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27,502 | Exploiting Color Name Space for Salient Object Detection | cs.CV | In this paper, we will investigate the contribution of color names for
salient object detection. Each input image is first converted to the color name
space, which is consisted of 11 probabilistic channels. By exploring the
topological structure relationship between the figure and the ground, we obtain
a saliency map t... | computer science |
27,503 | A Visual Measure of Changes to Weighted Self-Organizing Map Patterns | cs.CV | Estimating output changes by input changes is the main task in causal
analysis. In previous work, input and output Self-Organizing Maps (SOMs) were
associated for causal analysis of multivariate and nonlinear data. Based on the
association, a weight distribution of the output conditional on a given input
was obtained o... | computer science |
27,504 | MIHash: Online Hashing with Mutual Information | cs.CV | Learning-based hashing methods are widely used for nearest neighbor
retrieval, and recently, online hashing methods have demonstrated good
performance-complexity trade-offs by learning hash functions from streaming
data. In this paper, we first address a key challenge for online hashing: the
binary codes for indexed da... | computer science |
27,505 | Mastering Sketching: Adversarial Augmentation for Structured Prediction | cs.CV | We present an integral framework for training sketch simplification networks
that convert challenging rough sketches into clean line drawings. Our approach
augments a simplification network with a discriminator network, training both
networks jointly so that the discriminator network discerns whether a line
drawing is ... | computer science |
27,506 | LIDAR-based Driving Path Generation Using Fully Convolutional Neural
Networks | cs.CV | In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world dr... | computer science |
27,507 | Trespassing the Boundaries: Labeling Temporal Bounds for Object
Interactions in Egocentric Video | cs.CV | Manual annotations of temporal bounds for object interactions (i.e. start and
end times) are typical training input to recognition, localization and
detection algorithms. For three publicly available egocentric datasets, we
uncover inconsistencies in ground truth temporal bounds within and across
annotators and dataset... | computer science |
27,508 | Efficient Processing of Deep Neural Networks: A Tutorial and Survey | cs.CV | Deep neural networks (DNNs) are currently widely used for many artificial
intelligence (AI) applications including computer vision, speech recognition,
and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it
comes at the cost of high computational complexity. Accordingly, techniques
that enable ... | computer science |
27,509 | Active Convolution: Learning the Shape of Convolution for Image
Classification | cs.CV | In recent years, deep learning has achieved great success in many computer
vision applications. Convolutional neural networks (CNNs) have lately emerged
as a major approach to image classification. Most research on CNNs thus far has
focused on developing architectures such as the Inception and residual
networks. The co... | computer science |
27,510 | Multi-Path Region-Based Convolutional Neural Network for Accurate
Detection of Unconstrained "Hard Faces" | cs.CV | Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose... | computer science |
27,511 | Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local
Priors for Single-Frame Small Target Detection | cs.CV | Many state-of-the-art methods have been proposed for infrared small target
detection. They work well on the images with homogeneous backgrounds and
high-contrast targets. However, when facing highly heterogeneous backgrounds,
they would not perform very well, mainly due to: 1) the existence of strong
edges and other in... | computer science |
27,512 | Introduction To The Monogenic Signal | cs.CV | The monogenic signal is an image analysis methodology that was introduced by
Felsberg and Sommer in 2001 and has been employed for a variety of purposes in
image processing and computer vision research. In particular, it has been found
to be useful in the analysis of ultrasound imagery in several research
scenarios mos... | computer science |
27,513 | Deep Poincare Map For Robust Medical Image Segmentation | cs.CV | Precise segmentation is a prerequisite for an accurate quantification of the
imaged objects. It is a very challenging task in many medical imaging
