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27,902 | Partially Occluded Leaf Recognition via Subgraph Matching and Energy
Optimization | cs.CV | We present an approach to match partially occluded plant leaves with
databases of full plant leaves. Although contour based 2D shape matching has
been studied extensively in the last couple of decades, matching occluded
leaves with full leaf databases is an open and little worked on problem.
Classifying occluded plant ... | computer science |
27,903 | Automatic Real-time Background Cut for Portrait Videos | cs.CV | We in this paper solve the problem of high-quality automatic real-time
background cut for 720p portrait videos. We first handle the background
ambiguity issue in semantic segmentation by proposing a global background
attenuation model. A spatial-temporal refinement network is developed to
further refine the segmentatio... | computer science |
27,904 | Active Collaborative Ensemble Tracking | cs.CV | A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classifica... | computer science |
27,905 | Outline Colorization through Tandem Adversarial Networks | cs.CV | When creating digital art, coloring and shading are often time consuming
tasks that follow the same general patterns. A solution to automatically
colorize raw line art would have many practical applications. We propose a
setup utilizing two networks in tandem: a color prediction network based only
on outlines, and a sh... | computer science |
27,906 | Image reconstruction by domain transform manifold learning | cs.CV | Image reconstruction plays a critical role in the implementation of all
contemporary imaging modalities across the physical and life sciences including
optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the
sensor encodes an intermediate representation of an object in the sensor
domain, which is s... | computer science |
27,907 | Improving Small Object Proposals for Company Logo Detection | cs.CV | Many modern approaches for object detection are two-staged pipelines. The
first stage identifies regions of interest which are then classified in the
second stage. Faster R-CNN is such an approach for object detection which
combines both stages into a single pipeline. In this paper we apply Faster
R-CNN to the task of ... | computer science |
27,908 | Unbiased Shape Compactness for Segmentation | cs.CV | We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimizat... | computer science |
27,909 | Object Discovery via Cohesion Measurement | cs.CV | Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However,... | computer science |
27,910 | Expressing Facial Structure and Appearance Information in Frequency
Domain for Face Recognition | cs.CV | Beneath the uncertain primitive visual features of face images are the
primitive intrinsic structural patterns (PISP) essential for characterizing a
sample face discriminative attributes. It is on this basis that this paper
presents a simple yet effective facial descriptor formed from derivatives of
Gaussian and Gabor ... | computer science |
27,911 | A Unified Approach of Multi-scale Deep and Hand-crafted Features for
Defocus Estimation | cs.CV | In this paper, we introduce robust and synergetic hand-crafted features and a
simple but efficient deep feature from a convolutional neural network (CNN)
architecture for defocus estimation. This paper systematically analyzes the
effectiveness of different features, and shows how each feature can compensate
for the wea... | computer science |
27,912 | Understanding People Flow in Transportation Hubs | cs.CV | In this paper, we aim to monitor the flow of people in large public
infrastructures. We propose an unsupervised methodology to cluster people flow
patterns into the most typical and meaningful configurations. By processing 3D
images from a network of depth cameras, we built a descriptor for the flow
pattern. We define ... | computer science |
27,913 | The Pose Knows: Video Forecasting by Generating Pose Futures | cs.CV | Current approaches in video forecasting attempt to generate videos directly
in pixel space using Generative Adversarial Networks (GANs) or Variational
Autoencoders (VAEs). However, since these approaches try to model all the
structure and scene dynamics at once, in unconstrained settings they often
generate uninterpret... | computer science |
27,914 | Effective scaling registration approach by imposing the emphasis on the
scale factor | cs.CV | This paper proposes an effective approach for the scaling registration of
$m$-D point sets. Different from the rigid transformation, the scaling
registration can not be formulated into the common least square function due to
the ill-posed problem caused by the scale factor. Therefore, this paper designs
a novel objecti... | computer science |
27,915 | Joint Denoising / Compression of Image Contours via Shape Prior and
Context Tree | cs.CV | With the advent of depth sensing technologies, the extraction of object
contours in images---a common and important pre-processing step for later
higher-level computer vision tasks like object detection and human action
recognition---has become easier. However, acquisition noise in captured depth
images means that dete... | computer science |
27,916 | Indoor Frame Recovery from Refined Line Segments | cs.CV | An important yet challenging problem in understanding indoor scene is
recovering indoor frame structure from a monocular image. It is more difficult
when occlusions and illumination vary, and object boundaries are weak. To
