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26,002 | Learning Joint Representations of Videos and Sentences with Web Image
Search | cs.CV | Our objective is video retrieval based on natural language queries. In
addition, we consider the analogous problem of retrieving sentences or
generating descriptions given an input video. Recent work has addressed the
problem by embedding visual and textual inputs into a common space where
semantic similarities correla... | computer science |
26,003 | A combined Approach Based on Fuzzy Classification and Contextual Region
Growing to Image Segmentation | cs.CV | We present in this paper an image segmentation approach that combines a fuzzy
semantic region classification and a context based region-growing. Input image
is first over-segmented. Then, prior domain knowledge is used to perform a
fuzzy classification of these regions to provide a fuzzy semantic labeling.
This allows ... | computer science |
26,004 | Comparative study and enhancement of Camera Tampering Detection
algorithms | cs.CV | Recently the use of video surveillance systems is widely increasing.
Different places are equipped by camera surveillances such as hospitals,
schools, airports, museums and military places in order to ensure the safety
and security of the persons and their property. Therefore it becomes
significant to guarantee the pro... | computer science |
26,005 | Database of handwritten Arabic mathematical formulas images | cs.CV | Although publicly available, ground-truthed database have proven useful for
training, evaluating, and comparing recognition systems in many domains, the
availability of such database for handwritten Arabic mathematical formula
recognition in particular, is currently quite poor. In this paper, we present a
new public da... | computer science |
26,006 | End-to-End Localization and Ranking for Relative Attributes | cs.CV | We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network disc... | computer science |
26,007 | Convolutional Oriented Boundaries | cs.CV | We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a nove... | computer science |
26,008 | Deep Convolution Networks for Compression Artifacts Reduction | cs.CV | Lossy compression introduces complex compression artifacts, particularly
blocking artifacts, ringing effects and blurring. Existing algorithms either
focus on removing blocking artifacts and produce blurred output, or restore
sharpened images that are accompanied with ringing effects. Inspired by the
success of deep co... | computer science |
26,009 | Camera Pose Estimation from Lines using Plücker Coordinates | cs.CV | Correspondences between 3D lines and their 2D images captured by a camera are
often used to determine position and orientation of the camera in space. In
this work, we propose a novel algebraic algorithm to estimate the camera pose.
We parameterize 3D lines using Pl\"ucker coordinates that allow linear
projection of th... | computer science |
26,010 | Residual Networks of Residual Networks: Multilevel Residual Networks | cs.CV | A residual-networks family with hundreds or even thousands of layers
dominates major image recognition tasks, but building a network by simply
stacking residual blocks inevitably limits its optimization ability. This paper
proposes a novel residual-network architecture, Residual networks of Residual
networks (RoR), to ... | computer science |
26,011 | Deep Convolutional Neural Networks for Microscopy-Based Point of Care
Diagnostics | cs.CV | Point of care diagnostics using microscopy and computer vision methods have
been applied to a number of practical problems, and are particularly relevant
to low-income, high disease burden areas. However, this is subject to the
limitations in sensitivity and specificity of the computer vision methods used.
In general, ... | computer science |
26,012 | Fashion Landmark Detection in the Wild | cs.CV | Visual fashion analysis has attracted many attentions in the recent years.
