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27,102 | Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for
Hand Pose Estimation | cs.CV | State-of-the-art methods for 3D hand pose estimation from depth images
require large amounts of annotated training data. We propose to model the
statistical relationships of 3D hand poses and corresponding depth images using
two deep generative models with a shared latent space. By design, our
architecture allows for l... | computer science |
27,103 | A Novel Weight-Shared Multi-Stage Network Architecture of CNNs for Scale
Invariance | cs.CV | Convolutional neural networks (CNNs) have demonstrated remarkable results in
image classification tasks for benchmark and practical uses. The CNNs with
deeper architectures have achieved higher performances recently thanks to their
robustness to parallel shift of objects in images aw well as their numerous
parameters a... | computer science |
27,104 | Sparse Representation based Multi-sensor Image Fusion: A Review | cs.CV | As a result of several successful applications in computer vision and image
processing, sparse representation (SR) has attracted significant attention in
multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs)
that presume the basis functions, SR learns an over-complete dictionary from a
set of t... | computer science |
27,105 | Underwater Optical Image Processing: A Comprehensive Review | cs.CV | Underwater cameras are widely used to observe the sea floor. They are usually
included in autonomous underwater vehicles, unmanned underwater vehicles, and
in situ ocean sensor networks. Despite being an important sensor for monitoring
underwater scenes, there exist many issues with recent underwater camera
sensors. Be... | computer science |
27,106 | Unsupervised temporal context learning using convolutional neural
networks for laparoscopic workflow analysis | cs.CV | Computer-assisted surgery (CAS) aims to provide the surgeon with the right
type of assistance at the right moment. Such assistance systems are especially
relevant in laparoscopic surgery, where CAS can alleviate some of the drawbacks
that surgeons incur. For many assistance functions, e.g. displaying the
location of a ... | computer science |
27,107 | An Efficient Decomposition Framework for Discriminative Segmentation
with Supermodular Losses | cs.CV | Several supermodular losses have been shown to improve the perceptual quality
of image segmentation in a discriminative framework such as a structured output
support vector machine (SVM). These loss functions do not necessarily have the
same structure as the one used by the segmentation inference algorithm, and in
gene... | computer science |
27,108 | Online People Tracking and Identification with RFID and Kinect | cs.CV | We introduce a novel, accurate and practical system for real-time people
tracking and identification. We used a Kinect V2 sensor for tracking that
generates a body skeleton for up to six people in the view. We perform
identification using both Kinect and passive RFID, by first measuring the
velocity vector of person's ... | computer science |
27,109 | Estimation of the volume of the left ventricle from MRI images using
deep neural networks | cs.CV | Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI)
images and calculating its volume are important for diagnosing cardiac
diseases. In 2016, Kaggle organized a competition to estimate the volume of LV
from MRI images. The dataset consisted of a large number of cases, but only
provided systole and ... | computer science |
27,110 | End-to-End Interpretation of the French Street Name Signs Dataset | cs.CV | We introduce the French Street Name Signs (FSNS) Dataset consisting of more
than a million images of street name signs cropped from Google Street View
images of France. Each image contains several views of the same street name
sign. Every image has normalized, title case folded ground-truth text as it
would appear on a... | computer science |
27,111 | Evolution-Preserving Dense Trajectory Descriptors | cs.CV | Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve
state-of-the-art human action recognition results on a number of datasets. This
paper improves their performance by applying rank pooling to each trajectory,
encoding the temporal evolution of deep learning features computed along the
trajectory... | computer science |
27,112 | A Graphical Social Topology Model for Multi-Object Tracking | cs.CV | Tracking multiple objects is a challenging task when objects move in groups
and occlude each other. Existing methods have investigated the problems of
group division and group energy-minimization; however, lacking overall
object-group topology modeling limits their ability in handling complex object
and group dynamics.... | computer science |
27,113 | SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with
Single Sample Per Person | cs.CV | Real-world face recognition using a single sample per person (SSPP) is a
challenging task. The problem is exacerbated if the conditions under which the
gallery image and the probe set are captured are completely different. To
address these issues from the perspective of domain adaptation, we introduce an
SSPP domain ad... | computer science |
27,114 | Efficient Algorithms for Moral Lineage Tracing | cs.CV | Lineage tracing, the joint segmentation and tracking of living cells as they
move and divide in a sequence of light microscopy images, is a challenging
task. Jug et al. have proposed a mathematical abstraction of this task, the
moral lineage tracing problem (MLTP), whose feasible solutions define both a
segmentation of... | computer science |
27,115 | Graph Based Over-Segmentation Methods for 3D Point Clouds | cs.CV | Over-segmentation, or super-pixel generation, is a common preliminary stage
for many computer vision applications. New acquisition technologies enable the
capturing of 3D point clouds that contain color and geometrical information.
