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26,202 | Reduced Memory Region Based Deep Convolutional Neural Network Detection | cs.CV | Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convoluti... | computer science |
26,203 | Bottom-up Instance Segmentation using Deep Higher-Order CRFs | cs.CV | Traditional Scene Understanding problems such as Object Detection and
Semantic Segmentation have made breakthroughs in recent years due to the
adoption of deep learning. However, the former task is not able to localise
objects at a pixel level, and the latter task has no notion of different
instances of objects of the ... | computer science |
26,204 | Robust Structure from Motion in the Presence of Outliers and Missing
Data | cs.CV | Structure from motion is an import theme in computer vision. Although great
progress has been made both in theory and applications, most of the algorithms
only work for static scenes and rigid objects. In recent years, structure and
motion recovery of non-rigid objects and dynamic scenes have received a lot of
attentio... | computer science |
26,205 | An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles
using Axial Lumbar-Spine MRI | cs.CV | In this paper we present an interactive tool that can be used to quantify fat
infiltration in lumbar muscles, which is useful in studying fat infiltration
and lower back pain (LBP) in adults. Currently, a qualitative assessment by
visual grading via a 5-point scale is used to study fat infiltration in lumbar
muscles fr... | computer science |
26,206 | Image and Video Mining through Online Learning | cs.CV | Within the field of image and video recognition, the traditional approach is
a dataset split into fixed training and test partitions. However, the labelling
of the training set is time-consuming, especially as datasets grow in size and
complexity. Furthermore, this approach is not applicable to the home user, who
wants... | computer science |
26,207 | An empirical study on the effects of different types of noise in image
classification tasks | cs.CV | Image classification is one of the main research problems in computer vision
and machine learning. Since in most real-world image classification
applications there is no control over how the images are captured, it is
necessary to consider the possibility that these images might be affected by
noise (e.g. sensor noise ... | computer science |
26,208 | Automated detection of smuggled high-risk security threats using Deep
Learning | cs.CV | The security infrastructure is ill-equipped to detect and deter the smuggling
of non-explosive devices that enable terror attacks such as those recently
perpetrated in western Europe. The detection of so-called "small metallic
threats" (SMTs) in cargo containers currently relies on statistical risk
analysis, intelligen... | computer science |
26,209 | Track Facial Points in Unconstrained Videos | cs.CV | Tracking Facial Points in unconstrained videos is challenging due to the
non-rigid deformation that changes over time. In this paper, we propose to
exploit incremental learning for person-specific alignment in wild conditions.
Our approach takes advantage of part-based representation and cascade
regression for robust a... | computer science |
26,210 | Image Denoising Via Collaborative Support-Agnostic Recovery | cs.CV | In this paper, we propose a novel image denoising algorithm using
collaborative support-agnostic sparse reconstruction. An observed image is
first divided into patches. Similarly structured patches are grouped together
to be utilized for collaborative processing. In the proposed collaborative
schemes, similar patches a... | computer science |
26,211 | The Role of Context Selection in Object Detection | cs.CV | We investigate the reasons why context in object detection has limited
utility by isolating and evaluating the predictive power of different context
cues under ideal conditions in which context provided by an oracle. Based on
this study, we propose a region-based context re-scoring method with dynamic
context selection... | computer science |
26,212 | Rectifier Neural Network with a Dual-Pathway Architecture for Image
Denoising | cs.CV | Recently deep neural networks based on tanh activation function have shown
their impressive power in image denoising. In this letter, we try to use
rectifier function instead of tanh and propose a dual-pathway rectifier neural
network by combining two rectifier neurons with reversed input and output
weights in the same... | computer science |
26,213 | Sequential Deep Trajectory Descriptor for Action Recognition with
Three-stream CNN | cs.CV | Learning the spatial-temporal representation of motion information is crucial
to human action recognition. Nevertheless, most of the existing features or
descriptors cannot capture motion information effectively, especially for
long-term motion. To address this problem, this paper proposes a long-term
motion descriptor... | computer science |
26,214 | Style-Transfer via Texture-Synthesis | cs.CV | Style-transfer is a process of migrating a style from a given image to the
