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30,602 | Light Field Segmentation From Super-pixel Graph Representation | cs.CV | Efficient and accurate segmentation of light field is an important task in
computer vision and graphics. The large volume of input data and the redundancy
of light field make it an open challenge. In the paper, we propose a novel
graph representation for interactive light field segmentation based on light
field super-p... | computer science |
30,603 | Recurrent Attentional Reinforcement Learning for Multi-label Image
Recognition | cs.CV | Recognizing multiple labels of images is a fundamental but challenging task
in computer vision, and remarkable progress has been attained by localizing
semantic-aware image regions and predicting their labels with deep
convolutional neural networks. The step of hypothesis regions (region
proposals) localization in thes... | computer science |
30,604 | Accurate 3D Reconstruction of Dynamic Scenes from Monocular Image
Sequences with Severe Occlusions | cs.CV | The paper introduces an accurate solution to dense orthographic Non-Rigid
Structure from Motion (NRSfM) in scenarios with severe occlusions or, likewise,
inaccurate correspondences. We integrate a shape prior term into variational
optimisation framework. It allows to penalize irregularities of the
time-varying structur... | computer science |
30,605 | Attribute CNNs for Word Spotting in Handwritten Documents | cs.CV | Word spotting has become a field of strong research interest in document
image analysis over the last years. Recently, AttributeSVMs were proposed which
predict a binary attribute representation. At their time, this influential
method defined the state-of-the-art in segmentation-based word spotting. In
this work, we pr... | computer science |
30,606 | Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative
Learning | cs.CV | Gastric cancer is the second leading cause of cancer-related deaths
worldwide, and the major hurdle in biomedical image analysis is the
determination of the cancer extent. This assignment has high clinical relevance
and would generally require vast microscopic assessment by pathologists. Recent
advances in deep learnin... | computer science |
30,607 | Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks | cs.CV | Accelerating deep neural networks (DNNs) has been attracting increasing
attention as it can benefit a wide range of applications, e.g., enabling mobile
systems with limited computing resources to own powerful visual recognition
ability. A practical strategy to this goal usually relies on a two-stage
process: operating ... | computer science |
30,608 | Learning to Act Properly: Predicting and Explaining Affordances from
Images | cs.CV | We address the problem of affordance reasoning in diverse scenes that appear
in the real world. Affordances relate the agent's actions to their effects when
taken on the surrounding objects. In our work, we take the egocentric view of
the scene, and aim to reason about action-object affordances that respect both
the ph... | computer science |
30,609 | SuperPoint: Self-Supervised Interest Point Detection and Description | cs.CV | This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level... | computer science |
30,610 | Adversarial Synthesis Learning Enables Segmentation Without Target
Modality Ground Truth | cs.CV | A lack of generalizability is one key limitation of deep learning based
segmentation. Typically, one manually labels new training images when
segmenting organs in different imaging modalities or segmenting abnormal organs
from distinct disease cohorts. The manual efforts can be alleviated if one is
able to reuse manual... | computer science |
30,611 | An Order Preserving Bilinear Model for Person Detection in Multi-Modal
Data | cs.CV | We propose a new order preserving bilinear framework that exploits
low-resolution video for person detection in a multi-modal setting using deep
neural networks. In this setting cameras are strategically placed such that
less robust sensors, e.g. geophones that monitor seismic activity, are located
within the field of ... | computer science |
30,612 | Enhance Visual Recognition under Adverse Conditions via Deep Networks | cs.CV | Visual recognition under adverse conditions is a very important and
challenging problem of high practical value, due to the ubiquitous existence of
quality distortions during image acquisition, transmission, or storage. While
deep neural networks have been extensively exploited in the techniques of
low-quality image re... | computer science |
30,613 | Automatic Estimation of Ice Bottom Surfaces from Radar Imagery | cs.CV | Ground-penetrating radar on planes and satellites now makes it practical to
collect 3D observations of the subsurface structure of the polar ice sheets,
providing crucial data for understanding and tracking global climate change.
