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25,802 | Auxiliary Image Regularization for Deep CNNs with Noisy Labels | cs.CV | Precisely-labeled data sets with sufficient amount of samples are very
important for training deep convolutional neural networks (CNNs). However, many
of the available real-world data sets contain erroneously labeled samples and
those errors substantially hinder the learning of very accurate CNN models. In
this work, w... | computer science |
25,803 | Adapting Deep Visuomotor Representations with Weak Pairwise Constraints | cs.CV | Real-world robotics problems often occur in domains that differ significantly
from the robot's prior training environment. For many robotic control tasks,
real world experience is expensive to obtain, but data is easy to collect in
either an instrumented environment or in simulation. We propose a novel domain
adaptatio... | computer science |
25,804 | Multi-Scale Context Aggregation by Dilated Convolutions | cs.CV | State-of-the-art models for semantic segmentation are based on adaptations of
convolutional networks that had originally been designed for image
classification. However, dense prediction and image classification are
structurally different. In this work, we develop a new convolutional network
module that is specifically... | computer science |
25,805 | DeePM: A Deep Part-Based Model for Object Detection and Semantic Part
Localization | cs.CV | In this paper, we propose a deep part-based model (DeePM) for symbiotic
object detection and semantic part localization. For this purpose, we annotate
semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset,
which provides information on object pose, occlusion, viewpoint and
functionality. DeePM is a... | computer science |
25,806 | Face Alignment Across Large Poses: A 3D Solution | cs.CV | Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in CV community.
However, most algorithms are designed for faces in small to medium poses (below
45 degree), lacking the ability to align faces in large poses up to 90 degree.
The challen... | computer science |
25,807 | Rendering refraction and reflection of eyeglasses for synthetic eye
tracker images | cs.CV | While for the evaluation of robustness of eye tracking algorithms the use of
real-world data is essential, there are many applications where simulated,
synthetic eye images are of advantage. They can generate labelled ground-truth
data for appearance based gaze estimation algorithms or enable the development
of model b... | computer science |
25,808 | Node Specificity in Convolutional Deep Nets Depends on Receptive Field
Position and Size | cs.CV | In convolutional deep neural networks, receptive field (RF) size increases
with hierarchical depth. When RF size approaches full coverage of the input
image, different RF positions result in RFs with different specificity, as
portions of the RF fall out of the input space. This leads to a departure from
the convolution... | computer science |
25,809 | Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation | cs.CV | Deep neural networks with alternating convolutional, max-pooling and
decimation layers are widely used in state of the art architectures for
computer vision. Max-pooling purposefully discards precise spatial information
in order to create features that are more robust, and typically organized as
lower resolution spatia... | computer science |
25,810 | Where To Look: Focus Regions for Visual Question Answering | cs.CV | We present a method that learns to answer visual questions by selecting image
regions relevant to the text-based query. Our method exhibits significant
improvements in answering questions such as "what color," where it is necessary
to evaluate a specific location, and "what room," where it selectively
identifies inform... | computer science |
25,811 | Learning Visual Predictive Models of Physics for Playing Billiards | cs.CV | The ability to plan and execute goal specific actions in varied, unexpected
settings is a central requirement of intelligent agents. In this paper, we
explore how an agent can be equipped with an internal model of the dynamics of
the external world, and how it can use this model to plan novel actions by
running multipl... | computer science |
25,812 | Real-Time Anomalous Behavior Detection and Localization in Crowded
Scenes | cs.CV | In this paper, we propose an accurate and real-time anomaly detection and
localization in crowded scenes, and two descriptors for representing anomalous
behavior in video are proposed. We consider a video as being a set of cubic
patches. Based on the low likelihood of an anomaly occurrence, and the
redundancy of struct... | computer science |
25,813 | Constrained Deep Metric Learning for Person Re-identification | cs.CV | Person re-identification aims to re-identify the probe image from a given set
of images under different camera views. It is challenging due to large
variations of pose, illumination, occlusion and camera view. Since the
convolutional neural networks (CNN) have excellent capability of feature
extraction, certain deep le... | computer science |