applications due to relatively poor image quality and data scarcity. In this
work, we present an innovative segmentation paradigm, named Deep Poincare Map
(DPM), by coupling... | computer science |
27,514 | StyleBank: An Explicit Representation for Neural Image Style Transfer | cs.CV | We propose StyleBank, which is composed of multiple convolution filter banks
and each filter bank explicitly represents one style, for neural image style
transfer. To transfer an image to a specific style, the corresponding filter
bank is operated on top of the intermediate feature embedding produced by a
single auto-e... | computer science |
27,515 | Coherent Online Video Style Transfer | cs.CV | Training a feed-forward network for fast neural style transfer of images is
proven to be successful. However, the naive extension to process video frame by
frame is prone to producing flickering results. We propose the first end-to-end
network for online video style transfer, which generates temporally coherent
stylize... | computer science |
27,516 | Discriminative Transfer Learning for General Image Restoration | cs.CV | Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem co... | computer science |
27,517 | Femoral ROIs and Entropy for Texture-based Detection of Osteoarthritis
from High-Resolution Knee Radiographs | cs.CV | The relationship between knee osteoarthritis progression and changes in
tibial bone structure has long been recognized and various texture descriptors
have been proposed to detect early osteoarthritis (OA) from radiographs. This
work aims to investigate (1) femoral textures as an OA indicator and (2) the
potential of e... | computer science |
27,518 | Graph Regularized Tensor Sparse Coding for Image Representation | cs.CV | Sparse coding (SC) is an unsupervised learning scheme that has received an
increasing amount of interests in recent years. However, conventional SC
vectorizes the input images, which destructs the intrinsic spatial structures
of the images. In this paper, we propose a novel graph regularized tensor
sparse coding (GTSC)... | computer science |
27,519 | Robust Guided Image Filtering | cs.CV | The process of using one image to guide the filtering process of another one
is called Guided Image Filtering (GIF). The main challenge of GIF is the
structure inconsistency between the guidance image and the target image.
Besides, noise in the target image is also a challenging issue especially when
it is heavy. In th... | computer science |
27,520 | Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to
Specific Appearance for Crowd Counting | cs.CV | This paper proposes a crowd counting method. Crowd counting is difficult
because of large appearance changes of a target which caused by density and
scale changes. Conventional crowd counting methods generally utilize one
predictor (e,g., regression and multi-class classifier). However, such only one
predictor can not ... | computer science |
27,521 | Evaluation of Classifiers for Image Segmentation: Applications for
Eucalypt Forest Inventory | cs.CV | The task of counting eucalyptus trees from aerial images collected by
unmanned aerial vehicles (UAVs) has been frequently explored by techniques of
estimation of the basal area, i.e, by determining the expected number of trees
based on sampling techniques. An alternative is the use of machine learning to
identify patte... | computer science |
27,522 | Octree Generating Networks: Efficient Convolutional Architectures for
High-resolution 3D Outputs | cs.CV | We present a deep convolutional decoder architecture that can generate
volumetric 3D outputs in a compute- and memory-efficient manner by using an
octree representation. The network learns to predict both the structure of the
octree, and the occupancy values of individual cells. This makes it a
particularly valuable te... | computer science |
27,523 | Robust Depth-based Person Re-identification | cs.CV | Person re-identification (re-id) aims to match people across non-overlapping
camera views. So far the RGB-based appearance is widely used in most existing
works. However, when people appeared in extreme illumination or changed
clothes, the RGB appearance-based re-id methods tended to fail. To overcome
this problem, we ... | computer science |
27,524 | L2-constrained Softmax Loss for Discriminative Face Verification | cs.CV | In recent years, the performance of face verification systems has
significantly improved using deep convolutional neural networks (DCNNs). A
typical pipeline for face verification includes training a deep network for
subject classification with softmax loss, using the penultimate layer output as