content of another, synthesizing a new image which is an artistic mixture of
the two. Recent work on this problem adopting Convolutional Neural-networks
(CNN) ignited a renewed interest in this field, due to the very impressive
results obtained.... | computer science |
26,215 | Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy
videos with predefined complexity | cs.CV | This paper examines the performance of a Spatial Pooler (SP) of a
Hierarchical Temporal Memory (HTM) in the task of noisy object recognition. To
address this challenge, a dedicated custom-designed system based on the SP,
histogram calculation module and SVM classifier was implemented. In addition to
implementing their ... | computer science |
26,216 | Learning Semantic Part-Based Models from Google Images | cs.CV | We propose a technique to train semantic part-based models of object classes
from Google Images. Our models encompass the appearance of parts and their
spatial arrangement on the object, specific to each viewpoint. We learn these
rich models by collecting training instances for both parts and objects, and
automatically... | computer science |
26,217 | Segmentation and Classification of Skin Lesions for Disease Diagnosis | cs.CV | In this paper, a novel approach for automatic segmentation and classification
of skin lesions is proposed. Initially, skin images are filtered to remove
unwanted hairs and noise and then the segmentation process is carried out to
extract lesion areas. For segmentation, a region growing method is applied by
automatic in... | computer science |
26,218 | Semi-Supervised Sparse Representation Based Classification for Face
Recognition with Insufficient Labeled Samples | cs.CV | This paper addresses the problem of face recognition when there is only few,
or even only a single, labeled examples of the face that we wish to recognize.
Moreover, these examples are typically corrupted by nuisance variables, both
linear (i.e., additive nuisance variables such as bad lighting, wearing of
glasses) and... | computer science |
26,219 | Image denoising via group sparsity residual constraint | cs.CV | Group sparsity has shown great potential in various low-level vision tasks
(e.g, image denoising, deblurring and inpainting). In this paper, we propose a
new prior model for image denoising via group sparsity residual constraint
(GSRC). To enhance the performance of group sparse-based image denoising, the
concept of gr... | computer science |
26,220 | FALCON: Feature Driven Selective Classification for Energy-Efficient
Image Recognition | cs.CV | Machine-learning algorithms have shown outstanding image recognition or
classification performance for computer vision applications. However, the
compute and energy requirement for implementing such classifier models for
large-scale problems is quite high. In this paper, we propose Feature Driven
Selective Classificati... | computer science |
26,221 | MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block
Size and Misalignment | cs.CV | In this letter, a very simple no-reference image quality assessment (NR-IQA)
model for JPEG compressed images is proposed. The proposed metric called median
of unique gradients (MUG) is based on the very simple facts of unique gradient
magnitudes of JPEG compressed images. MUG is a parameterless metric and does
not nee... | computer science |
26,222 | Hyperspectral Unmixing with Endmember Variability using Partial
Membership Latent Dirichlet Allocation | cs.CV | The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based ... | computer science |
26,223 | Fully-Trainable Deep Matching | cs.CV | Deep Matching (DM) is a popular high-quality method for quasi-dense image
matching. Despite its name, however, the original DM formulation does not yield
a deep neural network that can be trained end-to-end via backpropagation. In
this paper, we remove this limitation by rewriting the complete DM algorithm as
a convolu... | computer science |
26,224 | A Multi-Scale Cascade Fully Convolutional Network Face Detector | cs.CV | Face detection is challenging as faces in images could be present at
arbitrary locations and in different scales. We propose a three-stage cascade
structure based on fully convolutional neural networks (FCNs). It first
proposes the approximate locations where the faces may be, then aims to find
the accurate location by... | computer science |
26,225 | Generative Visual Manipulation on the Natural Image Manifold | cs.CV | Realistic image manipulation is challenging because it requires modifying the
image appearance in a user-controlled way, while preserving the realism of the
result. Unless the user has considerable artistic skill, it is easy to "fall
off" the manifold of natural images while editing. In this paper, we propose to
learn ... | computer science |
26,226 | Detecting Text in Natural Image with Connectionist Text Proposal Network | cs.CV | We propose a novel Connectionist Text Proposal Network (CTPN) that accurately
localizes text lines in natural image. The CTPN detects a text line in a
sequence of fine-scale text proposals directly in convolutional feature maps.