But converting these noisy readings into useful observations is generally done
by hand, wh... | computer science |
30,614 | Context-Aware Semantic Inpainting | cs.CV | Recently image inpainting has witnessed rapid progress due to generative
adversarial networks (GAN) that are able to synthesize realistic contents.
However, most existing GAN-based methods for semantic inpainting apply an
auto-encoder architecture with a fully connected layer, which cannot accurately
maintain spatial i... | computer science |
30,615 | Deep learning for predicting refractive error from retinal fundus images | cs.CV | Refractive error, one of the leading cause of visual impairment, can be
corrected by simple interventions like prescribing eyeglasses. We trained a
deep learning algorithm to predict refractive error from the fundus photographs
from participants in the UK Biobank cohort, which were 45 degree field of view
images and th... | computer science |
30,616 | Exploring Models and Data for Remote Sensing Image Caption Generation | cs.CV | Inspired by recent development of artificial satellite, remote sensing images
have attracted extensive attention. Recently, noticeable progress has been made
in scene classification and target detection.However, it is still not clear how
to describe the remote sensing image content with accurate and concise
sentences. ... | computer science |
30,617 | Simulating Patho-realistic Ultrasound Images using Deep Generative
Networks with Adversarial Learning | cs.CV | Ultrasound imaging makes use of backscattering of waves during their
interaction with scatterers present in biological tissues. Simulation of
synthetic ultrasound images is a challenging problem on account of inability to
accurately model various factors of which some include intra-/inter scanline
interference, transdu... | computer science |
30,618 | Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking | cs.CV | The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtainin... | computer science |
30,619 | Encoding CNN Activations for Writer Recognition | cs.CV | The encoding of local features is an essential part for writer identification
and writer retrieval. While CNN activations have already been used as local
features in related works, the encoding of these features has attracted little
attention so far. In this work, we compare the established VLAD encoding with
triangula... | computer science |
30,620 | Human Action Recognition: Pose-based Attention draws focus to Hands | cs.CV | We propose a new spatio-temporal attention based mechanism for human action
recognition able to automatically attend to the hands most involved into the
studied action and detect the most discriminative moments in an action.
Attention is handled in a recurrent manner employing Recurrent Neural Network
(RNN) and is full... | computer science |
30,621 | Siamese Neural Networks for One-shot detection of Railway Track Switches | cs.CV | Deep Learning methods have been extensively used to analyze video data to
extract valuable information by classifying image frames and detecting objects.
We describe a unique approach for using video feed from a moving Locomotive to
continuously monitor the Railway Track and detect significant assets like
Switches on t... | computer science |
30,622 | Learning Intelligent Dialogs for Bounding Box Annotation | cs.CV | We introduce Intelligent Annotation Dialogs for bounding box annotation. We
train an agent to automatically choose a sequence of actions for a human
annotator to produce a bounding box in a minimal amount of time. Specifically,
we consider two actions: box verification [37], where the annotator verifies a
box generated... | computer science |
30,623 | Smart, Sparse Contours to Represent and Edit Images | cs.CV | We study the problem of reconstructing an image from information stored at
sparse contour locations. Existing contour-based image reconstruction methods
struggle to balance contour sparsity and reconstruction fidelity. Therefore,
denser contours are needed to capture subtle texture information even though
contours were... | computer science |
30,624 | Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks | cs.CV | This work shows that it is possible to fool/attack recent state-of-the-art
face detectors which are based on the single-stage networks. Successfully
attacking face detectors could be a serious malware vulnerability when
deploying a smart surveillance system utilizing face detectors. We show that
existing adversarial pe... | computer science |
30,625 | Beyond saliency: understanding convolutional neural networks from
saliency prediction on layer-wise relevance propagation | cs.CV | Despite the tremendous achievements of deep convolutional neural networks
(CNNs) in most of computer vision tasks, understanding how they actually work