the feature descriptor,... | computer science |
27,525 | Objects as context for detecting their semantic parts | cs.CV | We present a semantic part detection approach that effectively leverages
object information.We use the object appearance and its class as indicators of
what parts to expect. We also model the expected relative location of parts
inside the objects based on their appearance. We achieve this with a new
network module, cal... | computer science |
27,526 | Lucid Data Dreaming for Multiple Object Tracking | cs.CV | Convolutional networks reach top quality in pixel-level object tracking but
require a large amount of training data (1k~10k) to deliver such results. We
propose a new training strategy which achieves state-of-the-art results across
three evaluation datasets while using 20x~100x less annotated data than
competing method... | computer science |
27,527 | Learning and Refining of Privileged Information-based RNNs for Action
Recognition from Depth Sequences | cs.CV | Existing RNN-based approaches for action recognition from depth sequences
require either skeleton joints or hand-crafted depth features as inputs. An
end-to-end manner, mapping from raw depth maps to action classes, is
non-trivial to design due to the fact that: 1) single channel map lacks texture
thus weakens the disc... | computer science |
27,528 | Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination | cs.CV | Sparse coding (SC) is an automatic feature extraction and selection technique
that is widely used in unsupervised learning. However, conventional SC
vectorizes the input images, which breaks apart the local proximity of pixels
and destructs the elementary object structures of images. In this paper, we
propose a novel t... | computer science |
27,529 | Semi and Weakly Supervised Semantic Segmentation Using Generative
Adversarial Network | cs.CV | Semantic segmentation has been a long standing challenging task in computer
vision. It aims at assigning a label to each image pixel and needs significant
number of pixellevel annotated data, which is often unavailable. To address
this lack, in this paper, we leverage, on one hand, massive amount of available
unlabeled... | computer science |
27,530 | An Epipolar Line from a Single Pixel | cs.CV | Computing the epipolar geometry from feature points between cameras with very
different viewpoints is often error prone, as an object's appearance can vary
greatly between images. For such cases, it has been shown that using motion
extracted from video can achieve much better results than using a static image.
This pap... | computer science |
27,531 | Coordinating Filters for Faster Deep Neural Networks | cs.CV | Very large-scale Deep Neural Networks (DNNs) have achieved remarkable
successes in a large variety of computer vision tasks. However, the high
computation intensity of DNNs makes it challenging to deploy these models on
resource-limited systems. Some studies used low-rank approaches that
approximate the filters by low-... | computer science |
27,532 | Deep 6-DOF Tracking | cs.CV | We present a temporal 6-DOF tracking method which leverages deep learning to
achieve state-of-the-art performance on challenging datasets of real world
capture. Our method is both more accurate and more robust to occlusions than
the existing best performing approaches while maintaining real-time
performance. To assess ... | computer science |
27,533 | INTEL-TUT Dataset for Camera Invariant Color Constancy Research | cs.CV | In this paper, we provide a novel dataset designed for camera invariant color
constancy research. Camera invariance corresponds to the robustness of an
algorithm's performance when run on images of the same scene taken by different
cameras. Accordingly, images in the database correspond to several lab and
field scenes ... | computer science |
27,534 | A Holistic Approach for Optimizing DSP Block Utilization of a CNN
implementation on FPGA | cs.CV | Deep Neural Networks are becoming the de-facto standard models for image
understanding, and more generally for computer vision tasks. As they involve
highly parallelizable computations, CNN are well suited to current fine grain
programmable logic devices. Thus, multiple CNN accelerators have been
successfully implement... | computer science |
27,535 | Towards Automatic Learning of Procedures from Web Instructional Videos | cs.CV | The potential for agents, whether embodied or software, to learn by observing
other agents performing procedures involving objects and actions is rich.