25,814 | Mouse Pose Estimation From Depth Images | cs.CV | We focus on the challenging problem of efficient mouse 3D pose estimation
based on static images, and especially single depth images. We introduce an
approach to discriminatively train the split nodes of trees in random forest to
improve their performance on estimation of 3D joint positions of mouse. Our
algorithm is c... | computer science |
25,815 | Bayesian Identification of Fixations, Saccades, and Smooth Pursuits | cs.CV | Smooth pursuit eye movements provide meaningful insights and information on
subject's behavior and health and may, in particular situations, disturb the
performance of typical fixation/saccade classification algorithms. Thus, an
automatic and efficient algorithm to identify these eye movements is paramount
for eye-trac... | computer science |
25,816 | Weakly Supervised Object Boundaries | cs.CV | State-of-the-art learning based boundary detection methods require extensive
training data. Since labelling object boundaries is one of the most expensive
types of annotations, there is a need to relax the requirement to carefully
annotate images to make both the training more affordable and to extend the
amount of tra... | computer science |
25,817 | Shape and Symmetry Induction for 3D Objects | cs.CV | Actions as simple as grasping an object or navigating around it require a
rich understanding of that object's 3D shape from a given viewpoint. In this
paper we repurpose powerful learning machinery, originally developed for object
classification, to discover image cues relevant for recovering the 3D shape of
potentiall... | computer science |
25,818 | Principal Basis Analysis in Sparse Representation | cs.CV | This article introduces a new signal analysis method, which can be
interpreted as a principal component analysis in sparse decomposition of the
signal. The method, called principal basis analysis, is based on a novel
criterion: reproducibility of component which is an intrinsic characteristic of
regularity in natural s... | computer science |
25,819 | Video Tracking Using Learned Hierarchical Features | cs.CV | In this paper, we propose an approach to learn hierarchical features for
visual object tracking. First, we offline learn features robust to diverse
motion patterns from auxiliary video sequences. The hierarchical features are
learned via a two-layer convolutional neural network. Embedding the temporal
slowness constrai... | computer science |
25,820 | PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset | cs.CV | In this paper, we address the boundary detection task motivated by the
ambiguities in current definition of edge detection. To this end, we generate a
large database consisting of more than 10k images (which is 20x bigger than
existing edge detection databases) along with ground truth boundaries between
459 semantic cl... | computer science |
25,821 | Calculate distance to object in the area where car, using video analysis | cs.CV | The method of using video cameras installed on the car, to calculate the
distance to the object in its area of movement. | computer science |
25,822 | Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry | cs.CV | The discrimination and simplicity of features are very important for
effective and efficient pedestrian detection. However, most state-of-the-art
methods are unable to achieve good tradeoff between accuracy and efficiency.
Inspired by some simple inherent attributes of pedestrians (i.e., appearance
constancy and shape ... | computer science |
25,823 | Higher Order Conditional Random Fields in Deep Neural Networks | cs.CV | We address the problem of semantic segmentation using deep learning. Most
segmentation systems include a Conditional Random Field (CRF) to produce a
structured output that is consistent with the image's visual features. Recent
deep learning approaches have incorporated CRFs into Convolutional Neural
Networks (CNNs), wi... | computer science |
25,824 | Unsupervised Deep Feature Extraction for Remote Sensing Image
Classification | cs.CV | This paper introduces the use of single layer and deep convolutional networks
for remote sensing data analysis. Direct application to multi- and
hyper-spectral imagery of supervised (shallow or deep) convolutional networks
is very challenging given the high input data dimensionality and the relatively
small amount of a... | computer science |
25,825 | Tracking Motion and Proxemics using Thermal-sensor Array | cs.CV | Indoor tracking has all-pervasive applications beyond mere surveillance, for
example in education, health monitoring, marketing, energy management and so
on. Image and video based tracking systems are intrusive. Thermal array sensors
on the other hand can provide coarse-grained tracking while preserving privacy
of the ... | computer science |
25,826 | Exploring Person Context and Local Scene Context for Object Detection | cs.CV | In this paper we explore two ways of using context for object detection. The
first model focusses on people and the objects they commonly interact with,
such as fashion and sports accessories. The second model considers more general
object detection and uses the spatial relationships between objects and between