remains a significant challenge. In this paper, we propose a novel two-step
visualization method that aims to shed light on how deep CNNs recognize images
and the objec... | computer science |
30,626 | A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering | cs.CV | The bandwidth of a kernel function is a crucial parameter in the mean shift
algorithm. This paper proposes a novel adaptive bandwidth strategy which
contains three main contributions. (1) The differences among different adaptive
bandwidth are analyzed. (2) A new mean shift vector based on bidirectional
adaptive bandwid... | computer science |
30,627 | SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using
Sibling FCN with Objectness Prior Interaction | cs.CV | Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and ro... | computer science |
30,628 | Deep Hashing with Category Mask for Fast Video Retrieval | cs.CV | This paper proposes an end-to-end deep hashing framework with category mask
for fast video retrieval. We train our network in a supervised way by fully
exploiting inter-class diversity and intra-class identity. Classification loss
is optimized to maximize inter-class diversity, while intra-pair is introduced
to learn r... | computer science |
30,629 | The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for
Traffic Vision Research | cs.CV | Video image datasets are playing an essential role in design and evaluation
of traffic vision algorithms. Nevertheless, a longstanding inconvenience
concerning image datasets is that manually collecting and annotating
large-scale diversified datasets from real scenes is time-consuming and prone
to error. For that virtu... | computer science |
30,630 | On the Integration of Optical Flow and Action Recognition | cs.CV | Most of the top performing action recognition methods use optical flow as a
"black box" input. Here we take a deeper look at the combination of flow and
action recognition, and investigate why optical flow is helpful, what makes a
flow method good for action recognition, and how we can make it better. In
particular, we... | computer science |
30,631 | Simple Methods for Scanner Drift Normalization Validated for Automatic
Segmentation of Knee Magnetic Resonance Imaging - with data from the
Osteoarthritis Initiative | cs.CV | Scanner drift is a well-known magnetic resonance imaging (MRI) artifact
characterized by gradual signal degradation and scan intensity changes over
time. In addition, hardware and software updates may imply abrupt changes in
signal. The combined effects are particularly challenging for automatic image
analysis methods ... | computer science |
30,632 | Training and Testing Object Detectors with Virtual Images | cs.CV | In the area of computer vision, deep learning has produced a variety of
state-of-the-art models that rely on massive labeled data. However, collecting
and annotating images from the real world has a great demand for labor and
money investments and is usually too passive to build datasets with specific
characteristics, ... | computer science |
30,633 | Automated Surgical Skill Assessment in RMIS Training | cs.CV | Purpose: Manual feedback in basic RMIS training can consume a significant
amount of time from expert surgeons' schedule and is prone to subjectivity.
While VR-based training tasks can generate automated score reports, there is no
mechanism of generating automated feedback for surgeons performing basic
surgical tasks in... | computer science |
30,634 | Aerial Spectral Super-Resolution using Conditional Adversarial Networks | cs.CV | Inferring spectral signatures from ground based natural images has acquired a
lot of interest in applied deep learning. In contrast to the spectra of ground
based images, aerial spectral images have low spatial resolution and suffer
from higher noise interference. In this paper, we train a conditional
adversarial netwo... | computer science |
30,635 | Denoising of 3D magnetic resonance images with multi-channel residual
learning of convolutional neural network | cs.CV | The denoising of magnetic resonance (MR) images is a task of great importance
for improving the acquired image quality. Many methods have been proposed in
the literature to retrieve noise free images with good performances. Howerever,
the state-of-the-art denoising methods, all needs a time-consuming optimization
proce... | computer science |
30,636 | Combining Weakly and Webly Supervised Learning for Classifying Food
Images | cs.CV | Food classification from images is a fine-grained classification problem.
Manual curation of food images is cost, time and scalability prohibitive. On
the other hand, web data is available freely but contains noise. In this paper,
we address the problem of classifying food images with minimal data curation.