We develop a vertical anchor mechanism that jointly predicts location and
text/non-text sco... | computer science |
26,227 | Reliable Attribute-Based Object Recognition Using High Predictive Value
Classifiers | cs.CV | We consider the problem of object recognition in 3D using an ensemble of
attribute-based classifiers. We propose two new concepts to improve
classification in practical situations, and show their implementation in an
approach implemented for recognition from point-cloud data. First, the viewing
conditions can have a st... | computer science |
26,228 | DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for
Object Skeleton Extraction in Natural Images | cs.CV | Object skeletons are useful for object representation and object detection.
They are complementary to the object contour, and provide extra information,
such as how object scale (thickness) varies among object parts. But object
skeleton extraction from natural images is very challenging, because it
requires the extract... | computer science |
26,229 | Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking,
and Action Recognition on Lie Groups | cs.CV | Pose estimation, tracking, and action recognition of articulated objects from
depth images are important and challenging problems, which are normally
considered separately. In this paper, a unified paradigm based on Lie group
theory is proposed, which enables us to collectively address these related
problems. Our appro... | computer science |
26,230 | Towards Deep Compositional Networks | cs.CV | Hierarchical feature learning based on convolutional neural networks (CNN)
has recently shown significant potential in various computer vision tasks.
While allowing high-quality discriminative feature learning, the downside of
CNNs is the lack of explicit structure in features, which often leads to
overfitting, absence... | computer science |
26,231 | A Unified Gender-Aware Age Estimation | cs.CV | Human age estimation has attracted increasing researches due to its wide
applicability in such as security monitoring and advertisement recommendation.
Although a variety of methods have been proposed, most of them focus only on
the age-specific facial appearance. However, biological researches have shown
that not only... | computer science |
26,232 | Probabilistic Saliency Estimation | cs.CV | In this paper, we model the salient object detection problem under a
probabilistic framework encoding the boundary connectivity saliency cue and
smoothness constraints in an optimization problem. We show that this problem
has a closed form global optimum which estimates the salient object. We further
show that along wi... | computer science |
26,233 | Image Decomposition Using a Robust Regression Approach | cs.CV | This paper considers how to separate text and/or graphics from smooth
background in screen content and mixed content images and proposes an algorithm
to perform this segmentation task. The proposed methods make use of the fact
that the background in each block is usually smoothly varying and can be
modeled well by a li... | computer science |
26,234 | VIPLFaceNet: An Open Source Deep Face Recognition SDK | cs.CV | Robust face representation is imperative to highly accurate face recognition.
In this work, we propose an open source face recognition method with deep
representation named as VIPLFaceNet, which is a 10-layer deep convolutional
neural network with 7 convolutional layers and 3 fully-connected layers.
Compared with the w... | computer science |
26,235 | The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better | cs.CV | Accuracy, descriptor size, and the time required for extraction and matching
are all important factors when selecting local image descriptors. To optimize
over all these requirements, this paper presents a CUDA port for the recent
Learned Arrangement of Three Patches (LATCH) binary descriptors to the GPU
platform. The ... | computer science |
26,236 | Single-image RGB Photometric Stereo With Spatially-varying Albedo | cs.CV | We present a single-shot system to recover surface geometry of objects with
spatially-varying albedos, from images captured under a calibrated RGB
photometric stereo setup---with three light directions multiplexed across
different color channels in the observed RGB image. Since the problem is
ill-posed point-wise, we a... | computer science |
26,237 | Understanding Convolutional Neural Networks with A Mathematical Model | cs.CV | This work attempts to address two fundamental questions about the structure
of the convolutional neural networks (CNN): 1) why a non-linear activation
function is essential at the filter output of every convolutional layer? 2)
what is the advantage of the two-layer cascade system over the one-layer
system? A mathematic... | computer science |
26,238 | Joint Gender Classification and Age Estimation by Nearly Orthogonalizing
Their Semantic Spaces | cs.CV | In human face-based biometrics, gender classification and age estimation are
two typical learning tasks. Although a variety of approaches have been proposed
to handle them, just a few of them are solved jointly, even so, these joint
methods do not yet specifically concern the semantic difference between human
gender an... | computer science |
26,239 | ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised
Localization | cs.CV | We aim to localize objects in images using image-level supervision only.