This 3D information introduces a new conceptual change that can be utilized to
improve t... | computer science |
27,116 | One-Step Time-Dependent Future Video Frame Prediction with a
Convolutional Encoder-Decoder Neural Network | cs.CV | There is an inherent need for autonomous cars, drones, and other robots to
have a notion of how their environment behaves and to anticipate changes in the
near future. In this work, we focus on anticipating future appearance given the
current frame of a video. Existing work focuses on either predicting the future
appea... | computer science |
27,117 | FERA 2017 - Addressing Head Pose in the Third Facial Expression
Recognition and Analysis Challenge | cs.CV | The field of Automatic Facial Expression Analysis has grown rapidly in recent
years. However, despite progress in new approaches as well as benchmarking
efforts, most evaluations still focus on either posed expressions, near-frontal
recordings, or both. This makes it hard to tell how existing expression
recognition app... | computer science |
27,118 | Structured Deep Hashing with Convolutional Neural Networks for Fast
Person Re-identification | cs.CV | Given a pedestrian image as a query, the purpose of person re-identification
is to identify the correct match from a large collection of gallery images
depicting the same person captured by disjoint camera views. The critical
challenge is how to construct a robust yet discriminative feature
representation to capture th... | computer science |
27,119 | Integrating Three Mechanisms of Visual Attention for Active Visual
Search | cs.CV | Algorithms for robotic visual search can benefit from the use of visual
attention methods in order to reduce computational costs. Here, we describe how
three distinct mechanisms of visual attention can be integrated and
productively used to improve search performance. The first is viewpoint
selection as has been propos... | computer science |
27,120 | Enhanced Facial Recognition Framework based on Skin Tone and False Alarm
Rejection | cs.CV | Face detection is one of the challenging tasks in computer vision. Human face
detection plays an essential role in the first stage of face processing
applications such as face recognition, face tracking, image database
management, etc. In these applications, face objects often come from an
inconsequential part of image... | computer science |
27,121 | ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes | cs.CV | A key requirement for leveraging supervised deep learning methods is the
availability of large, labeled datasets. Unfortunately, in the context of RGB-D
scene understanding, very little data is available -- current datasets cover a
small range of scene views and have limited semantic annotations. To address
this issue,... | computer science |
27,122 | Learning from Ambiguously Labeled Face Images | cs.CV | Learning a classifier from ambiguously labeled face images is challenging
since training images are not always explicitly-labeled. For instance, face
images of two persons in a news photo are not explicitly labeled by their names
in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar)
method for ... | computer science |
27,123 | Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm
Minimization based on Group Sparse Representation | cs.CV | Nuclear norm minimization (NNM) tends to over-shrink the rank components and
treats the different rank components equally, thus limits its capability and
flexibility. Recent studies have shown that the weighted nuclear norm
minimization (WNNM) is expected to be more accurate than NNM. However, it still
lacks a plausibl... | computer science |
27,124 | Deep Heterogeneous Feature Fusion for Template-Based Face Recognition | cs.CV | Although deep learning has yielded impressive performance for face
recognition, many studies have shown that different networks learn different
feature maps: while some networks are more receptive to pose and illumination
others appear to capture more local information. Thus, in this work, we propose
a deep heterogeneo... | computer science |
27,125 | Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames
and Key Segments | cs.CV | Recognizing dynamic scenes is one of the fundamental problems in scene
understanding, which categorizes moving scenes such as a forest fire,
landslide, or avalanche. While existing methods focus on reliable capturing of
static and dynamic information, few works have explored frame selection from a
dynamic scene sequenc... | computer science |
27,126 | Application of Multi-channel 3D-cube Successive Convolution Network for
Convective Storm Nowcasting | cs.CV | Convective storm nowcasting has attracted substantial attention in various
fields. Existing methods under a deep learning framework rely primarily on
radar data. Although they perform nowcast storm advection well, it is still
challenging to nowcast storm initiation and growth, due to the limitations of
the radar observ... | computer science |
27,127 | A deep learning model integrating FCNNs and CRFs for brain tumor
segmentation | cs.CV | Accurate and reliable brain tumor segmentation is a critical component in
cancer diagnosis, treatment planning, and treatment outcome evaluation. Build
upon successful deep learning techniques, a novel brain tumor segmentation
method is developed by integrating fully convolutional neural networks (FCNNs)
and Conditiona... | computer science |
27,128 | Normalized Total Gradient: A New Measure for Multispectral Image
Registration | cs.CV | Image registration is a fundamental issue in multispectral image processing.