Previous work represented clothing regions by either bounding boxes or human
joints. This work presents fashion landmark detection or fashion alignment,
which is to predict the positions of functional key points defined on the
fashion items, suc... | computer science |
26,013 | Object Detection, Tracking, and Motion Segmentation for Object-level
Video Segmentation | cs.CV | We present an approach for object segmentation in videos that combines
frame-level object detection with concepts from object tracking and motion
segmentation. The approach extracts temporally consistent object tubes based on
an off-the-shelf detector. Besides the class label for each tube, this provides
a location pri... | computer science |
26,014 | 3D Human Pose Estimation Using Convolutional Neural Networks with 2D
Pose Information | cs.CV | While there has been a success in 2D human pose estimation with convolutional
neural networks (CNNs), 3D human pose estimation has not been thoroughly
studied. In this paper, we tackle the 3D human pose estimation task with
end-to-end learning using CNNs. Relative 3D positions between one joint and the
other joints are... | computer science |
26,015 | DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns | cs.CV | The recognition of human actions and the determination of human attributes
are two tasks that call for fine-grained classification. Indeed, often rather
small and inconspicuous objects and features have to be detected to tell their
classes apart. In order to deal with this challenge, we propose a novel
convolutional ne... | computer science |
26,016 | Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into
Medical Image Segmentation | cs.CV | This study introduced a novel system, called Gaze2Segment, integrating
biological and computer vision techniques to support radiologists' reading
experience with an automatic image segmentation task. During diagnostic
assessment of lung CT scans, the radiologists' gaze information were used to
create a visual attention... | computer science |
26,017 | Fractional Calculus In Image Processing: A Review | cs.CV | Over the last decade, it has been demonstrated that many systems in science
and engineering can be modeled more accurately by fractional-order than
integer-order derivatives, and many methods are developed to solve the problem
of fractional systems. Due to the extra free parameter order, fractional-order
based methods ... | computer science |
26,018 | Approximate search with quantized sparse representations | cs.CV | This paper tackles the task of storing a large collection of vectors, such as
visual descriptors, and of searching in it. To this end, we propose to
approximate database vectors by constrained sparse coding, where possible atom
weights are restricted to belong to a finite subset. This formulation
encompasses, as partic... | computer science |
26,019 | Enabling My Robot To Play Pictionary : Recurrent Neural Networks For
Sketch Recognition | cs.CV | Freehand sketching is an inherently sequential process. Yet, most approaches
for hand-drawn sketch recognition either ignore this sequential aspect or
exploit it in an ad-hoc manner. In our work, we propose a recurrent neural
network architecture for sketch object recognition which exploits the long-term
sequential and... | computer science |
26,020 | Automatic text extraction and character segmentation using maximally
stable extremal regions | cs.CV | Text detection and segmentation is an important prerequisite for many content
based image analysis tasks. The paper proposes a novel text extraction and
character segmentation algorithm using Maximally Stable Extremal Regions as
basic letter candidates. These regions are then subjected to thresholding and
thereafter va... | computer science |
26,021 | Solving Visual Madlibs with Multiple Cues | cs.CV | This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach ... | computer science |
26,022 | Recurrent Neural Networks to Correct Satellite Image Classification Maps | cs.CV | While initially devised for image categorization, convolutional neural
networks (CNNs) are being increasingly used for the pixelwise semantic labeling
of images. However, the proper nature of the most common CNN architectures
makes them good at recognizing but poor at localizing objects precisely. This
problem is magni... | computer science |
26,023 | Learning Dynamic Hierarchical Models for Anytime Scene Labeling | cs.CV | With increasing demand for efficient image and video analysis, test-time cost
of scene parsing becomes critical for many large-scale or time-sensitive vision
applications. We propose a dynamic hierarchical model for anytime scene
labeling that allows us to achieve flexible trade-offs between efficiency and
accuracy in ... | computer science |
26,024 | Clockwork Convnets for Video Semantic Segmentation | cs.CV | Recent years have seen tremendous progress in still-image segmentation;
however the na\"ive application of these state-of-the-art algorithms to every
video frame requires considerable computation and ignores the temporal
continuity inherent in video. We propose a video recognition framework that
relies on two key obser... | computer science |
26,025 | Automatic detection of moving objects in video surveillance | cs.CV | This work is in the field of video surveillance including motion detection.