Previous approaches to this problem mainly focus on discriminative object
regions and often fail to locate precise object boundaries. We address this
problem by introducing two types of context-aware guidance models, additive and
contrastive model... | computer science |
26,240 | Combining Texture and Shape Cues for Object Recognition With Minimal
Supervision | cs.CV | We present a novel approach to object classification and detection which
requires minimal supervision and which combines visual texture cues and shape
information learned from freely available unlabeled web search results. The
explosion of visual data on the web can potentially make visual examples of
almost any object... | computer science |
26,241 | Warped Convolutions: Efficient Invariance to Spatial Transformations | cs.CV | Convolutional Neural Networks (CNNs) are extremely efficient, since they
exploit the inherent translation-invariance of natural images. However,
translation is just one of a myriad of useful spatial transformations. Can the
same efficiency be attained when considering other spatial invariances? Such
generalized convolu... | computer science |
26,242 | 3D Face Reconstruction by Learning from Synthetic Data | cs.CV | Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of... | computer science |
26,243 | Learning Robust Features for Gait Recognition by Maximum Margin
Criterion | cs.CV | In the field of gait recognition from motion capture data, designing
human-interpretable gait features is a common practice of many fellow
researchers. To refrain from ad-hoc schemes and to find maximally
discriminative features we may need to explore beyond the limits of human
interpretability. This paper contributes ... | computer science |
26,244 | Perceptual Quality Prediction on Authentically Distorted Images Using a
Bag of Features Approach | cs.CV | Current top-performing blind perceptual image quality prediction models are
generally trained on legacy databases of human quality opinion scores on
synthetically distorted images. Therefore they learn image features that
effectively predict human visual quality judgments of inauthentic, and usually
isolated (single) d... | computer science |
26,245 | Transport-based analysis, modeling, and learning from signal and data
distributions | cs.CV | Transport-based techniques for signal and data analysis have received
increased attention recently. Given their abilities to provide accurate
generative models for signal intensities and other data distributions, they
have been used in a variety of applications including content-based retrieval,
cancer detection, image... | computer science |
26,246 | Visible Light-Based Human Visual System Conceptual Model | cs.CV | There is a widely held belief in the digital image and video processing
community, which is as follows: the Human Visual System (HVS) is more sensitive
to luminance (often confused with brightness) than photon energies (often
confused with chromaticity and chrominance). Passages similar to the following
occur with high... | computer science |
26,247 | Stamp processing with examplar features | cs.CV | Document digitization is becoming increasingly crucial. In this work, we
propose a shape based approach for automatic stamp verification/detection in
document images using an unsupervised feature learning. Given a small set of
training images, our algorithm learns an appropriate shape representation using
an unsupervis... | computer science |
26,248 | Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform | cs.CV | Using content-based binary codes to tag digital images has emerged as a
promising retrieval technology. Recently, Radon barcodes (RBCs) have been
introduced as a new binary descriptor for image search. RBCs are generated by
binarization of Radon projections and by assembling them into a vector, namely
the barcode. A si... | computer science |
26,249 | Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras | cs.CV | We propose a new technique for computing dense scene flow from two handheld
videos with wide camera baselines and different photometric properties due to
different sensors or camera settings like exposure and white balance. Our
technique innovates in two ways over existing methods: (1) it supports
independently moving ... | computer science |
26,250 | Radon-Gabor Barcodes for Medical Image Retrieval | cs.CV | In recent years, with the explosion of digital images on the Web,
content-based retrieval has emerged as a significant research area. Shapes,
textures, edges and segments may play a key role in describing the content of
an image. Radon and Gabor transforms are both powerful techniques that have
been widely studied to e... | computer science |
26,251 | Deep Impression: Audiovisual Deep Residual Networks for Multimodal
Apparent Personality Trait Recognition | cs.CV | Here, we develop an audiovisual deep residual network for multimodal apparent
personality trait recognition. The network is trained end-to-end for predicting
the Big Five personality traits of people from their videos. That is, the