Current research on automatic procedure learning heavily relies on action
labels or video subtitles, even during the evaluation phase, which makes them
infeasible in re... | computer science |
27,536 | Automatic Detection of Knee Joints and Quantification of Knee
Osteoarthritis Severity using Convolutional Neural Networks | cs.CV | This paper introduces a new approach to automatically quantify the severity
of knee OA using X-ray images. Automatically quantifying knee OA severity
involves two steps: first, automatically localizing the knee joints; next,
classifying the localized knee joint images. We introduce a new approach to
automatically detec... | computer science |
27,537 | Click Here: Human-Localized Keypoints as Guidance for Viewpoint
Estimation | cs.CV | We motivate and address a human-in-the-loop variant of the monocular
viewpoint estimation task in which the location and class of one semantic
object keypoint is available at test time. In order to leverage the keypoint
information, we devise a Convolutional Neural Network called Click-Here CNN
(CH-CNN) that integrates... | computer science |
27,538 | Novel Structured Low-rank algorithm to recover spatially smooth
exponential image time series | cs.CV | We propose a structured low rank matrix completion algorithm to recover a
time series of images consisting of linear combination of exponential
parameters at every pixel, from under-sampled Fourier measurements. The spatial
smoothness of these parameters is exploited along with the exponential
structure of the time ser... | computer science |
27,539 | Learning with Privileged Information for Multi-Label Classification | cs.CV | In this paper, we propose a novel approach for learning multi-label
classifiers with the help of privileged information. Specifically, we use
similarity constraints to capture the relationship between available
information and privileged information, and use ranking constraints to capture
the dependencies among multipl... | computer science |
27,540 | One Network to Solve Them All --- Solving Linear Inverse Problems using
Deep Projection Models | cs.CV | While deep learning methods have achieved state-of-the-art performance in
many challenging inverse problems like image inpainting and super-resolution,
they invariably involve problem-specific training of the networks. Under this
approach, different problems require different networks. In scenarios where we
need to sol... | computer science |
27,541 | Who's Better, Who's Best: Skill Determination in Video using Deep
Ranking | cs.CV | This paper presents a method for assessing skill of performance from video,
for a variety of tasks, ranging from drawing to surgery and rolling dough. We
formulate the problem as pairwise and overall ranking of video collections, and
propose a supervised deep ranking model to learn discriminative features
between pairs... | computer science |
27,542 | Towards thinner convolutional neural networks through Gradually Global
Pruning | cs.CV | Deep network pruning is an effective method to reduce the storage and
computation cost of deep neural networks when applying them to resource-limited
devices. Among many pruning granularities, neuron level pruning will remove
redundant neurons and filters in the model and result in thinner networks. In
this paper, we p... | computer science |
27,543 | Bundle Optimization for Multi-aspect Embedding | cs.CV | Understanding semantic similarity among images is the core of a wide range of
computer vision applications. An important step towards this goal is to collect
and learn human perceptions. Interestingly, the semantic context of images is
often ambiguous as images can be perceived with emphasis on different aspects,
which... | computer science |
27,544 | Sentiment Recognition in Egocentric Photostreams | cs.CV | Lifelogging is a process of collecting rich source of information about daily
life of people. In this paper, we introduce the problem of sentiment analysis
in egocentric events focusing on the moments that compose the images recalling
positive, neutral or negative feelings to the observer. We propose a method for
the c... | computer science |
27,545 | Iterative Object and Part Transfer for Fine-Grained Recognition | cs.CV | The aim of fine-grained recognition is to identify sub-ordinate categories in
images like different species of birds. Existing works have confirmed that, in
order to capture the subtle differences across the categories, automatic
localization of objects and parts is critical. Most approaches for object and
part localiz... | computer science |
27,546 | Flow-Guided Feature Aggregation for Video Object Detection | cs.CV | Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to... | computer science |
27,547 | Pose-conditioned Spatio-Temporal Attention for Human Action Recognition | cs.CV | We address human action recognition from multi-modal video data involving
articulated pose and RGB frames and propose a two-stream approach. The pose
stream is processed with a convolutional model taking as input a 3D tensor
holding data from a sub-sequence. A specific joint ordering, which respects the
topology of the... | computer science |
27,548 | Improved Lossy Image Compression with Priming and Spatially Adaptive Bit