We also tac... | computer science |
30,637 | Scene-Specific Pedestrian Detection Based on Parallel Vision | cs.CV | As a special type of object detection, pedestrian detection in generic scenes
has made a significant progress trained with large amounts of labeled training
data manually. While the models trained with generic dataset work bad when they
are directly used in specific scenes. With special viewpoints, flow light and
backg... | computer science |
30,638 | Large-Scale Object Discovery and Detector Adaptation from Unlabeled
Video | cs.CV | We explore object discovery and detector adaptation based on unlabeled video
sequences captured from a mobile platform. We propose a fully automatic
approach for object mining from video which builds upon a generic object
tracking approach. By applying this method to three large video datasets from
autonomous driving a... | computer science |
30,639 | Texture Synthesis with Recurrent Variational Auto-Encoder | cs.CV | We propose a recurrent variational auto-encoder for texture synthesis. A
novel loss function, FLTBNK, is used for training the texture synthesizer. It
is rotational and partially color invariant loss function. Unlike L2 loss,
FLTBNK explicitly models the correlation of color intensity between pixels. Our
texture synthe... | computer science |
30,640 | Use of Generative Adversarial Network for Cross-Domain Change Detection | cs.CV | This paper addresses the problem of cross-domain change detection from a
novel perspective of image-to-image translation. In general, change detection
aims to identify interesting changes between a given query image and a
reference image of the same scene taken at a different time. This problem
becomes a challenging on... | computer science |
30,641 | Blind Image Deblurring via Reweighted Graph Total Variation | cs.CV | Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, by
interpreting... | computer science |
30,642 | RIDI: Robust IMU Double Integration | cs.CV | This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, o... | computer science |
30,643 | Domain Adaptation Meets Disentangled Representation Learning and Style
Transfer | cs.CV | In order to solve the unsupervised domain adaptation problem, some methods
based on adversarial learning are proposed recently. These methods greatly
attract people's eyes because of the better ability to learn the common
representation space so that the feature distributions among many domains are
ambiguous and non-di... | computer science |
30,644 | Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep
Neural Networks and Cascaded Regression | cs.CV | Despite recent progress, computational visual aesthetic is still challenging.
Image cropping, which refers to the removal of unwanted scene areas, is an
important step to improve the aesthetic quality of an image. However, it is
challenging to evaluate whether cropping leads to aesthetically pleasing
results because th... | computer science |
30,645 | Deep Blind Image Inpainting | cs.CV | Image inpainting is a challenging problem as it needs to fill the information
of the corrupted regions. Most of the existing inpainting algorithms assume
that the positions of the corrupted regions are known. Different from the
existing methods that usually make some assumptions on the corrupted regions,
we present an ... | computer science |
30,646 | Brain Tumor Segmentation Based on Refined Fully Convolutional Neural
Networks with A Hierarchical Dice Loss | cs.CV | As a basic task in computer vision, semantic segmentation can provide
fundamental information for object detection and instance segmentation to help
the artificial intelligence better understand real world. Since the proposal of
fully convolutional neural network (FCNN), it has been widely used in semantic
segmentation... | computer science |
30,647 | Deep Meta Learning for Real-Time Visual Tracking based on
Target-Specific Feature Space | cs.CV | In this paper, we propose a novel on-line visual tracking framework based on
Siamese matching network and meta-learner network which runs at real-time
speed. Conventional deep convolutional feature based discriminative visual
tracking algorithms require continuous re-training of classifiers or
correlation filters for s... | computer science |
30,648 | Segmenting Sky Pixels in Images | cs.CV | Outdoor scene parsing models are often trained on ideal datasets and produce
quality results. However, this leads to a discrepancy when applied to the real
world. The quality of scene parsing, particularly sky classification, decreases
in night time images, images involving varying weather conditions, and scene
changes... | computer science |
30,649 | Detect-and-Track: Efficient Pose Estimation in Videos | cs.CV | This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation i... | computer science |
30,650 | Aircraft Fuselage Defect Detection using Deep Neural Networks | cs.CV | To ensure flight safety of aircraft structures, it is necessary to have
regular maintenance using visual and nondestructive inspection (NDI) methods.