The video surveillance is one of essential techniques for automatic video
analysis to extract crucial information or relevant scenes in video
surveillance systems. The aim of our work is to propose solutions for the
automatic detection of movin... | computer science |
26,026 | Deep Hashing: A Joint Approach for Image Signature Learning | cs.CV | Similarity-based image hashing represents crucial technique for visual data
storage reduction and expedited image search. Conventional hashing schemes
typically feed hand-crafted features into hash functions, which separates the
procedures of feature extraction and hash function learning. In this paper, we
propose a no... | computer science |
26,027 | Reasoning and Algorithm Selection Augmented Symbolic Segmentation | cs.CV | In this paper we present an alternative method to symbolic segmentation: we
approach symbolic segmentation as an algorithm selection problem. That is, let
there be a set A of available algorithms for symbolic segmentation, a set of
input features $F$, a set of image attribute $\mathbb{A}$ and a selection
mechanism $S(F... | computer science |
26,028 | Self-paced Learning for Weakly Supervised Evidence Discovery in
Multimedia Event Search | cs.CV | Multimedia event detection has been receiving increasing attention in recent
years. Besides recognizing an event, the discovery of evidences (which is
refered to as "recounting") is also crucial for user to better understand the
searching result. Due to the difficulty of evidence annotation, only limited
supervision of... | computer science |
26,029 | Beyond Correlation Filters: Learning Continuous Convolution Operators
for Visual Tracking | cs.CV | Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-reso... | computer science |
26,030 | DeepDiary: Automatic Caption Generation for Lifelogging Image Streams | cs.CV | Lifelogging cameras capture everyday life from a first-person perspective,
but generate so much data that it is hard for users to browse and organize
their image collections effectively. In this paper, we propose to use automatic
image captioning algorithms to generate textual representations of these
collections. We d... | computer science |
26,031 | On Minimal Accuracy Algorithm Selection in Computer Vision and
Intelligent Systems | cs.CV | In this paper we discuss certain theoretical properties of algorithm
selection approach to image processing and to intelligent system in general. We
analyze the theoretical limits of algorithm selection with respect to the
algorithm selection accuracy. We show the theoretical formulation of a crisp
bound on the algorit... | computer science |
26,032 | Temporal Registration in In-Utero Volumetric MRI Time Series | cs.CV | We present a robust method to correct for motion and deformations for
in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI
requires robust alignment across time in the presence of substantial and
unpredictable motion. We make a Markov assumption on the nature of deformations
to take advantage of... | computer science |
26,033 | When was that made? | cs.CV | In this paper, we explore deep learning methods for estimating when objects
were made. Automatic methods for this task could potentially be useful for
historians, collectors, or any individual interested in estimating when their
artifact was created. Direct applications include large-scale data organization
or retrieva... | computer science |
26,034 | Human Pose Estimation from Depth Images via Inference Embedded
Multi-task Learning | cs.CV | Human pose estimation (i.e., locating the body parts / joints of a person) is
a fundamental problem in human-computer interaction and multimedia
applications. Significant progress has been made based on the development of
depth sensors, i.e., accessible human pose prediction from still depth images
[32]. However, most ... | computer science |
26,035 | Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image
Denoising | cs.CV | Discriminative model learning for image denoising has been recently
attracting considerable attentions due to its favorable denoising performance.
In this paper, we take one step forward by investigating the construction of
feed-forward denoising convolutional neural networks (DnCNNs) to embrace the
progress in very de... | computer science |
26,036 | Branching Gaussian Processes with Applications to Spatiotemporal
Reconstruction of 3D Trees | cs.CV | We propose a robust method for estimating dynamic 3D curvilinear branching
structure from monocular images. While 3D reconstruction from images has been
widely studied, estimating thin structure has received less attention. This
problem becomes more challenging in the presence of camera error, scene motion,
and a const... | computer science |
26,037 | SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy
Image Segmentation | cs.CV | Region-based methods have proven necessary for improving segmentation
accuracy of neuronal structures in electron microscopy (EM) images. Most
region-based segmentation methods use a scoring function to determine region
merging. Such functions are usually learned with supervised algorithms that
demand considerable grou... | computer science |
26,038 | About Pyramid Structure in Convolutional Neural Networks | cs.CV | Deep convolutional neural networks (CNN) brought revolution without any doubt
to various challenging tasks, mainly in computer vision. However, their model
designing still requires attention to reduce number of learnable parameters,
with no meaningful reduction in performance. In this paper we investigate to
what exten... | computer science |
26,039 | The Importance of Skip Connections in Biomedical Image Segmentation | cs.CV | In this paper, we study the influence of both long and short skip connections
on Fully Convolutional Networks (FCN) for biomedical image segmentation. In
standard FCNs, only long skip connections are used to skip features from the
contracting path to the expanding path in order to recover spatial information
lost durin... | computer science |
26,040 | Every Filter Extracts A Specific Texture In Convolutional Neural
Networks | cs.CV | Many works have concentrated on visualizing and understanding the inner
mechanism of convolutional neural networks (CNNs) by generating images that
activate some specific neurons, which is called deep visualization. However, it
is still unclear what the filters extract from images intuitively. In this
paper, we propose... | computer science |
26,041 | Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field | cs.CV | Occlusion is one of the most challenging problems in depth estimation.