Rates for Recurrent Networks | cs.CV | We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wi... | computer science |
27,549 | Google Map Aided Visual Navigation for UAVs in GPS-denied Environment | cs.CV | We propose a framework for Google Map aided UAV navigation in GPS-denied
environment. Geo-referenced navigation provides drift-free localization and
does not require loop closures. The UAV position is initialized via
correlation, which is simple and efficient. We then use optical flow to predict
its position in subsequ... | computer science |
27,550 | Unrestricted Facial Geometry Reconstruction Using Image-to-Image
Translation | cs.CV | It has been recently shown that neural networks can recover the geometric
structure of a face from a single given image. A common denominator of most
existing face geometry reconstruction methods is the restriction of the
solution space to some low-dimensional subspace. While such a model
significantly simplifies the r... | computer science |
27,551 | CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training | cs.CV | We present variational generative adversarial networks, a general learning
framework that combines a variational auto-encoder with a generative
adversarial network, for synthesizing images in fine-grained categories, such
as faces of a specific person or objects in a category. Our approach models an
image as a composit... | computer science |
27,552 | Detecting Human Interventions on the Landscape: KAZE Features, Poisson
Point Processes, and a Construction Dataset | cs.CV | We present an algorithm capable of identifying a wide variety of
human-induced change on the surface of the planet by analyzing matches between
local features in time-sequenced remote sensing imagery. We evaluate feature
sets, match protocols, and the statistical modeling of feature matches. With
application of KAZE fe... | computer science |
27,553 | Learning High Dynamic Range from Outdoor Panoramas | cs.CV | Outdoor lighting has extremely high dynamic range. This makes the process of
capturing outdoor environment maps notoriously challenging since special
equipment must be used. In this work, we propose an alternative approach. We
first capture lighting with a regular, LDR omnidirectional camera, and aim to
recover the HDR... | computer science |
27,554 | Smartphone Based Colorimetric Detection via Machine Learning | cs.CV | We report the application of machine learning to smartphone based
colorimetric detection of pH values. The strip images were used as the training
set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms
that were able to successfully classify the distinct pH values. The difference
in the obtained ima... | computer science |
27,555 | SeGAN: Segmenting and Generating the Invisible | cs.CV | Objects often occlude each other in scenes; Inferring their appearance beyond
their visible parts plays an important role in scene understanding, depth
estimation, object interaction and manipulation. In this paper, we study the
challenging problem of completing the appearance of occluded objects. Doing so
requires kno... | computer science |
27,556 | Semantic Instance Segmentation via Deep Metric Learning | cs.CV | We propose a new method for semantic instance segmentation, by first
computing how likely two pixels are to belong to the same object, and then by
grouping similar pixels together. Our similarity metric is based on a deep,
fully convolutional embedding model. Our grouping method is based on selecting
all points that ar... | computer science |
27,557 | DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | cs.CV | We define the object detection from imagery problem as estimating a very
large but extremely sparse bounding box dependent probability distribution.
Subsequently we identify a sparse distribution estimation scheme, Directed
Sparse Sampling, and employ it in a single end-to-end CNN based detection
model. This methodolog... | computer science |
27,558 | Planecell: Representing the 3D Space with Planes | cs.CV | Reconstruction based on the stereo camera has received considerable attention
recently, but two particular challenges still remain. The first concerns the
need to aggregate similar pixels in an effective approach, and the second is to
maintain as much of the available information as possible while ensuring
sufficient a... | computer science |
27,559 | Dynamic Computational Time for Visual Attention | cs.CV | We propose a dynamic computational time model to accelerate the average
processing time for recurrent visual attention (RAM). Rather than attention
with a fixed number of steps for each input image, the model learns to decide
when to stop on the fly. To achieve this, we add an additional continue/stop
action per time s... | computer science |
27,560 | A deep learning classification scheme based on augmented-enhanced
features to segment organs at risk on the optic region in brain cancer
patients | cs.CV | Radiation therapy has emerged as one of the preferred techniques to treat
brain cancer patients. During treatment, a very high dose of radiation is
delivered to a very narrow area. Prescribed radiation therapy for brain cancer