In this paper, we propose an automatic image-based aircraft defect detection
using Deep Neural Networks (DNNs). To the best of our knowledge, this is the
first work for a... | computer science |
30,651 | Large-Scale 3D Scene Classification With Multi-View Volumetric CNN | cs.CV | We introduce a method to classify imagery using a convo- lutional neural
network (CNN) on multi-view image pro- jections. The power of our method comes
from using pro- jections of multiple images at multiple depth planes near the
reconstructed surface. This enables classification of categories whose salient
aspect is a... | computer science |
30,652 | A model for interpreting social interactions in local image regions | cs.CV | Understanding social interactions (such as 'hug' or 'fight') is a basic and
important capacity of the human visual system, but a challenging and still open
problem for modeling. In this work we study visual recognition of social
interactions, based on small but recognizable local regions. The approach is
based on two n... | computer science |
30,653 | Zero-Shot Learning via Latent Space Encoding | cs.CV | Zero-Shot Learning (ZSL) is typically achieved by resorting to a class
semantic embedding space to transfer the knowledge from the seen classes to
unseen ones. Capturing the common semantic characteristics between the visual
modality and the class semantic modality (e.g., attributes or word vector) is a
key to the succ... | computer science |
30,654 | RaspiReader: Open Source Fingerprint Reader | cs.CV | We open source an easy to assemble, spoof resistant, high resolution, optical
fingerprint reader, called RaspiReader, using ubiquitous components. By using
our open source STL files and software, RaspiReader can be built in under one
hour for only US $175. As such, RaspiReader provides the fingerprint research
communit... | computer science |
30,655 | Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint
Domain Knowledge | cs.CV | We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network an... | computer science |
30,656 | Taking Visual Motion Prediction To New Heightfields | cs.CV | While the basic laws of Newtonian mechanics are well understood, explaining a
physical scenario still requires manually modeling the problem with suitable
equations and estimating the associated parameters. In order to be able to
leverage the approximation capabilities of artificial intelligence techniques
in such phys... | computer science |
30,657 | Multi-modal Geolocation Estimation Using Deep Neural Networks | cs.CV | Estimating the location where an image was taken based solely on the contents
of the image is a challenging task, even for humans, as properly labeling an
image in such a fashion relies heavily on contextual information, and is not as
simple as identifying a single object in the image. Thus any methods which
attempt to... | computer science |
30,658 | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent
Progress on DukeMTMC Project | cs.CV | Although many methods perform well in single camera tracking, multi-camera
tracking remains a challenging problem with less attention. DukeMTMC is a
large-scale, well-annotated multi-camera tracking benchmark which makes great
progress in this field. This report is dedicated to briefly introduce our
method on DukeMTMC ... | computer science |
30,659 | Consensus-based Sequence Training for Video Captioning | cs.CV | Captioning models are typically trained using the cross-entropy loss.
However, their performance is evaluated on other metrics designed to better
correlate with human assessments. Recently, it has been shown that
reinforcement learning (RL) can directly optimize these metrics in tasks such
as captioning. However, this ... | computer science |
30,660 | Memory-Efficient Deep Salient Object Segmentation Networks on Gridized
Superpixels | cs.CV | Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of... | computer science |
30,661 | Adversarial Patch | cs.CV | We present a method to create universal, robust, targeted adversarial image
patches in the real world. The patches are universal because they can be used
to attack any scene, robust because they work under a wide variety of
transformations, and targeted because they can cause a classifier to output any
target class. Th... | computer science |
30,662 | Sky detection and log illumination refinement for PDE-based hazy image
contrast enhancement | cs.CV | This report presents the results of a sky detection technique used to improve
the performance of a previously developed partial differential equation
(PDE)-based hazy image enhancement algorithm. Additionally, a proposed
alternative method utilizes a function for log illumination refinement to
improve de-hazing results... | computer science |
30,663 | Efficient Parallel Connected Components Labeling with a Coarse-to-fine
Strategy | cs.CV | This paper proposes a new parallel approach to solve connected components on
a 2D binary image implemented with CUDA. We employ the following strategies to
accelerate neighborhood exploration after dividing an input image into
independent blocks. In the local labeling stage, a coarse-labeling algorithm,
including row-c... | computer science |
30,664 | Siamese LSTM based Fiber Structural Similarity Network (FS2Net) for
Rotation Invariant Brain Tractography Segmentation | cs.CV | In this paper, we propose a novel deep learning architecture combining
stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for
segmentation of brain fibers, obtained from tractography data, into
anatomically meaningful clusters. The proposed network learns the structural
difference between fiber... | computer science |
30,665 | A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote
Sensing Imagery Pan-Sharpening | cs.CV | Pan-sharpening is a fundamental and significant task in the field of remote
sensing imagery processing, in which high-resolution spatial details from