In filter wheel based multispectral imaging systems, the non-coplanar placement
of the filters always causes the misalignment of multiple channel images. The
selective characteristic of spectral response in multispectral imaging raises
two cha... | computer science |
27,129 | Deep Multi-camera People Detection | cs.CV | This paper addresses the problem of multi-view people occupancy map
estimation. Existing solutions for this problem either operate per-view, or
rely on a background subtraction pre-processing. Both approaches lessen the
detection performance as scenes become more crowded. The former does not
exploit joint information, ... | computer science |
27,130 | Computational Model for Predicting Visual Fixations from Childhood to
Adulthood | cs.CV | How people look at visual information reveals fundamental information about
themselves, their interests and their state of mind. While previous visual
attention models output static 2-dimensional saliency maps, saccadic models aim
to predict not only where observers look at but also how they move their eyes
to explore ... | computer science |
27,131 | Handwritten Arabic Numeral Recognition using Deep Learning Neural
Networks | cs.CV | Handwritten character recognition is an active area of research with
applications in numerous fields. Past and recent works in this field have
concentrated on various languages. Arabic is one language where the scope of
research is still widespread, with it being one of the most popular languages
in the world and being... | computer science |
27,132 | Visual Discovery at Pinterest | cs.CV | Over the past three years Pinterest has experimented with several visual
search and recommendation services, including Related Pins (2014), Similar
Looks (2015), Flashlight (2016) and Lens (2017). This paper presents an
overview of our visual discovery engine powering these services, and shares the
rationales behind ou... | computer science |
27,133 | Multi-Task Convolutional Neural Network for Pose-Invariant Face
Recognition | cs.CV | This paper explores multi-task learning (MTL) for face recognition. We answer
the questions of how and why MTL can improve the face recognition performance.
First, we propose a multi-task Convolutional Neural Network (CNN) for face
recognition where identity classification is the main task and pose,
illumination, and e... | computer science |
27,134 | Chord Angle Deviation using Tangent (CADT), an Efficient and Robust
Contour-based Corner Detector | cs.CV | Detection of corner is the most essential process in a large number of
computer vision and image processing applications. We have mentioned a number
of popular contour-based corner detectors in our paper. Among all these
detectors chord to triangular arm angle (CTAA) has been demonstrated as the
most dominant corner de... | computer science |
27,135 | Deep Hybrid Similarity Learning for Person Re-identification | cs.CV | Person Re-IDentification (Re-ID) aims to match person images captured from
two non-overlapping cameras. In this paper, a deep hybrid similarity learning
(DHSL) method for person Re-ID based on a convolution neural network (CNN) is
proposed. In our approach, a CNN learning feature pair for the input image pair
is simult... | computer science |
27,136 | Improving automated multiple sclerosis lesion segmentation with a
cascaded 3D convolutional neural network approach | cs.CV | In this paper, we present a novel automated method for White Matter (WM)
lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is
based on a cascade of two 3D patch-wise convolutional neural networks (CNN).
