring
(pollution, f... | computer science |
29,761 | Dynamic texture recognition using time-causal and time-recursive
spatio-temporal receptive fields | cs.CV | This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate... | computer science |
29,762 | Object Classification in Images of Neoclassical Artifacts Using Deep
Learning | cs.CV | In this paper, we report on our efforts for using Deep Learning for
classifying artifacts and their features in digital visuals as a part of the
Neoclassica framework. It was conceived to provide scholars with new methods
for analyzing and classifying artifacts and aesthetic forms from the era of
Classicism. The framew... | computer science |
29,763 | Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017
International Symposium on Biomedical Imaging (ISBI), Hosted by the
International Skin Imaging Collaboration (ISIC) | cs.CV | This article describes the design, implementation, and results of the latest
installment of the dermoscopic image analysis benchmark challenge. The goal is
to support research and development of algorithms for automated diagnosis of
melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks:
lesion s... | computer science |
29,764 | Improving Shadow Suppression for Illumination Robust Face Recognition | cs.CV | 2D face analysis techniques, such as face landmarking, face recognition and
face verification, are reasonably dependent on illumination conditions which
are usually uncontrolled and unpredictable in the real world. An illumination
robust preprocessing method thus remains a significant challenge in reliable
face analysi... | computer science |
29,765 | An adaptive thresholding approach for automatic optic disk segmentation | cs.CV | Optic disk segmentation is a prerequisite step in automatic retinal screening
systems. In this paper, we propose an algorithm for optic disk segmentation
based on a local adaptive thresholding method. Location of the optic disk is
validated by intensity and average vessel width of retinal images. Then an
adaptive thres... | computer science |
29,766 | Video Classification With CNNs: Using The Codec As A Spatio-Temporal
Activity Sensor | cs.CV | We investigate video classification via a two-stream convolutional neural
network (CNN) design that directly ingests information extracted from
compressed video bitstreams. Our approach begins with the observation that all
modern video codecs divide the input frames into macroblocks (MBs). We
demonstrate that selective... | computer science |
29,767 | Hierarchical semantic segmentation using modular convolutional neural
networks | cs.CV | Image recognition tasks that involve identifying parts of an object or the
contents of a vessel can be viewed as a hierarchical problem, which can be
solved by initial recognition of the main object, followed by recognition of
its parts or contents. To achieve such modular recognition, it is necessary to
use the output... | computer science |
29,768 | GHCLNet: A Generalized Hierarchically tuned Contact Lens detection
Network | cs.CV | Iris serves as one of the best biometric modality owing to its complex,
unique and stable structure. However, it can still be spoofed using fabricated
eyeballs and contact lens. Accurate identification of contact lens is must for
reliable performance of any biometric authentication system based on this
modality. In thi... | computer science |
29,769 | BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography
Data into Anatomically Meaningful Clusters | cs.CV | The segregation of brain fiber tractography data into distinct and
anatomically meaningful clusters can help to comprehend the complex brain
structure and early investigation and management of various neural disorders.
We propose a novel stacked bidirectional long short-term memory(LSTM) based
segmentation network, (Br... | computer science |
29,770 | Co-saliency Detection for RGBD Images Based on Multi-constraint Feature
Matching and Cross Label Propagation | cs.CV | Co-saliency detection aims at extracting the common salient regions from an
image group containing two or more relevant images. It is a newly emerging
topic in computer vision community. Different from the most existing
co-saliency methods focusing on RGB images, this paper proposes a novel
co-saliency detection model ... | computer science |
29,771 | Saliency Detection for Stereoscopic Images Based on Depth Confidence
Analysis and Multiple Cues Fusion | cs.CV | Stereoscopic perception is an important part of human visual system that
allows the brain to perceive depth. However, depth information has not been
well explored in existing saliency detection models. In this letter, a novel
saliency detection method for stereoscopic images is proposed. Firstly, we
propose a measure t... | computer science |
29,772 | Microaneurysm Detection in Fundus Images Using a Two-step Convolutional
Neural Networks | cs.CV | Diabetic Retinopathy (DR) is the prominent cause of blindness in the world.