panchromatic images are employed to enhance the spatial resolution of
multi-spectral (MS) images. As the transformation from low spatial resolution
MS image to high-resol... | computer science |
30,666 | Future Frame Prediction for Anomaly Detection -- A New Baseline | cs.CV | Anomaly detection in videos refers to the identification of events that do
not conform to expected behavior. However, almost all existing methods tackle
the problem by minimizing the reconstruction errors of training data, which
cannot guarantee a larger reconstruction error for an abnormal event. In this
paper, we pro... | computer science |
30,667 | Handwritten Bangla Character Recognition Using The State-of-Art Deep
Convolutional Neural Networks | cs.CV | In spite of advances in object recognition technology, Handwritten Bangla
Character Recognition (HBCR) remains largely unsolved due to the presence of
many ambiguous handwritten characters and excessively cursive Bangla
handwritings. Even the best existing recognizers do not lead to satisfactory
performance for practic... | computer science |
30,668 | Improved Inception-Residual Convolutional Neural Network for Object
Recognition | cs.CV | Machine learning and computer vision have driven many of the greatest
advances in the modeling of Deep Convolutional Neural Networks (DCNNs).
Nowadays, most of the research has been focused on improving recognition
accuracy with better DCNN models and learning approaches. The recurrent
convolutional approach is not app... | computer science |
30,669 | Discriminative and Geometry Aware Unsupervised Domain Adaptation | cs.CV | Domain adaptation (DA) aims to generalize a learning model across training
and testing data despite the mismatch of their data distributions. In light of
a theoretical estimation of upper error bound, we argue in this paper that an
effective DA method should 1) search a shared feature subspace where source and
target d... | computer science |
30,670 | Learning Deep and Compact Models for Gesture Recognition | cs.CV | We look at the problem of developing a compact and accurate model for gesture
recognition from videos in a deep-learning framework. Towards this we propose a
joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better
suited to capture the dynamic information in actions. The solution achieves
close to... | computer science |
30,671 | Significance of Softmax-based Features in Comparison to Distance Metric
Learning-based Features | cs.CV | The extraction of useful deep features is important for many computer vision
tasks. Deep features extracted from classification networks have proved to
perform well in those tasks. To obtain features of greater usefulness,
end-to-end distance metric learning (DML) has been applied to train the feature
extractor directl... | computer science |
30,672 | Exploring the significance of using perceptually relevant image
decolorization method for scene classification | cs.CV | A color image contains luminance and chrominance components representing the
intensity and color information respectively. The objective of the work
presented in this paper is to show the significance of incorporating the
chrominance information for the task of scene classification. An improved
color-to-grayscale image... | computer science |
30,673 | Dense Fully Convolutional Network for Skin Lesion Segmentation | cs.CV | Skin cancer is a deadly disease and is on the rise in the world. Computerized
diagnosis of skin cancer can accelerate the detection of this type of cancer
that is a key point in increasing the survival rate of patients. Lesion
segmentation in skin images is an important step in computerized detection of
the skin cancer... | computer science |
30,674 | Learning Deep Similarity Models with Focus Ranking for Fabric Image
Retrieval | cs.CV | Fabric image retrieval is beneficial to many applications including clothing
searching, online shopping and cloth modeling. Learning pairwise image
similarity is of great importance to an image retrieval task. With the
resurgence of Convolutional Neural Networks (CNNs), recent works have achieved
significant progresses... | computer science |
30,675 | ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for
3D Scans | cs.CV | We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data si... | computer science |
30,676 | Deep Reinforcement Learning for Unsupervised Video Summarization with
Diversity-Representativeness Reward | cs.CV | Video summarization aims to facilitate large-scale video browsing by
producing short, concise summaries that are diverse and representative of
original videos. In this paper, we formulate video summarization as a
sequential decision-making process and develop a deep summarization network
(DSN) to summarize videos. DSN ... | computer science |
30,677 | Deformable GANs for Pose-based Human Image Generation | cs.CV | In this paper we address the problem of generating person images conditioned
on a given pose. Specifically, given an image of a person and a target pose, we
synthesize a new image of that person in the novel pose. In order to deal with
pixel-to-pixel misalignments caused by the pose differences, we introduce
deformable... | computer science |
30,678 | Face Synthesis from Visual Attributes via Sketch using Conditional VAEs
and GANs | cs.CV | Automatic synthesis of faces from visual attributes is an important problem
in computer vision and has wide applications in law enforcement and
entertainment. With the advent of deep generative convolutional neural networks
(CNNs), attempts have been made to synthesize face images from attributes and
text descriptions.... | computer science |
30,679 | A Real-time and Registration-free Framework for Dynamic Shape
Instantiation | cs.CV | Real-time 3D navigation during minimally invasive procedures is an essential
yet challenging task, especially when considerable tissue motion is involved.