The first network is trained to be more sensitive revealing possible candidate
lesion voxel... | computer science |
27,137 | KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning
Efficient H-CNN Regressors | cs.CV | Keypoint detection is one of the most important pre-processing steps in tasks
such as face modeling, recognition and verification. In this paper, we present
an iterative method for Keypoint Estimation and Pose prediction of
unconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) for
addressing the face alig... | computer science |
27,138 | Improving Text Proposals for Scene Images with Fully Convolutional
Networks | cs.CV | Text Proposals have emerged as a class-dependent version of object proposals
- efficient approaches to reduce the search space of possible text object
locations in an image. Combined with strong word classifiers, text proposals
currently yield top state of the art results in end-to-end scene text
recognition. In this p... | computer science |
27,139 | Automatic Handgun Detection Alarm in Videos Using Deep Learning | cs.CV | Current surveillance and control systems still require human supervision and
intervention. This work presents a novel automatic handgun detection system in
videos appropriate for both, surveillance and control purposes. We reformulate
this detection problem into the problem of minimizing false positives and solve
it by... | computer science |
27,140 | Learning Normalized Inputs for Iterative Estimation in Medical Image
Segmentation | cs.CV | In this paper, we introduce a simple, yet powerful pipeline for medical image
segmentation that combines Fully Convolutional Networks (FCNs) with Fully
Convolutional Residual Networks (FC-ResNets). We propose and examine a design
that takes particular advantage of recent advances in the understanding of both
Convolutio... | computer science |
27,141 | EMNIST: an extension of MNIST to handwritten letters | cs.CV | The MNIST dataset has become a standard benchmark for learning,
classification and computer vision systems. Contributing to its widespread
adoption are the understandable and intuitive nature of the task, its
relatively small size and storage requirements and the accessibility and
ease-of-use of the database itself. Th... | computer science |
27,142 | Domain Adaptation for Visual Applications: A Comprehensive Survey | cs.CV | The aim of this paper is to give an overview of domain adaptation and
transfer learning with a specific view on visual applications. After a general
motivation, we first position domain adaptation in the larger transfer learning
problem. Second, we try to address and analyze briefly the state-of-the-art
methods for dif... | computer science |
27,143 | Vehicle Speed Detecting App | cs.CV | The report presents the measurement of vehicular speed using a smartphone
camera. The speed measurement is accomplished by detecting the position of the
vehicle on a camera frame using the LBP cascade classifier of OpenCV API, the
displacement of the detected vehicle with time is used to compute the speed.
Conversion c... | computer science |
27,144 | The Effect of Color Space Selection on Detectability and
Discriminability of Colored Objects | cs.CV | In this paper, we investigate the effect of color space selection on
detectability and discriminability of colored objects under various conditions.
20 color spaces from the literature are evaluated on a large dataset of
simulated and real images. We measure the suitability of color spaces from two
different perspectiv... | computer science |
27,145 | Learning to Detect Human-Object Interactions | cs.CV | We study the problem of detecting human-object interactions (HOI) in static
images, defined as predicting a human and an object bounding box with an
interaction class label that connects them. HOI detection is a fundamental
problem in computer vision as it provides semantic information about the
interactions among the ... | computer science |
27,146 | Adversarial Discriminative Domain Adaptation | cs.CV | Adversarial learning methods are a promising approach to training robust deep
networks, and can generate complex samples across diverse domains. They also
can improve recognition despite the presence of domain shift or dataset bias:
several adversarial approaches to unsupervised domain adaptation have recently
been int... | computer science |
27,147 | An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm
Segmentation | cs.CV | The poor contrast and the overlapping of cervical cell cytoplasm are the
major issues in the accurate segmentation of cervical cell cytoplasm. This
paper presents an automated unsupervised cytoplasm segmentation approach which
can effectively find the cytoplasm boundaries in overlapping cells. The
proposed approach fir... | computer science |
27,148 | Defect detection for patterned fabric images based on GHOG and low-rank
decomposition | cs.CV | In order to accurately detect defects in patterned fabric images, a novel
detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is
proposed in this paper. Defect-free pattern fabric images have the specified
direction, while defects damage their regularity of direction. Therefore, a
direction-aware d... | computer science |
27,149 | The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a
Marine Aquaculture Environment | cs.CV | An original dataset for semantic segmentation, Ciona17, is introduced, which
to the best of the authors' knowledge, is the first dataset of its kind with
pixel-level annotations pertaining to invasive species in a marine environment.