Previous work has modeled the single-occluder occlusion in light field and get
good results, however it is still difficult to obtain accurate depth for
multi-occluder occlusion. In this paper, we explore the multi-occluder
occlusion model in light f... | computer science |
26,042 | Face Alignment In-the-Wild: A Survey | cs.CV | Over the last two decades, face alignment or localizing fiducial facial
points has received increasing attention owing to its comprehensive
applications in automatic face analysis. However, such a task has proven
extremely challenging in unconstrained environments due to many confounding
factors, such as pose, occlusio... | computer science |
26,043 | Cross Euclidean-to-Riemannian Metric Learning with Application to Face
Recognition from Video | cs.CV | Riemannian manifolds have been widely employed for video representations in
visual classification tasks including video-based face recognition. The success
mainly derives from learning a discriminant Riemannian metric which encodes the
non-linear geometry of the underlying Riemannian manifolds. In this paper, we
propos... | computer science |
26,044 | Generating Synthetic Data for Text Recognition | cs.CV | Generating synthetic images is an art which emulates the natural process of
image generation in a closest possible manner. In this work, we exploit such a
framework for data generation in handwritten domain. We render synthetic data
using open source fonts and incorporate data augmentation schemes. As part of
this work... | computer science |
26,045 | A Riemannian Network for SPD Matrix Learning | cs.CV | Symmetric Positive Definite (SPD) matrix learning methods have become popular
in many image and video processing tasks, thanks to their ability to learn
appropriate statistical representations while respecting Riemannian geometry of
underlying SPD manifolds. In this paper we build a Riemannian network
architecture to o... | computer science |
26,046 | Visual place recognition using landmark distribution descriptors | cs.CV | Recent work by Suenderhauf et al. [1] demonstrated improved visual place
recognition using proposal regions coupled with features from convolutional
neural networks (CNN) to match landmarks between views. In this work we extend
the approach by introducing descriptors built from landmark features which also
encode the s... | computer science |
26,047 | Transitive Hashing Network for Heterogeneous Multimedia Retrieval | cs.CV | Hashing has been widely applied to large-scale multimedia retrieval due to
the storage and retrieval efficiency. Cross-modal hashing enables efficient
retrieval from database of one modality in response to a query of another
modality. Existing work on cross-modal hashing assumes heterogeneous
relationship across modali... | computer science |
26,048 | Weakly Supervised Object Localization Using Size Estimates | cs.CV | We present a technique for weakly supervised object localization (WSOL),
building on the observation that WSOL algorithms usually work better on images
with bigger objects. Instead of training the object detector on the entire
training set at the same time, we propose a curriculum learning strategy to
feed training ima... | computer science |
26,049 | Design of Efficient Convolutional Layers using Single Intra-channel
Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure | cs.CV | Deep convolutional neural networks achieve remarkable visual recognition
performance, at the cost of high computational complexity. In this paper, we
have a new design of efficient convolutional layers based on three schemes. The
3D convolution operation in a convolutional layer can be considered as
performing spatial ... | computer science |
26,050 | Depth2Action: Exploring Embedded Depth for Large-Scale Action
Recognition | cs.CV | This paper performs the first investigation into depth for large-scale human
action recognition in video where the depth cues are estimated from the videos
themselves. We develop a new framework called depth2action and experiment
thoroughly into how best to incorporate the depth information. We introduce
spatio-tempora... | computer science |
26,051 | Intrinsic Light Field Images | cs.CV | We present a method to automatically decompose a light field into its
intrinsic shading and albedo components. Contrary to previous work targeted to
2D single images and videos, a light field is a 4D structure that captures
non-integrated incoming radiance over a discrete angular domain. This higher
dimensionality of t... | computer science |
26,052 | SenTion: A framework for Sensing Facial Expressions | cs.CV | Facial expressions are an integral part of human cognition and communication,
and can be applied in various real life applications. A vital precursor to
accurate expression recognition is feature extraction. In this paper, we
propose SenTion: A framework for sensing facial expressions. We propose a novel
person indepen... | computer science |