To balance image acquisition speed and resolution, only 2D images or
low-resolution 3D volumes can be used clinically. In this paper, a real-time
and registration-fr... | computer science |
30,680 | Fractional Local Neighborhood Intensity Pattern for Image Retrieval
using Genetic Algorithm | cs.CV | In this paper, a new texture descriptor named "Fractional Local Neighborhood
Intensity Pattern" (FLNIP) has been proposed for content based image retrieval
(CBIR). It is an extension of the Local Neighborhood Intensity Pattern
(LNIP)[1]. FLNIP calculates the relative intensity difference between a
particular pixel and ... | computer science |
30,681 | A Unified Method for First and Third Person Action Recognition | cs.CV | In this paper, a new video classification methodology is proposed which can
be applied in both first and third person videos. The main idea behind the
proposed strategy is to capture complementary information of appearance and
motion efficiently by performing two independent streams on the videos. The
first stream is a... | computer science |
30,682 | Integrating semi-supervised label propagation and random forests for
multi-atlas based hippocampus segmentation | cs.CV | A novel multi-atlas based image segmentation method is proposed by
integrating a semi-supervised label propagation method and a supervised random
forests method in a pattern recognition based label fusion framework. The
semi-supervised label propagation method takes into consideration local and
global image appearance ... | computer science |
30,683 | Transfer learning for diagnosis of congenital abnormalities of the
kidney and urinary tract in children based on Ultrasound imaging data | cs.CV | Classification of ultrasound (US) kidney images for diagnosis of congenital
abnormalities of the kidney and urinary tract (CAKUT) in children is a
challenging task. It is desirable to improve existing pattern classification
models that are built upon conventional image features. In this study, we
propose a transfer lea... | computer science |
30,684 | Context aware saliency map generation using semantic segmentation | cs.CV | Saliency map detection, as a method for detecting important regions of an
image, is used in many applications such as image classification and
recognition. We propose that context detection could have an essential role in
image saliency detection. This requires extraction of high level features. In
this paper a salienc... | computer science |
30,685 | Interactive Video Object Segmentation in the Wild | cs.CV | In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is imp... | computer science |
30,686 | Deep Stacked Networks with Residual Polishing for Image Inpainting | cs.CV | Deep neural networks have shown promising results in image inpainting even if
the missing area is relatively large. However, most of the existing inpainting
networks introduce undesired artifacts and noise to the repaired regions. To
solve this problem, we present a novel framework which consists of two stacked
convolu... | computer science |
30,687 | Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic
Resonance Images | cs.CV | This paper presents an end-to-end solution for MRI thigh quadriceps
segmentation. This is the first attempt that deep learning methods are used for
the MRI thigh segmentation task. We use the state-of-the-art Fully
Convolutional Networks with transfer learning approach for the semantic
segmentation of regions of intere... | computer science |
30,688 | Facial emotion recognition using min-max similarity classifier | cs.CV | Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recordin... | computer science |
30,689 | Quality assessment metrics for edge detection and edge-aware filtering:
A tutorial review | cs.CV | The quality assessment of edges in an image is an important topic as it helps
to benchmark the performance of edge detectors, and edge-aware filters that are
used in a wide range of image processing tasks. The most popular image quality
metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and
Str... | computer science |
30,690 | Automated image segmentation for detecting cell spreading for
metastasizing assessments of cancer development | cs.CV | The automated segmentation of cells in microscopic images is an open research
problem that has important implications for studies of the developmental and
cancer processes based on in vitro models. In this paper, we present the
approach for segmentation of the DIC images of cultured cells using G-neighbor
smoothing fol... | computer science |
30,691 | Script Identification in Natural Scene Image and Video Frame using