objects... | computer science |
25,827 | A Computational Model for Amodal Completion | cs.CV | This paper presents a computational model to recover the most likely
interpretation of the 3D scene structure from a planar image, where some
objects may occlude others. The estimated scene interpretation is obtained by
integrating some global and local cues and provides both the complete
disoccluded objects that form ... | computer science |
25,828 | Towards Automatic Image Editing: Learning to See another You | cs.CV | Learning the distribution of images in order to generate new samples is a
challenging task due to the high dimensionality of the data and the highly
non-linear relations that are involved. Nevertheless, some promising results
have been reported in the literature recently,building on deep network
architectures. In this ... | computer science |
25,829 | An analysis of the factors affecting keypoint stability in scale-space | cs.CV | The most popular image matching algorithm SIFT, introduced by D. Lowe a
decade ago, has proven to be sufficiently scale invariant to be used in
numerous applications. In practice, however, scale invariance may be weakened
by various sources of error inherent to the SIFT implementation affecting the
stability and accura... | computer science |
25,830 | TennisVid2Text: Fine-grained Descriptions for Domain Specific Videos | cs.CV | Automatically describing videos has ever been fascinating. In this work, we
attempt to describe videos from a specific domain - broadcast videos of lawn
tennis matches. Given a video shot from a tennis match, we intend to generate a
textual commentary similar to what a human expert would write on a sports
website. Unli... | computer science |
25,831 | Structured learning of metric ensembles with application to person
re-identification | cs.CV | Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is ... | computer science |
25,832 | Loss Functions for Neural Networks for Image Processing | cs.CV | Neural networks are becoming central in several areas of computer vision and
image processing and different architectures have been proposed to solve
specific problems. The impact of the loss layer of neural networks, however,
has not received much attention in the context of image processing: the default
and virtually... | computer science |
25,833 | Real-Time Depth Refinement for Specular Objects | cs.CV | The introduction of consumer RGB-D scanners set off a major boost in 3D
computer vision research. Yet, the precision of existing depth scanners is not
accurate enough to recover fine details of a scanned object. While modern
shading based depth refinement methods have been proven to work well with
Lambertian objects, t... | computer science |
25,834 | Sliding-Window Optimization on an Ambiguity-Clearness Graph for
Multi-object Tracking | cs.CV | Multi-object tracking remains challenging due to frequent occurrence of
occlusions and outliers. In order to handle this problem, we propose an
Approximation-Shrink Scheme for sequential optimization. This scheme is
realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and
maintain sequence independen... | computer science |
25,835 | Sparseness helps: Sparsity Augmented Collaborative Representation for
Classification | cs.CV | Many classification approaches first represent a test sample using the
training samples of all the classes. This collaborative representation is then
used to label the test sample. It was a common belief that sparseness of the
representation is the key to success for this classification scheme. However,
more recently, ... | computer science |
25,836 | On-line Recognition of Handwritten Mathematical Symbols | cs.CV | Finding the name of an unknown symbol is often hard, but writing the symbol
is easy. This bachelor's thesis presents multiple systems that use the pen
trajectory to classify handwritten symbols. Five preprocessing steps, one data
augmentation algorithm, five features and five variants for multilayer
Perceptron training... | computer science |
25,837 | The Multiverse Loss for Robust Transfer Learning | cs.CV | Deep learning techniques are renowned for supporting effective transfer
learning. However, as we demonstrate, the transferred representations support
only a few modes of separation and much of its dimensionality is unutilized. In
this work, we suggest to learn, in the source domain, multiple orthogonal
classifiers. We ... | computer science |
25,838 | Sparse Coral Classification Using Deep Convolutional Neural Networks | cs.CV | Autonomous repair of deep-sea coral reefs is a recent proposed idea to
support the oceans ecosystem in which is vital for commercial fishing, tourism
and other species. This idea can be operated through using many small
autonomous underwater vehicles (AUVs) and swarm intelligence techniques to
locate and replace chunks... | computer science |
25,839 | Hierarchical Invariant Feature Learning with Marginalization for Person
Re-Identification | cs.CV | This paper addresses the problem of matching pedestrians across multiple
camera views, known as person re-identification. Variations in lighting