26,053 | Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras
Calibration | cs.CV | The plenoptic camera can capture both angular and spatial information of the
rays, enabling 3D reconstruction by single exposure. The geometry of the
recovered scene structure is affected by the calibration of the plenoptic
camera significantly. In this paper, we propose a novel unconstrained
two-parallel-plane (TPP) m... | computer science |
26,054 | A Comparative Study for the Weighted Nuclear Norm Minimization and
Nuclear Norm Minimization | cs.CV | Nuclear norm minimization (NNM) tends to over-shrink the rank components and
treats the different rank components equally, thus limits its capability and
flexibility. Recent studies have shown that the weighted nuclear norm
minimization (WNNM) is expected to be more accurate than NNM. However, it still
lacks a plausibl... | computer science |
26,055 | Temporally Consistent Motion Segmentation from RGB-D Video | cs.CV | We present a method for temporally consistent motion segmentation from RGB-D
videos assuming a piecewise rigid motion model. We formulate global energies
over entire RGB-D sequences in terms of the segmentation of each frame into a
number of objects, and the rigid motion of each object through the sequence. We
develop ... | computer science |
26,056 | Parameterized Principal Component Analysis | cs.CV | When modeling multivariate data, one might have an extra parameter of
contextual information that could be used to treat some observations as more
similar to others. For example, images of faces can vary by age, and one would
expect the face of a 40 year old to be more similar to the face of a 30 year
old than to a bab... | computer science |
26,057 | Geometry-aware Similarity Learning on SPD Manifolds for Visual
Recognition | cs.CV | Symmetric Positive Definite (SPD) matrices have been widely used for data
representation in many visual recognition tasks. The success mainly attributes
to learning discriminative SPD matrices with encoding the Riemannian geometry
of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD
similarity... | computer science |
26,058 | Frame- and Segment-Level Features and Candidate Pool Evaluation for
Video Caption Generation | cs.CV | We present our submission to the Microsoft Video to Language Challenge of
generating short captions describing videos in the challenge dataset. Our model
is based on the encoder--decoder pipeline, popular in image and video
captioning systems. We propose to utilize two different kinds of video
features, one to capture ... | computer science |
26,059 | Large Angle based Skeleton Extraction for 3D Animation | cs.CV | In this paper, we present a solution for arbitrary 3D character deformation
by investigating rotation angle of decomposition and preserving the mesh
topology structure. In computer graphics, skeleton extraction and
skeleton-driven animation is an active areas and gains increasing interests
from researchers. The accurac... | computer science |
26,060 | Scene Labeling Through Knowledge-Based Rules Employing Constrained
Integer Linear Programing | cs.CV | Scene labeling task is to segment the image into meaningful regions and
categorize them into classes of objects which comprised the image. Commonly
used methods typically find the local features for each segment and label them
using classifiers. Afterward, labeling is smoothed in order to make sure that
neighboring reg... | computer science |
26,061 | IM2CAD | cs.CV | Given a single photo of a room and a large database of furniture CAD models,
our goal is to reconstruct a scene that is as similar as possible to the scene
depicted in the photograph, and composed of objects drawn from the database. We
present a completely automatic system to address this IM2CAD problem that
produces h... | computer science |
26,062 | A Systematic Approach for Cross-source Point Cloud Registration by
Preserving Macro and Micro Structures | cs.CV | We propose a systematic approach for registering cross-source point clouds.
The compelling need for cross-source point cloud registration is motivated by
the rapid development of a variety of 3D sensing techniques, but many existing
registration methods face critical challenges as a result of the large
variations in cr... | computer science |
26,063 | Full Resolution Image Compression with Recurrent Neural Networks | cs.CV | This paper presents a set of full-resolution lossy image compression methods
based on neural networks. Each of the architectures we describe can provide
variable compression rates during deployment without requiring retraining of
the network: each network need only be trained once. All of our architectures
consist of a... | computer science |
26,064 | Multi-stage Object Detection with Group Recursive Learning | cs.CV | Most of existing detection pipelines treat object proposals independently and
predict bounding box locations and classification scores over them separately.