requires precisely defining the target treatment area, as well as delineating
vital brain str... | computer science |
27,561 | Efficient optimization for Hierarchically-structured Interacting
Segments (HINTS) | cs.CV | We propose an effective optimization algorithm for a general hierarchical
segmentation model with geometric interactions between segments. Any given tree
can specify a partial order over object labels defining a hierarchy. It is
well-established that segment interactions, such as inclusion/exclusion and
margin constrai... | computer science |
27,562 | Learning Convolutional Networks for Content-weighted Image Compression | cs.CV | Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-ba... | computer science |
27,563 | MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised
Monocular Reconstruction | cs.CV | In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The c... | computer science |
27,564 | Geometric Affordances from a Single Example via the Interaction Tensor | cs.CV | This paper develops and evaluates a new tensor field representation to
express the geometric affordance of one object over another. We expand the well
known bisector surface representation to one that is weight-driven and that
retains the provenance of surface points with directional vectors. We also
incorporate the no... | computer science |
27,565 | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks | cs.CV | Image-to-image translation is a class of vision and graphics problems where
the goal is to learn the mapping between an input image and an output image
using a training set of aligned image pairs. However, for many tasks, paired
training data will not be available. We present an approach for learning to
translate an im... | computer science |
27,566 | Relevance Subject Machine: A Novel Person Re-identification Framework | cs.CV | We propose a novel method called the Relevance Subject Machine (RSM) to solve
the person re-identification (re-id) problem. RSM falls under the category of
Bayesian sparse recovery algorithms and uses the sparse representation of the
input video under a pre-defined dictionary to identify the subject in the
video. Our a... | computer science |
27,567 | Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos | cs.CV | Deep learning has been demonstrated to achieve excellent results for image
classification and object detection. However, the impact of deep learning on
video analysis (e.g. action detection and recognition) has been limited due to
complexity of video data and lack of annotations. Previous convolutional neural
networks ... | computer science |
27,568 | TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for
Activity Recognition | cs.CV | Recent two-stream deep Convolutional Neural Networks (ConvNets) have made
significant progress in recognizing human actions in videos. Despite their
success, methods extending the basic two-stream ConvNet have not systematically
explored possible network architectures to further exploit spatiotemporal
dynamics within v... | computer science |
27,569 | Concurrent Segmentation and Localization for Tracking of Surgical
Instruments | cs.CV | Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular,... | computer science |
27,570 | Deep 3D Face Identification | cs.CV | We propose a novel 3D face recognition algorithm using a deep convolutional
neural network (DCNN) and a 3D augmentation technique. The performance of 2D
face recognition algorithms has significantly increased by leveraging the
representational power of deep neural networks and the use of large-scale
labeled training da... | computer science |
27,571 | Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke
Images | cs.CV | In this paper, a deep domain adaptation based method for video smoke
detection is proposed to extract a powerful feature representation of smoke.
Due to the smoke image samples limited in scale and diversity for deep CNN
training, we systematically produced adequate synthetic smoke images with a
wide variation in the s... | computer science |
27,572 | Unsupervised Holistic Image Generation from Key Local Patches | cs.CV | We introduce a new problem of generating an image based on a small number of
key local patches without any geometric prior. In this work, key local patches
are defined as informative regions of the target object or scene. This is a
challenging problem since it requires generating realistic images and
predicting locatio... | computer science |
27,573 | Novel Framework for Spectral Clustering using Topological Node
Features(TNF) | cs.CV | Spectral clustering has gained importance in recent years due to its ability
to cluster complex data as it requires only pairwise similarity among data
points with its ease of implementation. The central point in spectral
clustering is the process of capturing pair-wise similarity. In the literature,
many research tech... | computer science |
27,574 | A Hybrid Data Association Framework for Robust Online Multi-Object
Tracking | cs.CV | Global optimization algorithms have shown impressive performance in
data-association based multi-object tracking, but handling online data remains
a difficult hurdle to overcome. In this paper, we present a hybrid data