overcome these difficulties, a new approach based on line segment refinement
with two constraints... | computer science |
27,917 | SurfCut: Surfaces of Minimal Paths From Topological Structures | cs.CV | We present SurfCut, an algorithm for extracting a smooth, simple surface with
an unknown 3D curve boundary from a noisy 3D image and a seed point. Our method
is built on the novel observation that certain ridge curves of a function
defined on a front propagated using the Fast Marching algorithm lie on the
surface. Our ... | computer science |
27,918 | Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model
Meets Image Classification | cs.CV | Linear synthesis model based dictionary learning framework has achieved
remarkable performances in image classification in the last decade. Behaved as
a generative feature model, it however suffers from some intrinsic
deficiencies. In this paper, we propose a novel parametric nonlinear analysis
cosparse model (NACM) wi... | computer science |
27,919 | Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual
Grouping | cs.CV | This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking prob... | computer science |
27,920 | Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the
Segmentation(s) | cs.CV | We propose the ambiguity problem for the foreground object segmentation task
and motivate the importance of estimating and accounting for this ambiguity
when designing vision systems. Specifically, we distinguish between images
which lead multiple annotators to segment different foreground objects
(ambiguous) versus mi... | computer science |
27,921 | Adversarial PoseNet: A Structure-aware Convolutional Network for Human
Pose Estimation | cs.CV | For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joi... | computer science |
27,922 | Level Playing Field for Million Scale Face Recognition | cs.CV | Face recognition has the perception of a solved problem, however when tested
at the million-scale exhibits dramatic variation in accuracies across the
different algorithms. Are the algorithms very different? Is access to good/big
training data their secret weapon? Where should face recognition improve? To
address those... | computer science |
27,923 | Sub-Pixel Registration of Wavelet-Encoded Images | cs.CV | Sub-pixel registration is a crucial step for applications such as
super-resolution in remote sensing, motion compensation in magnetic resonance
imaging, and non-destructive testing in manufacturing, to name a few. Recently,
these technologies have been trending towards wavelet encoded imaging and
sparse/compressive sen... | computer science |
27,924 | A Statistical Model for Simultaneous Template Estimation, Bias
Correction, and Registration of 3D Brain Images | cs.CV | Template estimation plays a crucial role in computational anatomy since it
provides reference frames for performing statistical analysis of the underlying
anatomical population variability. While building models for template
estimation, variability in sites and image acquisition protocols need to be
accounted for. To a... | computer science |
27,925 | Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach | cs.CV | It has been well recognized that detecting drivable area is central to
self-driving cars. Most of existing methods attempt to locate road surface by
using lane line, thereby restricting to drivable area on which have a clear
lane mark. This paper proposes an unsupervised approach for detecting drivable
area utilizing b... | computer science |
27,926 | Shearlet-based compressed sensing for fast 3D cardiac MR imaging using
iterative reweighting | cs.CV | High-resolution three-dimensional (3D) cardiovascular magnetic resonance
(CMR) is a valuable medical imaging technique, but its widespread application
in clinical practice is hampered by long acquisition times. Here we present a
novel compressed sensing (CS) reconstruction approach using shearlets as a
sparsifying tran... | computer science |
27,927 | Generalized orderless pooling performs implicit salient matching | cs.CV | Most recent CNN architectures use average pooling as a final feature encoding
step. In the field of fine-grained recognition, however, recent global
representations like bilinear pooling offer improved performance. In this
paper, we generalize average and bilinear pooling to "alpha-pooling", allowing
for learning the p... | computer science |
27,928 | Machine Vision System for 3D Plant Phenotyping | cs.CV | Machine vision for plant phenotyping is an emerging research area for
producing high throughput in agriculture and crop science applications. Since
2D based approaches have their inherent limitations, 3D plant analysis is
becoming state of the art for current phenotyping technologies. We present an
automated system for... | computer science |
27,929 | Spotting the Difference: Context Retrieval and Analysis for Improved
Forgery Detection and Localization | cs.CV | As image tampering becomes ever more sophisticated and commonplace, the need
for image forensics algorithms that can accurately and quickly detect forgeries
grows. In this paper, we revisit the ideas of image querying and retrieval to
provide clues to better localize forgeries. We propose a method to perform
large-scal... | computer science |
27,930 | Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for
Unsupervised Domain Adaptation | cs.CV | In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted
as a discrepancy metric between the distributions of source and target domains.