However, the important semantic and spatial layout correlations among proposals
are often ignored, which are actually useful for more accurate object
detection. In... | computer science |
26,065 | AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene
Classification | cs.CV | Aerial scene classification, which aims to automatically label an aerial
image with a specific semantic category, is a fundamental problem for
understanding high-resolution remote sensing imagery. In recent years, it has
become an active task in remote sensing area and numerous algorithms have been
proposed for this ta... | computer science |
26,066 | Deeply-Supervised Recurrent Convolutional Neural Network for Saliency
Detection | cs.CV | This paper proposes a novel saliency detection method by developing a
deeply-supervised recurrent convolutional neural network (DSRCNN), which
performs a full image-to-image saliency prediction. For saliency detection, the
local, global, and contextual information of salient objects is important to
obtain a high qualit... | computer science |
26,067 | A Holistic Approach for Data-Driven Object Cutout | cs.CV | Object cutout is a fundamental operation for image editing and manipulation,
yet it is extremely challenging to automate it in real-world images, which
typically contain considerable background clutter. In contrast to existing
cutout methods, which are based mainly on low-level image analysis, we propose
a more holisti... | computer science |
26,068 | Saliency Detection via Combining Region-Level and Pixel-Level
Predictions with CNNs | cs.CV | This paper proposes a novel saliency detection method by combining
region-level saliency estimation and pixel-level saliency prediction with CNNs
(denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional
neural network (called pixel-level CNN) is constructed by modifying the VGGNet
architecture to ... | computer science |
26,069 | Seeing with Humans: Gaze-Assisted Neural Image Captioning | cs.CV | Gaze reflects how humans process visual scenes and is therefore increasingly
used in computer vision systems. Previous works demonstrated the potential of
gaze for object-centric tasks, such as object localization and recognition, but
it remains unclear if gaze can also be beneficial for scene-centric tasks, such
as im... | computer science |
26,070 | Refining Geometry from Depth Sensors using IR Shading Images | cs.CV | We propose a method to refine geometry of 3D meshes from a consumer level
depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared
(IR) camera. A major benefit to using an IR camera instead of an RGB camera is
that the IR images captured are narrow band images that filter out most
undesired ambie... | computer science |
26,071 | Efficient Multi-Frequency Phase Unwrapping using Kernel Density
Estimation | cs.CV | In this paper we introduce an efficient method to unwrap multi-frequency
phase estimates for time-of-flight ranging. The algorithm generates multiple
depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them.
The confidence produced by the KDE is also an effective means to detect
outliers. We also ... | computer science |
26,072 | How Image Degradations Affect Deep CNN-based Face Recognition? | cs.CV | Face recognition approaches that are based on deep convolutional neural
networks (CNN) have been dominating the field. The performance improvements
they have provided in the so called in-the-wild datasets are significant,
however, their performance under image quality degradations have not been
assessed, yet. This is p... | computer science |
26,073 | Leveraging Structural Context Models and Ranking Score Fusion for Human
Interaction Prediction | cs.CV | Predicting an interaction before it is fully executed is very important in
applications such as human-robot interaction and video surveillance. In a
two-human interaction scenario, there often contextual dependency structure
between the global interaction context of the two humans and the local context
of the different... | computer science |
26,074 | Photo Filter Recommendation by Category-Aware Aesthetic Learning | cs.CV | Nowadays, social media has become a popular platform for the public to share
photos. To make photos more visually appealing, users usually apply filters on
their photos without domain knowledge. However, due to the growing number of
filter types, it becomes a major issue for users to choose the best filter
type. For th... | computer science |
26,075 | Multi-Person Tracking by Multicut and Deep Matching | cs.CV | In [1], we proposed a graph-based formulation that links and clusters person
hypotheses over time by solving a minimum cost subgraph multicut problem. In
this paper, we modify and extend [1] in three ways: 1) We introduce a novel
local pairwise feature based on local appearance matching that is robust to
partial occlus... | computer science |
26,076 | Semantic Understanding of Scenes through the ADE20K Dataset | cs.CV | Scene parsing, or recognizing and segmenting objects and stuff in an image,
is one of the key problems in computer vision. Despite the community's efforts
in data collection, there are still few image datasets covering a wide range of
scenes and object categories with dense and detailed annotations for scene
parsing. I... | computer science |