Diverse outdoor illumination, a range of object shapes, colour, and severe
occlusion p... | computer science |
27,150 | Collaborative Deep Reinforcement Learning for Joint Object Search | cs.CV | We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present... | computer science |
27,151 | 3D Face Reconstruction with Geometry Details from a Single Image | cs.CV | 3D face reconstruction from a single image is a classical and challenging
problem, with wide applications in many areas. Inspired by recent works in face
animation from RGB-D or monocular video inputs, we develop a novel method for
reconstructing 3D faces from unconstrained 2D images, using a coarse-to-fine
optimizatio... | computer science |
27,152 | Revisiting Graph Construction for Fast Image Segmentation | cs.CV | In this paper, we propose a simple but effective method for fast image
segmentation. We re-examine the locality-preserving character of spectral
clustering by constructing a graph over image regions with both global and
local connections. Our novel approach to build graph connections relies on two
key observations: 1) ... | computer science |
27,153 | MAT: A Multimodal Attentive Translator for Image Captioning | cs.CV | In this work we formulate the problem of image captioning as a multimodal
translation task. Analogous to machine translation, we present a
sequence-to-sequence recurrent neural networks (RNN) model for image caption
generation. Different from most existing work where the whole image is
represented by convolutional neur... | computer science |
27,154 | The Game Imitation: Deep Supervised Convolutional Networks for Quick
Video Game AI | cs.CV | We present a vision-only model for gaming AI which uses a late integration
deep convolutional network architecture trained in a purely supervised
imitation learning context. Although state-of-the-art deep learning models for
video game tasks generally rely on more complex methods such as deep-Q
learning, we show that a... | computer science |
27,155 | Robust Shape Registration using Fuzzy Correspondences | cs.CV | Shape registration is the process of aligning one 3D model to another. Most
previous methods to align shapes with no known correspondences attempt to solve
for both the transformation and correspondences iteratively. We present a shape
registration approach that solves for the transformation using fuzzy
correspondences... | computer science |
27,156 | CityPersons: A Diverse Dataset for Pedestrian Detection | cs.CV | Convnets have enabled significant progress in pedestrian detection recently,
but there are still open questions regarding suitable architectures and
training data. We revisit CNN design and point out key adaptations, enabling
plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset.
To achieve furth... | computer science |
27,157 | Zoom Out-and-In Network with Recursive Training for Object Proposal | cs.CV | In this paper, we propose a zoom-out-and-in network for generating object
proposals. We utilize different resolutions of feature maps in the network to
detect object instances of various sizes. Specifically, we divide the anchor
candidates into three clusters based on the scale size and place them on
feature maps of di... | computer science |
27,158 | Person Search with Natural Language Description | cs.CV | Searching persons in large-scale image databases with the query of natural
language description has important applications in video surveillance. Existing
methods mainly focused on searching persons with image-based or attribute-based
queries, which have major limitations for a practical usage. In this paper, we
study ... | computer science |
27,159 | DR2-Net: Deep Residual Reconstruction Network for Image Compressive
Sensing | cs.CV | Most traditional algorithms for compressive sensing image reconstruction
suffer from the intensive computation. Recently, deep learning-based
reconstruction algorithms have been reported, which dramatically reduce the
time complexity than iterative reconstruction algorithms. In this paper, we
propose a novel \textbf{D}... | computer science |
27,160 | A Survey on Deep Learning in Medical Image Analysis | cs.CV | Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. W... | computer science |
27,161 | Deep learning-based assessment of tumor-associated stroma for diagnosing
breast cancer in histopathology images | cs.CV | Diagnosis of breast carcinomas has so far been limited to the morphological
interpretation of epithelial cells and the assessment of epithelial tissue
architecture. Consequently, most of the automated systems have focused on
characterizing the epithelial regions of the breast to detect cancer. In this
paper, we propose... | computer science |
27,162 | Progressively Diffused Networks for Semantic Image Segmentation | cs.CV | This paper introduces Progressively Diffused Networks (PDNs) for unifying
multi-scale context modeling with deep feature learning, by taking semantic
image segmentation as an exemplar application. Prior neural networks, such as
ResNet, tend to enhance representational power by increasing the depth of
architectures and ... | computer science |
27,163 | Learning Spatial Regularization with Image-level Supervisions for
Multi-label Image Classification | cs.CV | Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because... | computer science |
27,164 | Efficient Large-scale Approximate Nearest Neighbor Search on the GPU | cs.CV | We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a... | computer science |