However, existing MMD-based domain adaptation methods generally ignore the
changes of class prior distributions, i.e., class weight bias across domains.
This remai... | computer science |
27,931 | Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI
Super-Resolution | cs.CV | In this work, we investigate the value of uncertainty modeling in 3D
super-resolution with convolutional neural networks (CNNs). Deep learning has
shown success in a plethora of medical image transformation problems, such as
super-resolution (SR) and image synthesis. However, the highly ill-posed nature
of such problem... | computer science |
27,932 | Submodular Trajectory Optimization for Aerial 3D Scanning | cs.CV | Drones equipped with cameras are emerging as a powerful tool for large-scale
aerial 3D scanning, but existing automatic flight planners do not exploit all
available information about the scene, and can therefore produce inaccurate and
incomplete 3D models. We present an automatic method to generate drone
trajectories, ... | computer science |
27,933 | Hyperspectral Image Classification with Markov Random Fields and a
Convolutional Neural Network | cs.CV | This paper presents a new supervised classification algorithm for remotely
sensed hyperspectral image (HSI) which integrates spectral and spatial
information in a unified Bayesian framework. First, we formulate the HSI
classification problem from a Bayesian perspective. Then, we adopt a
convolutional neural network (CN... | computer science |
27,934 | Dense-Captioning Events in Videos | cs.CV | Most natural videos contain numerous events. For example, in a video of a
"man playing a piano", the video might also contain "another man dancing" or "a
crowd clapping". We introduce the task of dense-captioning events, which
involves both detecting and describing events in a video. We propose a new
model that is able... | computer science |
27,935 | Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep
Convolutional Neural Networks | cs.CV | We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
two-stages deep convolutional neural networks (DCNN). Compared to existing
DCNN-based DR detection methods, the proposed algorithm have the following
advantages: (1) Our method can point out the location and type of lesions in
the fundus imag... | computer science |
27,936 | Offline Handwritten Recognition of Malayalam District Name - A Holistic
Approach | cs.CV | Various machine learning methods for writer independent recognition of
Malayalam handwritten district names are discussed in this paper. Data
collected from 56 different writers are used for the experiments. The proposed
work can be used for the recognition of district in the address written in
Malayalam. Different met... | computer science |
27,937 | Statistical learning of rational wavelet transform for natural images | cs.CV | Motivated with the concept of transform learning and the utility of rational
wavelet transform in audio and speech processing, this paper proposes Rational
Wavelet Transform Learning in Statistical sense (RWLS) for natural images. The
proposed RWLS design is carried out via lifting framework and is shown to have
a clos... | computer science |
27,938 | Investigation of Different Skeleton Features for CNN-based 3D Action
Recognition | cs.CV | Deep learning techniques are being used in skeleton based action recognition
tasks and outstanding performance has been reported. Compared with RNN based
methods which tend to overemphasize temporal information, CNN-based approaches
can jointly capture spatio-temporal information from texture color images
encoded from ... | computer science |
27,939 | Transfer Learning by Ranking for Weakly Supervised Object Annotation | cs.CV | Most existing approaches to training object detectors rely on fully
supervised learning, which requires the tedious manual annotation of object
location in a training set. Recently there has been an increasing interest in
developing weakly supervised approach to detector training where the object
location is not manual... | computer science |
27,940 | Error Corrective Boosting for Learning Fully Convolutional Networks with
Limited Data | cs.CV | Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on... | computer science |
27,941 | Active Image-based Modeling with a Toy Drone | cs.CV | Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of ou... | computer science |
27,942 | Visual Attribute Transfer through Deep Image Analogy | cs.CV | We propose a new technique for visual attribute transfer across images that
may have very different appearance but have perceptually similar semantic
structure. By visual attribute transfer, we mean transfer of visual information
(such as color, tone, texture, and style) from one image to another. For
example, one imag... | computer science |
27,943 | Recovery of structure of looped jointed objects from multiframes | cs.CV | A method to recover structural parameters of looped jointed objects from
multiframes is being developed. Each rigid part of the jointed body needs only
to be traced at two (that is at junction) points.