association framework with a min-cost multi-commodity network flow for robust
online multi-object tra... | computer science |
27,575 | Semantic-driven Generation of Hyperlapse from $360^\circ$ Video | cs.CV | We present a system for converting a fully panoramic ($360^\circ$) video into
a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our
system exploits visual saliency and semantics to non-uniformly sample in space
and time for generating hyperlapses. In addition, users can optionally choose
objec... | computer science |
27,576 | End-To-End Face Detection and Recognition | cs.CV | Plenty of face detection and recognition methods have been proposed and got
delightful results in decades. Common face recognition pipeline consists of: 1)
face detection, 2) face alignment, 3) feature extraction, 4) similarity
calculation, which are separated and independent from each other. The separated
face analyzi... | computer science |
27,577 | (DE)^2 CO: Deep Depth Colorization | cs.CV | The ability to classify objects is fundamental for robots. Besides knowledge
about their visual appearance, captured by the RGB channel, robots heavily need
also depth information to make sense of the world. While the use of deep
networks on RGB robot images has benefited from the plethora of results
obtained on databa... | computer science |
27,578 | Single Image Super Resolution - When Model Adaptation Matters | cs.CV | In the recent years impressive advances were made for single image
super-resolution. Deep learning is behind a big part of this success. Deep(er)
architecture design and external priors modeling are the key ingredients. The
internal contents of the low resolution input image is neglected with deep
modeling despite the ... | computer science |
27,579 | BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for
Predicting the 3D Poses of Challenging Objects without Using Depth | cs.CV | We introduce a novel method for 3D object detection and pose estimation from
color images only. We first use segmentation to detect the objects of interest
in 2D even in presence of partial occlusions and cluttered background. By
contrast with recent patch-based methods, we rely on a "holistic" approach: We
apply to th... | computer science |
27,580 | Thin-Slicing Network: A Deep Structured Model for Pose Estimation in
Videos | cs.CV | Deep ConvNets have been shown to be effective for the task of human pose
estimation from single images. However, several challenging issues arise in the
video-based case such as self-occlusion, motion blur, and uncommon poses with
few or no examples in training data sets. Temporal information can provide
additional cue... | computer science |
27,581 | Unsupervised learning from video to detect foreground objects in single
images | cs.CV | Unsupervised learning from visual data is one of the most difficult
challenges in computer vision, being a fundamental task for understanding how
visual recognition works. From a practical point of view, learning from
unsupervised visual input has an immense practical value, as very large
quantities of unlabeled videos... | computer science |
27,582 | Fast Predictive Multimodal Image Registration | cs.CV | We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be appl... | computer science |
27,583 | Quicksilver: Fast Predictive Image Registration - a Deep Learning
Approach | cs.CV | This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we ... | computer science |
27,584 | InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single
Image | cs.CV | We introduce InverseFaceNet, a deep convolutional inverse rendering framework
for faces that jointly estimates facial pose, shape, expression, reflectance
and illumination from a single input image in a single shot. By estimating all
these parameters from just a single image, advanced editing possibilities on a
single ... | computer science |
27,585 | Transfer of View-manifold Learning to Similarity Perception of Novel
Objects | cs.CV | We develop a model of perceptual similarity judgment based on re-training a
deep convolution neural network (DCNN) that learns to associate different views
of each 3D object to capture the notion of object persistence and continuity in
our visual experience. The re-training process effectively performs distance
metric ... | computer science |
27,586 | Efficient Registration of Pathological Images: A Joint
PCA/Image-Reconstruction Approach | cs.CV | Registration involving one or more images containing pathologies is
challenging, as standard image similarity measures and spatial transforms
cannot account for common changes due to pathologies. Low-rank/Sparse (LRS)
decomposition removes pathologies prior to registration; however, LRS is
memory-demanding and slow, wh... | computer science |
27,587 | Geodesic Distance Histogram Feature for Video Segmentation | cs.CV | This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorp... | computer science |
27,588 | Efficient Asymmetric Co-Tracking using Uncertainty Sampling | cs.CV | Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating ... | computer science |
27,589 | View Selection with Geometric Uncertainty Modeling | cs.CV | Estimating positions of world points from features observed in images is a