The early treatment of DR can be conducted from detection of microaneurysms
(MA) which is reddish spots in retina images. Automated microaneurysm detection
can be a helpful system for ophthalmologists for detection of MA. In this
paper, deep le... | computer science |
29,773 | K-means clustering for efficient and robust registration of multi-view
point sets | cs.CV | Efficiency and robustness are the important performance for the registration
of multi-view point sets. To address these two issues, this paper casts the
multi-view registration into a clustering problem, which can be solved by the
extended K-means clustering algorithm. Before the clustering, all the centroids
are unifo... | computer science |
29,774 | An Adaptive Framework for Missing Depth Inference Using Joint Bilateral
Filter | cs.CV | Depth imaging has largely focused on sensor and intrinsics properties.
However, the accuracy of acquire pixel is largely dependent on the capture. We
propose a new depth estimation and approximation algorithm which takes an
arbitrary 3D point cloud as input, with potentially complex geometric
structures, and generates ... | computer science |
29,775 | Deep Learning for Rapid Sparse MR Fingerprinting Reconstruction | cs.CV | PURPOSE: Demonstrate a novel fast method for reconstruction of
multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods.
METHODS: A neural network (NN) is defined using the TensorFlow framework and
trained on simulated MRF data computed using the Bloch equations. The accuracy
of the NN reconstructio... | computer science |
29,776 | Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT
using Cascaded 3D Fully Convolutional Network | cs.CV | Automatic multi-organ segmentation of the dual energy computed tomography
(DECT) data can be beneficial for biomedical research and clinical
applications. However, it is a challenging task. Recent advances in deep
learning showed the feasibility to use 3-D fully convolutional networks (FCN)
for voxel-wise dense predict... | computer science |
29,777 | Vehicle classification based on convolutional networks applied to FM-CW
radar signals | cs.CV | This paper investigates the processing of Frequency Modulated-Continuos Wave
(FM-CW) radar signals for vehicle classification. In the last years deep
learning has gained interest in several scientific fields and signal processing
is not one exception. In this work we address the recognition of the vehicle
category usin... | computer science |
29,778 | Convolutional Neural Networks for Histopathology Image Classification:
Training vs. Using Pre-Trained Networks | cs.CV | We explore the problem of classification within a medical image data-set
based on a feature vector extracted from the deepest layer of pre-trained
Convolution Neural Networks. We have used feature vectors from several
pre-trained structures, including networks with/without transfer learning to
evaluate the performance ... | computer science |
29,779 | Isointense Infant Brain Segmentation with a Hyper-dense Connected
Convolutional Neural Network | cs.CV | Neonatal brain segmentation in magnetic resonance (MR) is a challenging
problem due to poor image quality and low contrast between white and gray
matter regions. Most existing approaches for this problem are based on
multi-atlas label fusion strategies, which are time-consuming and sensitive to
registration errors. As ... | computer science |
29,780 | Volumetric Data Exploration with Machine Learning-Aided Visualization in
Neutron Science | cs.CV | Recent advancements in neutron and x-ray sources, instrumentation and data
collection modes have significantly increased the experimental data size (which
could easily contain $10^{8}$-$10^{10}$ points), so that conventional
volumetric visualization approaches become inefficient for both still imaging
and interactive O... | computer science |
29,781 | Face Transfer with Generative Adversarial Network | cs.CV | Face transfer animates the facial performances of the character in the target
video by a source actor. Traditional methods are typically based on face
modeling. We propose an end-to-end face transfer method based on Generative
Adversarial Network. Specifically, we leverage CycleGAN to generate the face
image of the tar... | computer science |
29,782 | Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet
Core55 | cs.CV | We introduce a large-scale 3D shape understanding benchmark using data and
annotation from ShapeNet 3D object database. The benchmark consists of two
tasks: part-level segmentation of 3D shapes and 3D reconstruction from single
view images. Ten teams have participated in the challenge and the best
performing teams have... | computer science |
29,783 | Scalable Dense Monocular Surface Reconstruction | cs.CV | This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalab... | computer science |
29,784 | Combining LiDAR Space Clustering and Convolutional Neural Networks for
Pedestrian Detection | cs.CV | Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches,... | computer science |
29,785 | Learning to Learn Image Classifiers with Informative Visual Analogy | cs.CV | In recent years, we witnessed a huge success of Convolutional Neural Networks
on the task of the image classification. However, these models are notoriously
data hungry and require tons of training images to learn the parameters. In
contrast, people are far better learner who can learn a new concept very fast
with only... | computer science |
29,786 | Single Shot Temporal Action Detection | cs.CV | Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-a... | computer science |
29,787 | Procedural Modeling and Physically Based Rendering for Synthetic Data
Generation in Automotive Applications | cs.CV | We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image s... | computer science |
29,788 | A Deep Learning Approach for Reconstruction Filter Kernel Discretization | cs.CV | In this paper, we present substantial evidence that a deep neural network
will intrinsically learn the appropriate way to discretize the ideal continuous
reconstruction filter. Currently, the Ram-Lak filter or heuristic filters which
impose different noise assumptions are used for filtered back-projection. All
of these... | computer science |
29,789 | VPGNet: Vanishing Point Guided Network for Lane and Road Marking
Detection and Recognition | cs.CV | In this paper, we propose a unified end-to-end trainable multi-task network
that jointly handles lane and road marking detection and recognition that is
guided by a vanishing point under adverse weather conditions. We tackle rainy
and low illumination conditions, which have not been extensively studied until
now due to... | computer science |
29,790 | Superpixels Based Marker Tracking Vs. Hue Thresholding In Rodent
Biomechanics Application | cs.CV | Examining locomotion has improved our basic understanding of motor control
and aided in treating motor impairment. Mice and rats are premier models of
human disease and increasingly the model systems of choice for basic
neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics
of these running roden... | computer science |
29,791 | Do Convolutional Neural Networks Learn Class Hierarchy? | cs.CV | Convolutional Neural Networks (CNNs) currently achieve state-of-the-art
accuracy in image classification. With a growing number of classes, the
accuracy usually drops as the possibilities of confusion increase.