network does not require any feature engineering or visual analysis such as
face detecti... | computer science |
26,252 | SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural
Networks | cs.CV | Ever more robust, accurate and detailed mapping using visual sensing has
proven to be an enabling factor for mobile robots across a wide variety of
applications. For the next level of robot intelligence and intuitive user
interaction, maps need extend beyond geometry and appearence - they need to
contain semantics. We ... | computer science |
26,253 | Target-driven Visual Navigation in Indoor Scenes using Deep
Reinforcement Learning | cs.CV | Two less addressed issues of deep reinforcement learning are (1) lack of
generalization capability to new target goals, and (2) data inefficiency i.e.,
the model requires several (and often costly) episodes of trial and error to
converge, which makes it impractical to be applied to real-world scenarios. In
this paper, ... | computer science |
26,254 | A convolutional approach to reflection symmetry | cs.CV | We present a convolutional approach to reflection symmetry detection in 2D.
Our model, built on the products of complex-valued wavelet convolutions,
simplifies previous edge-based pairwise methods. Being parameter-centered, as
opposed to feature-centered, it has certain computational advantages when the
object sizes ar... | computer science |
26,255 | GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for
Multimodal Information Fusion | cs.CV | Data generated from real world events are usually temporal and contain
multimodal information such as audio, visual, depth, sensor etc. which are
required to be intelligently combined for classification tasks. In this paper,
we propose a novel generalized deep neural network architecture where temporal
streams from mul... | computer science |
26,256 | Development of a Fuzzy Expert System based Liveliness Detection Scheme
for Biometric Authentication | cs.CV | Liveliness detection acts as a safe guard against spoofing attacks. Most of
the researchers used vision based techniques to detect liveliness of the user,
but they are highly sensitive to illumination effects. Therefore it is very
hard to design a system, which will work robustly under all circumstances.
Literature sho... | computer science |
26,257 | Deep Kinematic Pose Regression | cs.CV | Learning articulated object pose is inherently difficult because the pose is
high dimensional but has many structural constraints. Most existing work do not
model such constraints and does not guarantee the geometric validity of their
pose estimation, therefore requiring a post-processing to recover the correct
geometr... | computer science |
26,258 | Pose from Action: Unsupervised Learning of Pose Features based on Motion | cs.CV | Human actions are comprised of a sequence of poses. This makes videos of
humans a rich and dense source of human poses. We propose an unsupervised
method to learn pose features from videos that exploits a signal which is
complementary to appearance and can be used as supervision: motion. The key
idea is that humans go ... | computer science |
26,259 | Learning camera viewpoint using CNN to improve 3D body pose estimation | cs.CV | The objective of this work is to estimate 3D human pose from a single RGB
image. Extracting image representations which incorporate both spatial relation
of body parts and their relative depth plays an essential role in accurate3D
pose reconstruction. In this paper, for the first time, we show that camera
viewpoint in ... | computer science |
26,260 | Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective
Dataset of Paintings | cs.CV | Computational aesthetics is an emerging field of research which has attracted
different research groups in the last few years. In this field, one of the main
approaches to evaluate the aesthetic quality of paintings and photographs is a
feature-based approach. Among the different features proposed to reach this
goal, c... | computer science |
26,261 | Fast Single Shot Detection and Pose Estimation | cs.CV | For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide... | computer science |
26,262 | Coarse-to-fine Surgical Instrument Detection for Cataract Surgery
Monitoring | cs.CV | The amount of surgical data, recorded during video-monitored surgeries, has
extremely increased. This paper aims at improving existing solutions for the
automated analysis of cataract surgeries in real time. Through the analysis of
a video recording the operating table, it is possible to know which instruments
exit or ... | computer science |
26,263 | Multi-Residual Networks: Improving the Speed and Accuracy of Residual
Networks | cs.CV | In this article, we take one step toward understanding the learning behavior
of deep residual networks, and supporting the observation that deep residual
networks behave like ensembles. We propose a new convolutional neural network
architecture which builds upon the success of residual networks by explicitly
exploiting... | computer science |
26,264 | A scalable convolutional neural network for task-specified scenarios via