26,077 | We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based
Methods | cs.CV | Recently, Wi-Fi has caught tremendous attention for its ubiquity, and,
motivated by Wi-Fi's low cost and privacy preservation, researchers have been
putting lots of investigation into its potential on action recognition and even
person identification. In this paper, we offer an comprehensive overview on
these two topic... | computer science |
26,078 | A Recurrent Encoder-Decoder Network for Sequential Face Alignment | cs.CV | We propose a novel recurrent encoder-decoder network model for real-time
video-based face alignment. Our proposed model predicts 2D facial point maps
regularized by a regression loss, while uniquely exploiting recurrent learning
at both spatial and temporal dimensions. At the spatial level, we add a
feedback loop conne... | computer science |
26,079 | On the Existence of a Projective Reconstruction | cs.CV | In this note we study the connection between the existence of a projective
reconstruction and the existence of a fundamental matrix satisfying the
epipolar constraints. | computer science |
26,080 | Rigid Slice-To-Volume Medical Image Registration through Markov Random
Fields | cs.CV | Rigid slice-to-volume registration is a challenging task, which finds
application in medical imaging problems like image fusion for image guided
surgeries and motion correction for volume reconstruction. It is usually
formulated as an optimization problem and solved using standard continuous
methods. In this paper, we ... | computer science |
26,081 | Learning Spatially Regularized Correlation Filters for Visual Tracking | cs.CV | Robust and accurate visual tracking is one of the most challenging computer
vision problems. Due to the inherent lack of training data, a robust approach
for constructing a target appearance model is crucial. Recently,
discriminatively learned correlation filters (DCF) have been successfully
applied to address this pro... | computer science |
26,082 | Detecting Vanishing Points using Global Image Context in a Non-Manhattan
World | cs.CV | We propose a novel method for detecting horizontal vanishing points and the
zenith vanishing point in man-made environments. The dominant trend in existing
methods is to first find candidate vanishing points, then remove outliers by
enforcing mutual orthogonality. Our method reverses this process: we propose a
set of h... | computer science |
26,083 | Back to Basics: Unsupervised Learning of Optical Flow via Brightness
Constancy and Motion Smoothness | cs.CV | Recently, convolutional networks (convnets) have proven useful for predicting
optical flow. Much of this success is predicated on the availability of large
datasets that require expensive and involved data acquisition and laborious la-
beling. To bypass these challenges, we propose an unsuper- vised approach
(i.e., wit... | computer science |
26,084 | Visual Processing by a Unified Schatten-$p$ Norm and $\ell_q$ Norm
Regularized Principal Component Pursuit | cs.CV | In this paper, we propose a non-convex formulation to recover the authentic
structure from the corrupted real data. Typically, the specific structure is
assumed to be low rank, which holds for a wide range of data, such as images
and videos. Meanwhile, the corruption is assumed to be sparse. In the
literature, such a p... | computer science |
26,085 | VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain
Segmentation | cs.CV | Recently deep residual learning with residual units for training very deep
neural networks advanced the state-of-the-art performance on 2D image
recognition tasks, e.g., object detection and segmentation. However, how to
fully leverage contextual representations for recognition tasks from volumetric
data has not been w... | computer science |
26,086 | STFCN: Spatio-Temporal FCN for Semantic Video Segmentation | cs.CV | This paper presents a novel method to involve both spatial and temporal
features for semantic video segmentation. Current work on convolutional neural
networks(CNNs) has shown that CNNs provide advanced spatial features supporting
a very good performance of solutions for both image and video analysis,
especially for th... | computer science |
26,087 | Domain Separation Networks | cs.CV | The cost of large scale data collection and annotation often makes the
application of machine learning algorithms to new tasks or datasets
prohibitively expensive. One approach circumventing this cost is training
models on synthetic data where annotations are provided automatically. Despite
their appeal, such models of... | computer science |
26,088 | Multiple objects tracking in surveillance video using color and Hu
moments | cs.CV | Multiple objects tracking finds its applications in many high level vision
analysis like object behaviour interpretation and gait recognition. In this
paper, a feature based method to track the multiple moving objects in
surveillance video sequence is proposed. Object tracking is done by extracting
the color and Hu mom... | computer science |
26,089 | Efficient Continuous Relaxations for Dense CRF | cs.CV | Dense conditional random fields (CRF) with Gaussian pairwise potentials have
emerged as a popular framework for several computer vision applications such as
stereo correspondence and semantic segmentation. By modeling long-range