conditions, environment and pose changes across camera views make
re-identification a challenging problem. Previous methods address these
challenges by designing specific feat... | computer science |
25,840 | Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015
Robust Reading Competition Challenge 4 | cs.CV | Different from focused texts present in natural images, which are captured
with user's intention and intervention, incidental texts usually exhibit much
more diversity, variability and complexity, thus posing significant
difficulties and challenges for scene text detection and recognition
algorithms. The ICDAR 2015 Rob... | computer science |
25,841 | Fine-Grained Classification via Mixture of Deep Convolutional Neural
Networks | cs.CV | We present a novel deep convolutional neural network (DCNN) system for
fine-grained image classification, called a mixture of DCNNs (MixDCNN). The
fine-grained image classification problem is characterised by large intra-class
variations and small inter-class variations. To overcome these problems our
proposed MixDCNN ... | computer science |
25,842 | Design of Kernels in Convolutional Neural Networks for Image
Classification | cs.CV | Despite the effectiveness of Convolutional Neural Networks (CNNs) for image
classification, our understanding of the relationship between shape of
convolution kernels and learned representations is limited. In this work, we
explore and employ the relationship between shape of kernels which define
Receptive Fields (RFs)... | computer science |
25,843 | Behavior Discovery and Alignment of Articulated Object Classes from
Unstructured Video | cs.CV | We propose an automatic system for organizing the content of a collection of
unstructured videos of an articulated object class (e.g. tiger, horse). By
exploiting the recurring motion patterns of the class across videos, our
system: 1) identifies its characteristic behaviors; and 2) recovers
pixel-to-pixel alignments a... | computer science |
25,844 | Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video | cs.CV | This paper addresses the challenge of 3D full-body human pose estimation from
a monocular image sequence. Here, two cases are considered: (i) the image
locations of the human joints are provided and (ii) the image locations of
joints are unknown. In the former case, a novel approach is introduced that
integrates a spar... | computer science |
25,845 | Implicit Sparse Code Hashing | cs.CV | We address the problem of converting large-scale high-dimensional image data
into binary codes so that approximate nearest-neighbor search over them can be
efficiently performed. Different from most of the existing unsupervised
approaches for yielding binary codes, our method is based on a
dimensionality-reduction crit... | computer science |
25,846 | Analyzing Classifiers: Fisher Vectors and Deep Neural Networks | cs.CV | Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and
successful algorithms for solving image classification problems. However, both
are generally considered `black box' predictors as the non-linear
transformations involved have so far prevented transparent and interpretable
reasoning. Recently, a p... | computer science |
25,847 | Fast and High Quality Highlight Removal from A Single Image | cs.CV | Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversi... | computer science |
25,848 | Labeling the Features Not the Samples: Efficient Video Classification
with Minimal Supervision | cs.CV | Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following ... | computer science |
25,849 | Rethinking the Inception Architecture for Computer Vision | cs.CV | Convolutional networks are at the core of most state-of-the-art computer
vision solutions for a wide variety of tasks. Since 2014 very deep
convolutional networks started to become mainstream, yielding substantial gains
in various benchmarks. Although increased model size and computational cost
tend to translate to imm... | computer science |
25,850 | The MegaFace Benchmark: 1 Million Faces for Recognition at Scale | cs.CV | Recent face recognition experiments on a major benchmark LFW show stunning
performance--a number of algorithms achieve near to perfect score, surpassing
human recognition rates. In this paper, we advocate evaluations at the million
scale (LFW includes only 13K photos of 5K people). To this end, we have
assembled the Me... | computer science |
25,851 | Double Sparse Multi-Frame Image Super Resolution | cs.CV | A large number of image super resolution algorithms based on the sparse
coding are proposed, and some algorithms realize the multi-frame super
resolution. In multi-frame super resolution based on the sparse coding, both
accurate image registration and sparse coding are required. Previous study on
multi-frame super reso... | computer science |
25,852 | Active Learning for Delineation of Curvilinear Structures | cs.CV | Many recent delineation techniques owe much of their increased effectiveness
to path classification algorithms that make it possible to distinguish
promising paths from others. The downside of this development is that they
require annotated training data, which is tedious to produce.