knowledge distillation | cs.CV | In this paper, we explore the redundancy in convolutional neural network,
which scales with the complexity of vision tasks. Considering that many
front-end visual systems are interested in only a limited range of visual
targets, the removing of task-specified network redundancy can promote a wide
range of potential app... | computer science |
26,265 | Poisson Noise Reduction with Higher-order Natural Image Prior Model | cs.CV | Poisson denoising is an essential issue for various imaging applications,
such as night vision, medical imaging and microscopy. State-of-the-art
approaches are clearly dominated by patch-based non-local methods in recent
years. In this paper, we aim to propose a local Poisson denoising model with
both structure simplic... | computer science |
26,266 | Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images
towards Achieving Label-free Angiography | cs.CV | Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribu... | computer science |
26,267 | Contextual Relationship-based Activity Segmentation on an Event Stream
in the IoT Environment with Multi-user Activities | cs.CV | The human activity recognition in the IoT environment plays the central role
in the ambient assisted living, where the human activities can be represented
as a concatenated event stream generated from various smart objects. From the
concatenated event stream, each activity should be distinguished separately for
the hum... | computer science |
26,268 | Adaptive Decontamination of the Training Set: A Unified Formulation for
Discriminative Visual Tracking | cs.CV | Tracking-by-detection methods have demonstrated competitive performance in
recent years. In these approaches, the tracking model heavily relies on the
quality of the training set. Due to the limited amount of labeled training
data, additional samples need to be extracted and labeled by the tracker
itself. This often le... | computer science |
26,269 | Discriminative Scale Space Tracking | cs.CV | Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This pap... | computer science |
26,270 | Transfer Learning for Material Classification using Convolutional
Networks | cs.CV | Material classification in natural settings is a challenge due to complex
interplay of geometry, reflectance properties, and illumination. Previous work
on material classification relies strongly on hand-engineered features of
visual samples. In this work we use a Convolutional Neural Network (convnet)
that learns desc... | computer science |
26,271 | Hands-Free Segmentation of Medical Volumes via Binary Inputs | cs.CV | We propose a novel hands-free method to interactively segment 3D medical
volumes. In our scenario, a human user progressively segments an organ by
answering a series of questions of the form "Is this voxel inside the object to
segment?". At each iteration, the chosen question is defined as the one halving
a set of cand... | computer science |
26,272 | GAdaBoost: Accelerating Adaboost Feature Selection with Genetic
Algorithms | cs.CV | Boosted cascade of simple features, by Viola and Jones, is one of the most
famous object detection frameworks. However, it suffers from a lengthy training
process. This is due to the vast features space and the exhaustive search
nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to
accelerate t... | computer science |
26,273 | Automated Visual Fin Identification of Individual Great White Sharks | cs.CV | This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fu... | computer science |
26,274 | Markov Random Field Model-Based Salt and Pepper Noise Removal | cs.CV | Problem of impulse noise reduction is a very well studied problem in image
processing community and many different approaches have been proposed to tackle
this problem. In the current work, the problem of fixed value impulse noise
(salt and pepper) removal from images is investigated by use of a Markov Random
Field (MR... | computer science |
26,275 | Robust Estimation of Multiple Inlier Structures | cs.CV | The robust estimator presented in this paper processes each structure
independently. The scales of the structures are estimated adaptively and no
threshold is involved in spite of different objective functions. The user has
to specify only the number of elemental subsets for random sampling. After
classifying all the i... | computer science |
26,276 | Matrix Variate RBM Model with Gaussian Distributions | cs.CV | Restricted Boltzmann Machine (RBM) is a particular type of random neural
network models modeling vector data based on the assumption of Bernoulli
distribution. For multi-dimensional and non-binary data, it is necessary to
vectorize and discretize the information in order to apply the conventional
RBM. It is well-known ... | computer science |
26,277 | From Facial Expression Recognition to Interpersonal Relation Prediction | cs.CV | Interpersonal relation defines the association, e.g., warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterized and quantified from face images in the wild. We address this
challenging proble... | computer science |