This method has been linearized for 4-part loops, with recovery from at least
19 frames. | computer science |
27,944 | Cascaded Boundary Regression for Temporal Action Detection | cs.CV | Temporal action detection in long videos is an important problem.
State-of-the-art methods address this problem by applying action classifiers on
sliding windows. Although sliding windows may contain an identifiable portion
of the actions, they may not necessarily cover the entire action instance,
which would lead to i... | computer science |
27,945 | Marine Animal Classification with Correntropy Loss Based Multi-view
Learning | cs.CV | To analyze marine animals behavior, seasonal distribution and abundance,
digital imagery can be acquired by visual or Lidar camera. Depending on the
quantity and properties of acquired imagery, the animals are characterized as
either features (shape, color, texture, etc.), or dissimilarity matrices
derived from differe... | computer science |
27,946 | Unsupervised Part-based Weighting Aggregation of Deep Convolutional
Features for Image Retrieval | cs.CV | In this paper, we propose a simple but effective semantic part-based
weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the
discriminative filters of deep convolutional layers as part detectors.
Moreover, we propose the effective unsupervised strategy to select some part
detectors to generate th... | computer science |
27,947 | Super-Resolution of Wavelet-Encoded Images | cs.CV | Multiview super-resolution image reconstruction (SRIR) is often cast as a
resampling problem by merging non-redundant data from multiple low-resolution
(LR) images on a finer high-resolution (HR) grid, while inverting the effect of
the camera point spread function (PSF). One main problem with multiview methods
is that ... | computer science |
27,948 | Amortized Inference and Learning in Latent Conditional Random Fields for
Weakly-Supervised Semantic Image Segmentation | cs.CV | Conditional random fields (CRFs) are commonly employed as a post-processing
tool for image segmentation tasks. The unary potentials of the CRF are often
learnt independently by a classifier, thereby decoupling the inference in CRF
from the training of classifier. Such a scheme works effectively, when
pixel-level labell... | computer science |
27,949 | Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation | cs.CV | While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation fo... | computer science |
27,950 | Optical Flow in Mostly Rigid Scenes | cs.CV | The optical flow of natural scenes is a combination of the motion of the
observer and the independent motion of objects. Existing algorithms typically
focus on either recovering motion and structure under the assumption of a
purely static world or optical flow for general unconstrained scenes. We
combine these approach... | computer science |
27,951 | Rotation Averaging and Strong Duality | cs.CV | In this paper we explore the role of duality principles within the problem of
rotation averaging, a fundamental task in a wide range of computer vision
applications. In its conventional form, rotation averaging is stated as a
minimization over multiple rotation constraints. As these constraints are
non-convex, this pro... | computer science |
27,952 | Weakly-supervised Visual Grounding of Phrases with Linguistic Structures | cs.CV | We propose a weakly-supervised approach that takes image-sentence pairs as
input and learns to visually ground (i.e., localize) arbitrary linguistic
phrases, in the form of spatial attention masks. Specifically, the model is
trained with images and their associated image-level captions, without any
explicit region-to-p... | computer science |
27,953 | Learning to Estimate 3D Hand Pose from Single RGB Images | cs.CV | Low-cost consumer depth cameras and deep learning have enabled reasonable 3D
hand pose estimation from single depth images. In this paper, we present an
approach that estimates 3D hand pose from regular RGB images. This task has far
more ambiguities due to the missing depth information. To this end, we propose
a deep n... | computer science |
27,954 | Gabor Convolutional Networks | cs.CV | Steerable properties dominate the design of traditional filters, e.g., Gabor
filters, and endow features the capability of dealing with spatial
transformations. However, such excellent properties have not been well explored
in the popular deep convolutional neural networks (DCNNs). In this paper, we
propose a new deep ... | computer science |
27,955 | Toward Open-Set Face Recognition | cs.CV | Much research has been conducted on both face identification and face
verification, with greater focus on the latter. Research on face identification
has mostly focused on using closed-set protocols, which assume that all probe
images used in evaluation contain identities of subjects that are enrolled in
the gallery. R... | computer science |
27,956 | Attributes2Classname: A discriminative model for attribute-based
unsupervised zero-shot learning | cs.CV | We propose a novel approach for unsupervised zero-shot learning (ZSL) of