key problem in 3D reconstruction, image mosaicking,simultaneous localization
and mapping and structure from motion. We consider a special instance in which
there is a dominant ground plane $\mathcal{G}$ viewed from a parallel viewing
plane $\mat... | computer science |
27,590 | Customizing First Person Image Through Desired Actions | cs.CV | This paper studies a problem of inverse visual path planning: creating a
visual scene from a first person action. Our conjecture is that the spatial
arrangement of a first person visual scene is deployed to afford an action, and
therefore, the action can be inversely used to synthesize a new scene such that
the action ... | computer science |
27,591 | Multiple Instance Detection Network with Online Instance Classifier
Refinement | cs.CV | Of late, weakly supervised object detection is with great importance in
object recognition. Based on deep learning, weakly supervised detectors have
achieved many promising results. However, compared with fully supervised
detection, it is more challenging to train deep network based detectors in a
weakly supervised man... | computer science |
27,592 | Compositional Human Pose Regression | cs.CV | Regression based methods are not performing as well as detection based
methods for human pose estimation. A central problem is that the structural
information in the pose is not well exploited in the previous regression
methods. In this work, we propose a structure-aware regression approach. It
adopts a reparameterized... | computer science |
27,593 | Complexity-Aware Assignment of Latent Values in Discriminative Models
for Accurate Gesture Recognition | cs.CV | Many of the state-of-the-art algorithms for gesture recognition are based on
Conditional Random Fields (CRFs). Successful approaches, such as the
Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose
values are mapped to the values of the labels. In this paper we propose a novel
methodology to se... | computer science |
27,594 | A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural
Network for Photo Aesthetic Assessment | cs.CV | Deep convolutional neural networks (CNN) have recently been shown to generate
promising results for aesthetics assessment. However, the performance of these
deep CNN methods is often compromised by the constraint that the neural network
only takes the fixed-size input. To accommodate this requirement, input images
need... | computer science |
27,595 | SAR image despeckling through convolutional neural networks | cs.CV | In this paper we investigate the use of discriminative model learning through
Convolutional Neural Networks (CNNs) for SAR image despeckling. The network
uses a residual learning strategy, hence it does not recover the filtered
image, but the speckle component, which is then subtracted from the noisy one.
Training is c... | computer science |
27,596 | The Stixel world: A medium-level representation of traffic scenes | cs.CV | Recent progress in advanced driver assistance systems and the race towards
autonomous vehicles is mainly driven by two factors: (1) increasingly
sophisticated algorithms that interpret the environment around the vehicle and
react accordingly, and (2) the continuous improvements of sensor technology
itself. In terms of ... | computer science |
27,597 | Efficient Version-Space Reduction for Visual Tracking | cs.CV | Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to va... | computer science |
27,598 | People Counting in Crowded and Outdoor Scenes using a Hybrid
Multi-Camera Approach | cs.CV | This paper presents two novel approaches for people counting in crowded and
open environments that combine the information gathered by multiple views.
Multiple camera are used to expand the field of view as well as to mitigate the
problem of occlusion that commonly affects the performance of counting methods
using sing... | computer science |
27,599 | Randomness in Deconvolutional Networks for Visual Representation | cs.CV | Toward a deeper understanding on the inner work of deep neural networks, we
investigate CNN (convolutional neural network) using DCN (deconvolutional
network) and randomization technique, and gain new insights for the intrinsic
property of this network architecture. For the random representations of an
untrained CNN, w... | computer science |
27,600 | Dense Multi-view 3D-reconstruction Without Dense Correspondences | cs.CV | We introduce a variational method for multi-view shape-from-shading under
natural illumination. The key idea is to couple PDE-based solutions for
single-image based shape-from-shading problems across multiple images and
multiple color channels by means of a variational formulation. Rather than
alternatingly solving the... | computer science |
27,601 | Geometric Loss Functions for Camera Pose Regression with Deep Learning | cs.CV | Deep learning has shown to be effective for robust and real-time monocular
image relocalisation. In particular, PoseNet is a deep convolutional neural
network which learns to regress the 6-DOF camera pose from a single image. It
learns to localize using high level features and is robust to difficult
lighting, motion bl... | computer science |
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