26,278 | Partial Least Squares Regression on Riemannian Manifolds and Its
Application in Classifications | cs.CV | Partial least squares regression (PLSR) has been a popular technique to
explore the linear relationship between two datasets. However, most of
algorithm implementations of PLSR may only achieve a suboptimal solution
through an optimization on the Euclidean space. In this paper, we propose
several novel PLSR models on R... | computer science |
26,279 | Detecting facial landmarks in the video based on a hybrid framework | cs.CV | To dynamically detect the facial landmarks in the video, we propose a novel
hybrid framework termed as detection-tracking-detection (DTD). First, the face
bounding box is achieved from the first frame of the video sequence based on a
traditional face detection method. Then, a landmark detector detects the facial
landma... | computer science |
26,280 | Multi-View Constraint Propagation with Consensus Prior Knowledge | cs.CV | In many applications, the pairwise constraint is a kind of weaker supervisory
information which can be collected easily. The constraint propagation has been
proved to be a success of exploiting such side-information. In recent years,
some methods of multi-view constraint propagation have been proposed. However,
the pro... | computer science |
26,281 | Image Denoising via Multi-scale Nonlinear Diffusion Models | cs.CV | Image denoising is a fundamental operation in image processing and holds
considerable practical importance for various real-world applications. Arguably
several thousands of papers are dedicated to image denoising. In the past
decade, sate-of-the-art denoising algorithm have been clearly dominated by
non-local patch-ba... | computer science |
26,282 | FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression
Recognition | cs.CV | Relatively small data sets available for expression recognition research make
the training of deep networks for expression recognition very challenging.
Although fine-tuning can partially alleviate the issue, the performance is
still below acceptable levels as the deep features probably contain redun- dant
information ... | computer science |
26,283 | Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning
Challenge | cs.CV | Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation... | computer science |
26,284 | Characterization of Lung Nodule Malignancy using Hybrid Shape and
Appearance Features | cs.CV | Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagn... | computer science |
26,285 | Fast and reliable stereopsis measurement at multiple distances with iPad | cs.CV | Purpose: To present a new fast and reliable application for iPad (ST) for
screening stereopsis at multiple distances.
Methods: A new iPad application (app) based on a random dot stereogram was
designed for screening stereopsis at multiple distances. Sixty-five subjects
with no ocular diseases and wearing their habitu... | computer science |
26,286 | How should we evaluate supervised hashing? | cs.CV | Hashing produces compact representations for documents, to perform tasks like
classification or retrieval based on these short codes. When hashing is
supervised, the codes are trained using labels on the training data. This paper
first shows that the evaluation protocols used in the literature for supervised
hashing ar... | computer science |
26,287 | How Useful is Region-based Classification of Remote Sensing Images in a
Deep Learning Framework? | cs.CV | In this paper, we investigate the impact of segmentation algorithms as a
preprocessing step for classification of remote sensing images in a deep
learning framework. Especially, we address the issue of segmenting the image
into regions to be classified using pre-trained deep neural networks as feature
extractors for an... | computer science |
26,288 | Distributed Training of Deep Neural Networks: Theoretical and Practical
Limits of Parallel Scalability | cs.CV | This paper presents a theoretical analysis and practical evaluation of the
main bottlenecks towards a scalable distributed solution for the training of
Deep Neuronal Networks (DNNs). The presented results show, that the current
state of the art approach, using data-parallelized Stochastic Gradient Descent
(SGD), is qui... | computer science |
26,289 | Realtime Hierarchical Clustering based on Boundary and Surface
Statistics | cs.CV | Visual grouping is a key mechanism in human scene perception. There, it
belongs to the subconscious, early processing and is key prerequisite for other
high level tasks such as recognition. In this paper, we introduce an efficient,
realtime capable algorithm which likewise agglomerates a valuable hierarchical
clusterin... | computer science |
26,290 | A quantitative analysis of tilt in the Café Wall illusion: a
bioplausible model for foveal and peripheral vision | cs.CV | The biological characteristics of human visual processing can be investigated
through the study of optical illusions and their perception, giving rise to
intuitions that may improve computer vision to match human performance.