In this paper, we propose an Acti... | computer science |
25,853 | Actions ~ Transformations | cs.CV | What defines an action like "kicking ball"? We argue that the true meaning of
an action lies in the change or transformation an action brings to the
environment. In this paper, we propose a novel representation for actions by
modeling an action as a transformation which changes the state of the
environment before the a... | computer science |
25,854 | Compressive hyperspectral imaging via adaptive sampling and dictionary
learning | cs.CV | In this paper, we propose a new sampling strategy for hyperspectral signals
that is based on dictionary learning and singular value decomposition (SVD).
Specifically, we first learn a sparsifying dictionary from training spectral
data using dictionary learning. We then perform an SVD on the dictionary and
use the first... | computer science |
25,855 | The Indian Spontaneous Expression Database for Emotion Recognition | cs.CV | Automatic recognition of spontaneous facial expressions is a major challenge
in the field of affective computing. Head rotation, face pose, illumination
variation, occlusion etc. are the attributes that increase the complexity of
recognition of spontaneous expressions in practical applications. Effective
recognition of... | computer science |
25,856 | A Literature Survey of various Fingerprint De-noising Techniques to
justify the need of a new De-noising model based upon Pixel Component
Analysis | cs.CV | Image Preprocessing is a vital step in the field of image processing for
biometric pattern recognition. This paper studies and reviews various classical
and modern fingerprint image de-noising models. The various model used for
de-noising ranges widely from transform matrix using frequency, histogram model
de-noising, ... | computer science |
25,857 | Weighted Schatten $p$-Norm Minimization for Image Denoising and
Background Subtraction | cs.CV | Low rank matrix approximation (LRMA), which aims to recover the underlying
low rank matrix from its degraded observation, has a wide range of applications
in computer vision. The latest LRMA methods resort to using the nuclear norm
minimization (NNM) as a convex relaxation of the nonconvex rank minimization.
However, N... | computer science |
25,858 | Simulations for Validation of Vision Systems | cs.CV | As the computer vision matures into a systems science and engineering
discipline, there is a trend in leveraging latest advances in computer graphics
simulations for performance evaluation, learning, and inference. However, there
is an open question on the utility of graphics simulations for vision with
apparently cont... | computer science |
25,859 | Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees | cs.CV | In this paper, we study the problem of learning a model for human pose
estimation as mixtures of compositional sub-trees in two layers of prediction.
This involves estimating the pose of a sub-tree followed by identifying the
relationships between sub-trees that are used to handle occlusions between
different parts. Th... | computer science |
25,860 | Prototypical Priors: From Improving Classification to Zero-Shot Learning | cs.CV | Recent works on zero-shot learning make use of side information such as
visual attributes or natural language semantics to define the relations between
output visual classes and then use these relationships to draw inference on new
unseen classes at test time. In a novel extension to this idea, we propose the
use of vi... | computer science |
25,861 | What can we learn about CNNs from a large scale controlled object
dataset? | cs.CV | Tolerance to image variations (e.g. translation, scale, pose, illumination)
is an important desired property of any object recognition system, be it human
or machine. Moving towards increasingly bigger datasets has been trending in
computer vision specially with the emergence of highly popular deep learning
models. Whi... | computer science |
25,862 | Staple: Complementary Learners for Real-Time Tracking | cs.CV | Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to def... | computer science |
25,863 | Sublabel-Accurate Relaxation of Nonconvex Energies | cs.CV | We propose a novel spatially continuous framework for convex relaxations
based on functional lifting. Our method can be interpreted as a
sublabel-accurate solution to multilabel problems. We show that previously
proposed functional lifting methods optimize an energy which is linear between
two labels and hence require ... | computer science |
25,864 | Model Validation for Vision Systems via Graphics Simulation | cs.CV | Rapid advances in computation, combined with latest advances in computer
graphics simulations have facilitated the development of vision systems and
training them in virtual environments. One major stumbling block is in
certification of the designs and tuned parameters of these systems to work in