27,165 | SurvivalNet: Predicting patient survival from diffusion weighted
magnetic resonance images using cascaded fully convolutional and 3D
convolutional neural networks | cs.CV | Automatic non-invasive assessment of hepatocellular carcinoma (HCC)
malignancy has the potential to substantially enhance tumor treatment
strategies for HCC patients. In this work we present a novel framework to
automatically characterize the malignancy of HCC lesions from DWI images. We
predict HCC malignancy in two s... | computer science |
27,166 | Reflection Separation Using Guided Annotation | cs.CV | Photographs taken through a glass surface often contain an approximately
linear superposition of reflected and transmitted layers. Decomposing an image
into these layers is generally an ill-posed task and the use of an additional
image prior and user provided cues is presently necessary in order to obtain
good results.... | computer science |
27,167 | Synthesis versus analysis in patch-based image priors | cs.CV | In global models/priors (for example, using wavelet frames), there is a well
known analysis vs synthesis dichotomy in the way signal/image priors are
formulated. In patch-based image models/priors, this dichotomy is also present
in the choice of how each patch is modeled. This paper shows that there is
another analysis... | computer science |
27,168 | Derivative Based Focal Plane Array Nonuniformity Correction | cs.CV | This paper presents a fast and robust method for fixed pattern noise
nonuniformity correction of infrared focal plane arrays. The proposed method
requires neither shutter nor elaborate calibrations and therefore enables a
real time correction with no interruptions. Based on derivative estimation of
the fixed pattern no... | computer science |
27,169 | The Power of Sparsity in Convolutional Neural Networks | cs.CV | Deep convolutional networks are well-known for their high computational and
memory demands. Given limited resources, how does one design a network that
balances its size, training time, and prediction accuracy? A surprisingly
effective approach to trade accuracy for size and speed is to simply reduce the
number of chan... | computer science |
27,170 | Weighted Motion Averaging for the Registration of Multi-View Range Scans | cs.CV | Multi-view registration is a fundamental but challenging problem in 3D
reconstruction and robot vision. Although the original motion averaging
algorithm has been introduced as an effective means to solve the multi-view
registration problem, it does not consider the reliability and accuracy of each
relative motion. Acco... | computer science |
27,171 | Visual Tracking by Reinforced Decision Making | cs.CV | One of the major challenges of model-free visual tracking problem has been
the difficulty originating from the unpredictable and drastic changes in the
appearance of objects we target to track. Existing methods tackle this problem
by updating the appearance model on-line in order to adapt to the changes in
the appearan... | computer science |
27,172 | Learning Compact Appearance Representation for Video-based Person
Re-Identification | cs.CV | This paper presents a novel approach for video-based person re-identification
using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we
intend to extract a compact yet discriminative appearance representation from
several frames rather than the whole sequence. Specifically, given a video, the
repres... | computer science |
27,173 | Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation | cs.CV | The empirical fact that classifiers, trained on given data collections,
perform poorly when tested on data acquired in different settings is
theoretically explained in domain adaptation through a shift among
distributions of the source and target domains. Alleviating the domain shift
problem, especially in the challeng... | computer science |
27,174 | Object Detection in Videos with Tubelet Proposal Networks | cs.CV | Object detection in videos has drawn increasing attention recently with the
introduction of the large-scale ImageNet VID dataset. Different from object
detection in static images, temporal information in videos is vital for object
detection. To fully utilize temporal information, state-of-the-art methods are
based on s... | computer science |
27,175 | Mimicking Ensemble Learning with Deep Branched Networks | cs.CV | This paper proposes a branched residual network for image classification. It
is known that high-level features of deep neural network are more
representative than lower-level features. By sharing the low-level features,
the network can allocate more memory to high-level features. The upper layers
of our proposed networ... | computer science |
27,176 | Fast Resampling of 3D Point Clouds via Graphs | cs.CV | To reduce cost in storing, processing and visualizing a large-scale point
cloud, we consider a randomized resampling strategy to select a representative
subset of points while preserving application-dependent features. The proposed
strategy is based on graphs, which can represent underlying surfaces and lend
themselves... | computer science |
27,177 | BrnoCompSpeed: Review of Traffic Camera Calibration and Comprehensive
Dataset for Monocular Speed Measurement | cs.CV | In this paper, we focus on traffic camera calibration and visual speed
measurement from a single monocular camera, which is an important task of
visual traffic surveillance. Existing methods addressing this problem are hard
to compare due to lack of a common dataset with reliable ground truth.