interactions, dense CRFs provide a more detailed labelling compared to their
sparse counter... | computer science |
26,090 | CrowdNet: A Deep Convolutional Network for Dense Crowd Counting | cs.CV | Our work proposes a novel deep learning framework for estimating crowd
density from static images of highly dense crowds. We use a combination of deep
and shallow, fully convolutional networks to predict the density map for a
given crowd image. Such a combination is used for effectively capturing both
the high-level se... | computer science |
26,091 | Large-scale Continuous Gesture Recognition Using Convolutional Neural
Networks | cs.CV | This paper addresses the problem of continuous gesture recognition from
sequences of depth maps using convolutional neutral networks (ConvNets). The
proposed method first segments individual gestures from a depth sequence based
on quantity of movement (QOM). For each segmented gesture, an Improved Depth
Motion Map (IDM... | computer science |
26,092 | Convolutional Network for Attribute-driven and Identity-preserving Human
Face Generation | cs.CV | This paper focuses on the problem of generating human face pictures from
specific attributes. The existing CNN-based face generation models, however,
either ignore the identity of the generated face or fail to preserve the
identity of the reference face image. Here we address this problem from the
view of optimization,... | computer science |
26,093 | Failure Detection for Facial Landmark Detectors | cs.CV | Most face applications depend heavily on the accuracy of the face and facial
landmarks detectors employed. Prediction of attributes such as gender, age, and
identity usually completely fail when the faces are badly aligned due to
inaccurate facial landmark detection. Despite the impressive recent advances in
face and f... | computer science |
26,094 | Searching Action Proposals via Spatial Actionness Estimation and
Temporal Path Inference and Tracking | cs.CV | In this paper, we address the problem of searching action proposals in
unconstrained video clips. Our approach starts from actionness estimation on
frame-level bounding boxes, and then aggregates the bounding boxes belonging to
the same actor across frames via linking, associating, tracking to generate
spatial-temporal... | computer science |
26,095 | Does V-NIR based Image Enhancement Come with Better Features? | cs.CV | Image enhancement using the visible (V) and near-infrared (NIR) usually
enhances useful image details. The enhanced images are evaluated by observers
perception, instead of quantitative feature evaluation. Thus, can we say that
these enhanced images using NIR information has better features in comparison
to the compute... | computer science |
26,096 | Neural Networks with Smooth Adaptive Activation Functions for Regression | cs.CV | In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters
that control the shapes of activation functions. These parameters are trained
along with other parameters in the NN. AAFs have improved performance of Neural
Networks (NN) in multiple classification tasks. In this paper, we propose and
apply A... | computer science |
26,097 | A Non-Local Conventional Approach for Noise Removal in 3D MRI | cs.CV | In this paper, a filtering approach for the 3D magnetic resonance imaging
(MRI) assuming a Rician model for noise is addressed. Our denoising method is
based on the Conventional Approach (CA) proposed to deal with the noise issue
in the squared domain of the acquired magnitude MRI, where the noise
distribution follows ... | computer science |
26,098 | On Clustering and Embedding Mixture Manifolds using a Low Rank
Neighborhood Approach | cs.CV | Samples from intimate (non-linear) mixtures are generally modeled as being
drawn from a smooth manifold. Scenarios where the data contains multiple
intimate mixtures with some constituent materials in common can be thought of
as manifolds which share a boundary. Two important steps in the processing of
such data are (i... | computer science |
26,099 | Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using
CNN Features | cs.CV | In this paper we report results for recognizing colorectal NBI endoscopic
images by using features extracted from convolutional neural network (CNN). In
this comparative study, we extract features from different layers from
different CNN models, and then train linear SVM classifiers. Experimental
results with 10-fold c... | computer science |
26,100 | Transfer Learning for Endoscopic Image Classification | cs.CV | In this paper we propose a method for transfer learning of endoscopic images.
For transferring between features obtained from images taken by different (old
and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed
by Hoffman et al. in order to use L2 distance constraints as regularization,
called M... | computer science |
26,101 | A Novel Approach for Shot Boundary Detection in Videos | cs.CV | This paper presents a novel approach for video shot boundary detection. The
proposed approach is based on split and merge concept. A fisher linear
discriminant criterion is used to guide the process of both splitting and
merging. For the purpose of capturing the between class and within class
scatter we employ 2D2 FLD ... | computer science |
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