real world. In this pa... | computer science |
25,865 | ASIST: Automatic Semantically Invariant Scene Transformation | cs.CV | We present ASIST, a technique for transforming point clouds by replacing
objects with their semantically equivalent counterparts. Transformations of
this kind have applications in virtual reality, repair of fused scans, and
robotics. ASIST is based on a unified formulation of semantic labeling and
object replacement; b... | computer science |
25,866 | Motion trails from time-lapse video | cs.CV | From an image sequence captured by a stationary camera, background
subtraction can detect moving foreground objects in the scene. Distinguishing
foreground from background is further improved by various heuristics. Then each
object's motion can be emphasized by duplicating its positions as a motion
trail. These trails ... | computer science |
25,867 | A Deep Structured Model with Radius-Margin Bound for 3D Human Activity
Recognition | cs.CV | Understanding human activity is very challenging even with the recently
developed 3D/depth sensors. To solve this problem, this work investigates a
novel deep structured model, which adaptively decomposes an activity instance
into temporal parts using the convolutional neural networks (CNNs). Our model
advances the tra... | computer science |
25,868 | A Shapley Value Solution to Game Theoretic-based Feature Reduction in
False Alarm Detection | cs.CV | False alarm is one of the main concerns in intensive care units and can
result in care disruption, sleep deprivation, and insensitivity of care-givers
to alarms. Several methods have been proposed to suppress the false alarm rate
through improving the quality of physiological signals by filtering, and
developing more a... | computer science |
25,869 | Maximum Entropy Binary Encoding for Face Template Protection | cs.CV | In this paper we present a framework for secure identification using deep
neural networks, and apply it to the task of template protection for face
authentication. We use deep convolutional neural networks (CNNs) to learn a
mapping from face images to maximum entropy binary (MEB) codes. The mapping is
robust enough to ... | computer science |
25,870 | Vanishing point attracts gaze in free-viewing and visual search tasks | cs.CV | To investigate whether the vanishing point (VP) plays a significant role in
gaze guidance, we ran two experiments. In the first one, we recorded fixations
of 10 observers (4 female; mean age 22; SD=0.84) freely viewing 532 images, out
of which 319 had VP (shuffled presentation; each image for 4 secs). We found
that the... | computer science |
25,871 | Image reconstruction from dense binary pixels | cs.CV | Recently, the dense binary pixel Gigavision camera had been introduced,
emulating a digital version of the photographic film. While seems to be a
promising solution for HDR imaging, its output is not directly usable and
requires an image reconstruction process. In this work, we formulate this
problem as the minimizatio... | computer science |
25,872 | The Next Best Underwater View | cs.CV | To image in high resolution large and occlusion-prone scenes, a camera must
move above and around. Degradation of visibility due to geometric occlusions
and distances is exacerbated by scattering, when the scene is in a
participating medium. Moreover, underwater and in other media, artificial
lighting is needed. Overal... | computer science |
25,873 | PatchBatch: a Batch Augmented Loss for Optical Flow | cs.CV | We propose a new pipeline for optical flow computation, based on Deep
Learning techniques. We suggest using a Siamese CNN to independently, and in
parallel, compute the descriptors of both images. The learned descriptors are
then compared efficiently using the L2 norm and do not require network
processing of patch pair... | computer science |
25,874 | Rank Pooling for Action Recognition | cs.CV | We propose a function-based temporal pooling method that captures the latent
structure of the video sequence data - e.g. how frame-level features evolve
over time in a video. We show how the parameters of a function that has been
fit to the video data can serve as a robust new video representation. As a
specific exampl... | computer science |
25,875 | Fixation prediction with a combined model of bottom-up saliency and
vanishing point | cs.CV | By predicting where humans look in natural scenes, we can understand how they
perceive complex natural scenes and prioritize information for further
high-level visual processing. Several models have been proposed for this
purpose, yet there is a gap between best existing saliency models and human
performance. While man... | computer science |
25,876 | Recognition from Hand Cameras | cs.CV | We revisit the study of a wrist-mounted camera system (referred to as
HandCam) for recognizing activities of hands. HandCam has two unique properties
as compared to egocentric systems (referred to as HeadCam): (1) it avoids the