Therefore, it is not clea... | computer science |
27,178 | Traffic Surveillance Camera Calibration by 3D Model Bounding Box
Alignment for Accurate Vehicle Speed Measurement | cs.CV | In this paper, we focus on fully automatic traffic surveillance camera
calibration, which we use for speed measurement of passing vehicles. We improve
over a recent state-of-the-art camera calibration method for traffic
surveillance based on two detected vanishing points. More importantly, we
propose a novel automatic ... | computer science |
27,179 | Crowd Sourcing Image Segmentation with iaSTAPLE | cs.CV | We propose a novel label fusion technique as well as a crowdsourcing protocol
to efficiently obtain accurate epithelial cell segmentations from non-expert
crowd workers. Our label fusion technique simultaneously estimates the true
segmentation, the performance levels of individual crowd workers, and an image
segmentati... | computer science |
27,180 | VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization | cs.CV | Machine learning techniques, namely convolutional neural networks (CNN) and
regression forests, have recently shown great promise in performing 6-DoF
localization of monocular images. However, in most cases image-sequences,
rather only single images, are readily available. To this extent, none of the
proposed learning-... | computer science |
27,181 | Using Deep Learning and Google Street View to Estimate the Demographic
Makeup of the US | cs.CV | The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag betwee... | computer science |
27,182 | 3D Reconstruction of Temples in the Special Region of Yogyakarta By
Using Close-Range Photogrammetry | cs.CV | Object reconstruction is one of the main problems in cultural heritage
preservation. This problem is due to lack of data in documentation. Thus in
this research we presented a method of 3D reconstruction using close-range
photogrammetry. We collected 1319 photos from five temples in Yogyakarta. Using
A-KAZE algorithm, ... | computer science |
27,183 | MomentsNet: a simple learning-free method for binary image recognition | cs.CV | In this paper, we propose a new simple and learning-free deep learning
network named MomentsNet, whose convolution layer, nonlinear processing layer
and pooling layer are constructed by Moments kernels, binary hashing and
block-wise histogram, respectively. Twelve typical moments (including
geometrical moment, Zernike ... | computer science |
27,184 | Boosted Multiple Kernel Learning for First-Person Activity Recognition | cs.CV | Activity recognition from first-person (ego-centric) videos has recently
gained attention due to the increasing ubiquity of the wearable cameras. There
has been a surge of efforts adapting existing feature descriptors and designing
new descriptors for the first-person videos. An effective activity recognition
system re... | computer science |
27,185 | Synthesising Dynamic Textures using Convolutional Neural Networks | cs.CV | Here we present a parametric model for dynamic textures. The model is based
on spatiotemporal summary statistics computed from the feature representations
of a Convolutional Neural Network (CNN) trained on object recognition. We
demonstrate how the model can be used to synthesise new samples of dynamic
textures and to ... | computer science |
27,186 | CT Image Denoising with Perceptive Deep Neural Networks | cs.CV | Increasing use of CT in modern medical practice has raised concerns over
associated radiation dose. Reduction of radiation dose associated with CT can
increase noise and artifacts, which can adversely affect diagnostic confidence.
Denoising of low-dose CT images on the other hand can help improve diagnostic
confidence,... | computer science |
27,187 | Convolutional Neural Network Committees for Melanoma Classification with
Classical And Expert Knowledge Based Image Transforms Data Augmentation | cs.CV | Skin cancer is a major public health problem, as is the most common type of
cancer and represents more than half of cancer diagnoses worldwide. Early
detection influences the outcome of the disease and motivates our work. We
investigate the composition of CNN committees and data augmentation for the the
ISBI 2017 Melan... | computer science |
27,188 | Learning Chained Deep Features and Classifiers for Cascade in Object
Detection | cs.CV | Cascade is a widely used approach that rejects obvious negative samples at
early stages for learning better classifier and faster inference. This paper
presents chained cascade network (CC-Net). In this CC-Net, the cascaded
classifier at a stage is aided by the classification scores in previous stages.