Geometric illusions are a specific subfamily in which orientations and angles
are misperceived... | computer science |
26,291 | Walker-Independent Features for Gait Recognition from Motion Capture
Data | cs.CV | MoCap-based human identification, as a pattern recognition discipline, can be
optimized using a machine learning approach. Yet in some applications such as
video surveillance new identities can appear on the fly and labeled data for
all encountered people may not always be available. This work introduces the
concept of... | computer science |
26,292 | Is the deconvolution layer the same as a convolutional layer? | cs.CV | In this note, we want to focus on aspects related to two questions most
people asked us at CVPR about the network we presented. Firstly, What is the
relationship between our proposed layer and the deconvolution layer? And
secondly, why are convolutions in low-resolution (LR) space a better choice?
These are key questio... | computer science |
26,293 | EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm
Based on kNN Graph | cs.CV | Approximate nearest neighbor (ANN) search is a fundamental problem in many
areas of data mining, machine learning and computer vision. The performance of
traditional hierarchical structure (tree) based methods decreases as the
dimensionality of data grows, while hashing based methods usually lack
efficiency in practice... | computer science |
26,294 | Funnel-Structured Cascade for Multi-View Face Detection with
Alignment-Awareness | cs.CV | Multi-view face detection in open environment is a challenging task due to
diverse variations of face appearances and shapes. Most multi-view face
detectors depend on multiple models and organize them in parallel, pyramid or
tree structure, which compromise between the accuracy and time-cost. Aiming at
a more favorable... | computer science |
26,295 | EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras | cs.CV | Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor s... | computer science |
26,296 | Example-Based Image Synthesis via Randomized Patch-Matching | cs.CV | Image and texture synthesis is a challenging task that has long been drawing
attention in the fields of image processing, graphics, and machine learning.
This problem consists of modelling the desired type of images, either through
training examples or via a parametric modeling, and then generating images that
belong t... | computer science |
26,297 | The face-space duality hypothesis: a computational model | cs.CV | Valentine's face-space suggests that faces are represented in a psychological
multidimensional space according to their perceived properties. However, the
proposed framework was initially designed as an account of invariant facial
features only, and explanations for dynamic features representation were
neglected. In th... | computer science |
26,298 | Real-time Human Pose Estimation from Video with Convolutional Neural
Networks | cs.CV | In this paper, we present a method for real-time multi-person human pose
estimation from video by utilizing convolutional neural networks. Our method is
aimed for use case specific applications, where good accuracy is essential and
variation of the background and poses is limited. This enables us to use a
generic netwo... | computer science |
26,299 | DimensionApp : android app to estimate object dimensions | cs.CV | In this project, we develop an android app that uses on computer vision
techniques to estimate an object dimension present in field of view. The app
while having compact size, is accurate upto +/- 5 mm and robust towards touch
inputs. We use single-view metrology to compute accurate measurement. Unlike
previous approac... | computer science |
26,300 | Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for
Multi-feature Image Retrieval: A Manifold Aspect | cs.CV | Single feature is inefficient to describe content of an image, which is a
shortcoming in traditional image retrieval task. We know that one image can be
described by different features. Multi-feature fusion ranking can be utilized
to improve the ranking list of query. In this paper, we first analyze graph
structure and... | computer science |
26,301 | Perceptual uniform descriptor and Ranking on manifold: A bridge between
image representation and ranking for image retrieval | cs.CV | Incompatibility of image descriptor and ranking is always neglected in image
retrieval. In this paper, manifold learning and Gestalt psychology theory are
involved to solve the incompatibility problem. A new holistic descriptor called
Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed,
which co... | computer science |
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