need to detect hands; (2) it more consistently observes the activities of
hands. By taking a... | computer science |
25,877 | Sparsifying Neural Network Connections for Face Recognition | cs.CV | This paper proposes to learn high-performance deep ConvNets with sparse
neural connections, referred to as sparse ConvNets, for face recognition. The
sparse ConvNets are learned in an iterative way, each time one additional layer
is sparsified and the entire model is re-trained given the initial weights
learned in prev... | computer science |
25,878 | Scalable domain adaptation of convolutional neural networks | cs.CV | Convolutional neural networks (CNNs) tend to become a standard approach to
solve a wide array of computer vision problems. Besides important theoretical
and practical advances in their design, their success is built on the existence
of manually labeled visual resources, such as ImageNet. The creation of such
datasets i... | computer science |
25,879 | Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images | cs.CV | Image representations, from SIFT and bag of visual words to Convolutional
Neural Networks (CNNs) are a crucial component of almost all computer vision
systems. However, our understanding of them remains limited. In this paper we
study several landmark representations, both shallow and deep, by a number of
complementary... | computer science |
25,880 | On The Continuous Steering of the Scale of Tight Wavelet Frames | cs.CV | In analogy with steerable wavelets, we present a general construction of
adaptable tight wavelet frames, with an emphasis on scaling operations. In
particular, the derived wavelets can be "dilated" by a procedure comparable to
the operation of steering steerable wavelets. The fundamental aspects of the
construction are... | computer science |
25,881 | In-situ multi-scattering tomography | cs.CV | To recover the three dimensional (3D) volumetric distribution of matter in an
object, images of the object are captured from multiple directions and
locations. Using these images tomographic computations extract the
distribution. In highly scattering media and constrained, natural irradiance,
tomography must explicitly... | computer science |
25,882 | Direct Intrinsics: Learning Albedo-Shading Decomposition by
Convolutional Regression | cs.CV | We introduce a new approach to intrinsic image decomposition, the task of
decomposing a single image into albedo and shading components. Our strategy,
which we term direct intrinsics, is to learn a convolutional neural network
(CNN) that directly predicts output albedo and shading channels from an input
RGB image patch... | computer science |
25,883 | SSD: Single Shot MultiBox Detector | cs.CV | We present a method for detecting objects in images using a single deep
neural network. Our approach, named SSD, discretizes the output space of
bounding boxes into a set of default boxes over different aspect ratios and
scales per feature map location. At prediction time, the network generates
scores for the presence ... | computer science |
25,884 | Learning to Point and Count | cs.CV | This paper proposes the problem of point-and-count as a test case to break
the what-and-where deadlock. Different from the traditional detection problem,
the goal is to discover key salient points as a way to localize and count the
number of objects simultaneously. We propose two alternatives, one that counts
first and... | computer science |
25,885 | Computational Models for Multiview Dense Depth Maps of Dynamic Scene | cs.CV | This paper reviews the recent progresses of the depth map generation for
dynamic scene and its corresponding computational models. This paper mainly
covers the homogeneous ambiguity models in depth sensing, resolution models in
depth processing, and consistency models in depth optimization. We also
summarize the future... | computer science |
25,886 | Is Hamming distance the only way for matching binary image feature
descriptors? | cs.CV | Brute force matching of binary image feature descriptors is conventionally
performed using the Hamming distance. This paper assesses the use of
alternative metrics in order to see whether they can produce feature
correspondences that yield more accurate homography matrices. Two statistical
tests, namely ANOVA (Analysis... | computer science |
25,887 | Towards the Application of Linear Programming Methods For Multi-Camera
Pose Estimation | cs.CV | We presented a separation based optimization algorithm which, rather than
optimization the entire variables altogether, This would allow us to employ: 1)
a class of nonlinear functions with three variables and 2) a convex quadratic
multivariable polynomial, for minimization of reprojection error. Neglecting
the inversi... | computer science |
25,888 | Tracking Objects with Higher Order Interactions using Delayed Column
Generation | cs.CV | We study the problem of multi-target tracking and data association in video.