Feature chaining... | computer science |
27,189 | Robust and fully automated segmentation of mandible from CT scans | cs.CV | Mandible bone segmentation from computed tomography (CT) scans is challenging
due to mandible's structural irregularities, complex shape patterns, and lack
of contrast in joints. Furthermore, connections of teeth to mandible and
mandible to remaining parts of the skull make it extremely difficult to
identify mandible b... | computer science |
27,190 | Analyzing Learned Convnet Features with Dirichlet Process Gaussian
Mixture Models | cs.CV | Convolutional Neural Networks (Convnets) have achieved good results in a
range of computer vision tasks the recent years. Though given a lot of
attention, visualizing the learned representations to interpret Convnets, still
remains a challenging task. The high dimensionality of internal representations
and the high abs... | computer science |
27,191 | ViP-CNN: Visual Phrase Guided Convolutional Neural Network | cs.CV | As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. pers... | computer science |
27,192 | k-Means Clustering and Ensemble of Regressions: An Algorithm for the
ISIC 2017 Skin Lesion Segmentation Challenge | cs.CV | This abstract briefly describes a segmentation algorithm developed for the
ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of
the competition is to perform a segmentation (in the form of a binary mask
image) of skin lesions in dermoscopic images as close as possible to a
segmentation performe... | computer science |
27,193 | Improving high-pass fusion method using wavelets | cs.CV | In an appropriate image fusion method, spatial information of the
panchromatic image is injected into the multispectral images such that the
spectral information is not distorted. The high-pass modulation method is a
successful method in image fusion. However, the main drawback of this method is
that this technique use... | computer science |
27,194 | Feasibility of Principal Component Analysis in hand gesture recognition
system | cs.CV | Nowadays actions are increasingly being handled in electronic ways, instead
of physical interaction. From earlier times biometrics is used in the
authentication of a person. It recognizes a person by using a human trait
associated with it like eyes (by calculating the distance between the eyes) and
using hand gestures,... | computer science |
27,195 | Toward Streaming Synapse Detection with Compositional ConvNets | cs.CV | Connectomics is an emerging field in neuroscience that aims to reconstruct
the 3-dimensional morphology of neurons from electron microscopy (EM) images.
Recent studies have successfully demonstrated the use of convolutional neural
networks (ConvNets) for segmenting cell membranes to individuate neurons.
However, there ... | computer science |
27,196 | Multi-Context Attention for Human Pose Estimation | cs.CV | In this paper, we propose to incorporate convolutional neural networks with a
multi-context attention mechanism into an end-to-end framework for human pose
estimation. We adopt stacked hourglass networks to generate attention maps from
features at multiple resolutions with various semantics. The Conditional Random
Fiel... | computer science |
27,197 | Viewpoint Adaptation for Rigid Object Detection | cs.CV | An object detector performs suboptimally when applied to image data taken
from a viewpoint different from the one with which it was trained. In this
paper, we present a viewpoint adaptation algorithm that allows a trained
single-view object detector to be adapted to a new, distinct viewpoint. We
first illustrate how a ... | computer science |
27,198 | Learning Non-local Image Diffusion for Image Denoising | cs.CV | Image diffusion plays a fundamental role for the task of image denoising.
Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a
simple but very effective framework for image denoising. However, as the TNRD
model is a local model, the diffusion behavior of which is purely controlled by
informat... | computer science |
27,199 | Speckle Reduction with Trained Nonlinear Diffusion Filtering | cs.CV | Speckle reduction is a prerequisite for many image processing tasks in
synthetic aperture radar (SAR) images, as well as all coherent images. In
recent years, predominant state-of-the-art approaches for despeckling are
usually based on nonlocal methods which mainly concentrate on achieving utmost
image restoration qual... | computer science |
27,200 | Deep representation learning for human motion prediction and
classification | cs.CV | Generative models of 3D human motion are often restricted to a small number
of activities and can therefore not generalize well to novel movements or
applications. In this work we propose a deep learning framework for human
motion capture data that learns a generic representation from a large corpus of
motion capture d... | computer science |
27,201 | Toward high-performance online HCCR: a CNN approach with DropDistortion,
path signature and spatial stochastic max-pooling | cs.CV | This paper presents an investigation of several techniques that increase the
accuracy of online handwritten Chinese character recognition (HCCR). We propose
a new training strategy named DropDistortion to train a deep convolutional
neural network (DCNN) with distorted samples. DropDistortion gradually lowers
the degree... | computer science |
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