Interestingly, the class confusion patterns follow a hierarchical structure
over the classes. We present visu... | computer science |
29,792 | Scene Parsing with Global Context Embedding | cs.CV | We present a scene parsing method that utilizes global context information
based on both the parametric and non- parametric models. Compared to previous
methods that only exploit the local relationship between objects, we train a
context network based on scene similarities to generate feature representations
for global... | computer science |
29,793 | Multi-focus image fusion using VOL and EOL in DCT domain | cs.CV | The purpose of multi-focus image fusion is gathering the essential
information and the focused parts from the input multi-focus images into a
single image. These multi-focused images are captured with different depths of
focus of cameras. Multi-focus image fusion is very time-saving and appropriate
in discrete cosine t... | computer science |
29,794 | Pose-based Deep Gait Recognition | cs.CV | Human gait or walking manner is a biometric feature that allows
identification of a person when other biometric features such as the face or
iris are not visible. In this paper, we present a new pose-based convolutional
neural network model for gait recognition. Unlike many methods that consider
the full-height silhoue... | computer science |
29,795 | Learning Deep Context-aware Features over Body and Latent Parts for
Person Re-identification | cs.CV | Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design ... | computer science |
29,796 | Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests
with CNN-based Hypothesis Selection | cs.CV | Automated segmentation approaches are crucial to quantitatively analyze
large-scale 3D microscopy images. Particularly in deep tissue regions,
automatic methods still fail to provide error-free segmentations. To improve
the segmentation quality throughout imaged samples, we present a new
supervoxel-based 3D segmentatio... | computer science |
29,797 | The Robust Reading Competition Annotation and Evaluation Platform | cs.CV | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and
re-established in 2011, has become the de-facto evaluation standard for the
international community.
Concurrent with its second incarnation in 2011, a continuous effort started
to develop an online framework to facilitate the hosting and management of
... | computer science |
29,798 | Simultaneous Recognition and Pose Estimation of Instruments in Minimally
Invasive Surgery | cs.CV | Detection of surgical instruments plays a key role in ensuring patient safety
in minimally invasive surgery. In this paper, we present a novel method for 2D
vision-based recognition and pose estimation of surgical instruments that
generalizes to different surgical applications. At its core, we propose a novel
scene mod... | computer science |
29,799 | Dropout Sampling for Robust Object Detection in Open-Set Conditions | cs.CV | Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label u... | computer science |
29,800 | Enhancing the Performance of Convolutional Neural Networks on Quality
Degraded Datasets | cs.CV | Despite the appeal of deep neural networks that largely replace the
traditional handmade filters, they still suffer from isolated cases that cannot
be properly handled only by the training of convolutional filters. Abnormal
factors, including real-world noise, blur, or other quality degradations, ruin
the output of a n... | computer science |
29,801 | Identifying Mild Traumatic Brain Injury Patients From MR Images Using
Bag of Visual Words | cs.CV | Mild traumatic brain injury (mTBI) is a growing public health problem with an
estimated incidence of one million people annually in US. Neurocognitive tests
are used to both assess the patient condition and to monitor the patient
progress. This work aims to directly use MR images taken shortly after injury
to detect wh... | computer science |
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