Attention based Convolutional-LSTM Network | cs.CV | Script identification plays a significant role in analysing documents and
videos. In this paper, we focus on the problem of script identification in
scene text images and video scripts. Because of low image quality, complex
background and similar layout of characters shared by some scripts like Greek,
Latin, etc., text... | computer science |
30,692 | Aggregated Channels Network for Real-Time Pedestrian Detection | cs.CV | Convolutional neural networks (CNNs) have demonstrated their superiority in
numerous computer vision tasks, yet their computational cost results
prohibitive for many real-time applications such as pedestrian detection which
is usually performed on low-consumption hardware. In order to alleviate this
drawback, most stra... | computer science |
30,693 | Depth-Adaptive Computational Policies for Efficient Visual Tracking | cs.CV | Current convolutional neural networks algorithms for video object tracking
spend the same amount of computation for each object and video frame. However,
it is harder to track an object in some frames than others, due to the varying
amount of clutter, scene complexity, amount of motion, and object's
distinctiveness aga... | computer science |
30,694 | Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs
for Contour Prediction | cs.CV | Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-s... | computer science |
30,695 | Unsupervised Object-Level Video Summarization with Online Motion
Auto-Encoder | cs.CV | Unsupervised video summarization plays an important role on digesting,
browsing, and searching the ever-growing videos everyday. Despite the great
progress achieved by prior works (e.g., the frame-level video summarization),
the underlying fine-grained semantic and motion information (i.e., objects of
interest and thei... | computer science |
30,696 | Threat of Adversarial Attacks on Deep Learning in Computer Vision: A
Survey | cs.CV | Deep learning is at the heart of the current rise of machine learning and
artificial intelligence. In the field of Computer Vision, it has become the
workhorse for applications ranging from self-driving cars to surveillance and
security. Whereas deep neural networks have demonstrated phenomenal success
(often beyond hu... | computer science |
30,697 | Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence:
Applications to Data Fusion in Imaging | cs.CV | The recently proposed plug-and-play (PnP) framework allows leveraging recent
developments in image denoising to tackle other, more involved, imaging inverse
problems. In a PnP method, a black-box denoiser is plugged into an iterative
algorithm, taking the place of a formal denoising step that corresponds to the
proximi... | computer science |
30,698 | Denoising Adversarial Autoencoders: Classifying Skin Lesions Using
Limited Labelled Training Data | cs.CV | We propose a novel deep learning model for classifying medical images in the
setting where there is a large amount of unlabelled medical data available, but
labelled data is in limited supply. We consider the specific case of
classifying skin lesions as either malignant or benign. In this setting, the
proposed approach... | computer science |
30,699 | Restricted Deformable Convolution based Road Scene Semantic Segmentation
Using Surround View Cameras | cs.CV | Understanding the surrounding environment of the vehicle is still one of the
challenges for autonomous driving. This paper addresses 360-degree road scene
semantic segmentation using surround view cameras, which are widely equipped in
existing production cars. First, in order to address large distortion problem
in the ... | computer science |
30,700 | A Novel Approach to Skew-Detection and Correction of English Alphabets
for OCR | cs.CV | Optical Character Recognition has been a challenging field in the advent of
digital computers. It is needed where information is to be readable both to
humans and machines. The process of OCR is composed of a set of pre and post
processing steps that decide the level of accuracy of recognition. This paper
deals with on... | computer science |
30,701 | Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation | cs.CV | This paper presents a new approach for integrating semantic information for
vision-based vehicle navigation. Although vision-based vehicle navigation
systems using pre-mapped visual landmarks are capable of achieving submeter
level accuracy in large-scale urban environment, a typical error source in this
type of system... | computer science |
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