We formulate this in terms of selecting a subset of high-quality tracks subject
to the constraint that no pair of selected tracks is associated with a common
detection (of an object). This objective is equivalent to the classic NP-hard
problem... | computer science |
25,889 | Fine-grained Image Classification by Exploring Bipartite-Graph Labels | cs.CV | Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealin... | computer science |
25,890 | Embedding Label Structures for Fine-Grained Feature Representation | cs.CV | Recent algorithms in convolutional neural networks (CNN) considerably advance
the fine-grained image classification, which aims to differentiate subtle
differences among subordinate classes. However, previous studies have rarely
focused on learning a fined-grained and structured feature representation that
is able to l... | computer science |
25,891 | Video captioning with recurrent networks based on frame- and video-level
features and visual content classification | cs.CV | In this paper, we describe the system for generating textual descriptions of
short video clips using recurrent neural networks (RNN), which we used while
participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015.
Our work builds on static image captioning systems with RNN based language
models and ... | computer science |
25,892 | Minimally Supervised Feature Selection for Classification (Master's
Thesis, University Politehnica of Bucharest) | cs.CV | In the context of the highly increasing number of features that are available
nowadays we design a robust and fast method for feature selection. The method
tries to select the most representative features that are independent from each
other, but are strong together. We propose an algorithm that requires very
limited l... | computer science |
25,893 | Deep Learning Algorithms with Applications to Video Analytics for A
Smart City: A Survey | cs.CV | Deep learning has recently achieved very promising results in a wide range of
areas such as computer vision, speech recognition and natural language
processing. It aims to learn hierarchical representations of data by using deep
architecture models. In a smart city, a lot of data (e.g. videos captured from
many distrib... | computer science |
25,894 | Enhanced image feature coverage: Key-point selection using genetic
algorithms | cs.CV | Coverage of image features play an important role in many vision algorithms
since their distribution affect the estimated homography. This paper presents a
Genetic Algorithm (GA) in order to select the optimal set of features yielding
maximum coverage of the image which is measured by a robust method based on
spatial s... | computer science |
25,895 | 3D Reconstruction of Crime Scenes and Design Considerations for an
Interactive Investigation Tool | cs.CV | Crime Scene Investigation (CSI) is a carefully planned systematic process
with the purpose of acquiring physical evidences to shed light upon the
physical reality of the crime and eventually detect the identity of the
criminal. Capturing images and videos of the crime scene is an important part
of this process in order... | computer science |
25,896 | VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector
Products | cs.CV | VRFP is a real-time video retrieval framework based on short text input
queries, which obtains weakly labeled training images from the web after the
query is known. The retrieved web images representing the query and each
database video are treated as unordered collections of images, and each
collection is represented ... | computer science |
25,897 | Deep Residual Learning for Image Recognition | cs.CV | Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced... | computer science |
25,898 | Evaluation of Object Detection Proposals Under Condition Variations | cs.CV | Object detection is a fundamental task in many computer vision applications,
therefore the importance of evaluating the quality of object detection is well
acknowledged in this domain. This process gives insight into the capabilities
of methods in handling environmental changes. In this paper, a new method for
object d... | computer science |
25,899 | Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection | cs.CV | This paper introduces a fast algorithm for randomized computation of a
low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this
matrix to represent the development of a spatial grid through time e.g. data
from a static video source. DMD was originally introduced in the fluid
mechanics community, but... | computer science |
25,900 | Robust Dictionary based Data Representation | cs.CV | The robustness to noise and outliers is an important issue in linear
representation in real applications. We focus on the problem that samples are
grossly corrupted, which is also the 'sample specific' corruptions problem. A
reasonable assumption is that corrupted samples cannot be represented by the
dictionary while c... | computer science |
25,901 | Deep Feature Learning with Relative Distance Comparison for Person
Re-identification | cs.CV | Identifying the same individual across different scenes is an important yet
difficult task in intelligent video surveillance. Its main difficulty lies in
how to preserve similarity of the same person against large appearance and
structure variation while discriminating different individuals. In this paper,
we present a... | computer science |
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