classes based on their names. Most existing unsupervised ZSL methods aim to
learn a model for directly comparing image features and class names. However,
this proves to be a difficult task due to dominance of non-visual semantics in
underlying vec... | computer science |
27,957 | Am I Done? Predicting Action Progress in Videos | cs.CV | In this paper we introduce the problem of predicting action progress in
videos. We argue that this is an extremely important task because, on the one
hand, it can be valuable for a wide range of applications and, on the other
hand, it facilitates better action detection results. To solve this problem we
introduce a nov... | computer science |
27,958 | From Zero-shot Learning to Conventional Supervised Classification:
Unseen Visual Data Synthesis | cs.CV | Robust object recognition systems usually rely on powerful feature extraction
mechanisms from a large number of real images. However, in many realistic
applications, collecting sufficient images for ever-growing new classes is
unattainable. In this paper, we propose a new Zero-shot learning (ZSL)
framework that can syn... | computer science |
27,959 | A Deep Learning Perspective on the Origin of Facial Expressions | cs.CV | Facial expressions play a significant role in human communication and
behavior. Psychologists have long studied the relationship between facial
expressions and emotions. Paul Ekman et al., devised the Facial Action Coding
System (FACS) to taxonomize human facial expressions and model their behavior.
The ability to reco... | computer science |
27,960 | Action Tubelet Detector for Spatio-Temporal Action Localization | cs.CV | Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that ... | computer science |
27,961 | Edge-based Component-Trees for Multi-Channel Image Segmentation | cs.CV | We introduce the concept of edge-based component-trees for images with an
arbitrary number of channels. The approach is a natural extension of the
classical component-tree devoted to gray-scale images. The similar structure
enables the translation of many gray-level image processing techniques based on
the component-tr... | computer science |
27,962 | Auto-painter: Cartoon Image Generation from Sketch by Using Conditional
Generative Adversarial Networks | cs.CV | Recently, realistic image generation using deep neural networks has become a
hot topic in machine learning and computer vision. Images can be generated at
the pixel level by learning from a large collection of images. Learning to
generate colorful cartoon images from black-and-white sketches is not only an
interesting ... | computer science |
27,963 | Recurrent Soft Attention Model for Common Object Recognition | cs.CV | We propose the Recurrent Soft Attention Model, which integrates the visual
attention from the original image to a LSTM memory cell through a down-sample
network. The model recurrently transmits visual attention to the memory cells
for glimpse mask generation, which is a more natural way for attention
integration and ex... | computer science |
27,964 | Motion Prediction Under Multimodality with Conditional Stochastic
Networks | cs.CV | Given a visual history, multiple future outcomes for a video scene are
equally probable, in other words, the distribution of future outcomes has
multiple modes. Multimodality is notoriously hard to handle by standard
regressors or classifiers: the former regress to the mean and the latter
discretize a continuous high d... | computer science |
27,965 | Characterizing and Improving Stability in Neural Style Transfer | cs.CV | Recent progress in style transfer on images has focused on improving the
quality of stylized images and speed of methods. However, real-time methods are
highly unstable resulting in visible flickering when applied to videos. In this
work we characterize the instability of these methods by examining the solution
set of ... | computer science |
27,966 | TALL: Temporal Activity Localization via Language Query | cs.CV | This paper focuses on temporal localization of actions in untrimmed videos.
Existing methods typically train classifiers for a pre-defined list of actions
and apply them in a sliding window fashion. However, activities in the wild
consist of a wide combination of actors, actions and objects; it is difficult
to design a... | computer science |
27,967 | Bridging between Computer and Robot Vision through Data Augmentation: a
Case Study on Object Recognition | cs.CV | Despite the impressive progress brought by deep network in visual object
recognition, robot vision is still far from being a solved problem. The most
successful convolutional architectures are developed starting from ImageNet, a
large scale collection of images of object categories downloaded from the Web.
This kind of... | computer science |
27,968 | Part-based Deep Hashing for Large-scale Person Re-identification | cs.CV | Large-scale is a trend in person re-identification (re-id). It is important
that real-time search be performed in a large gallery. While previous methods
mostly focus on discriminative learning, this paper makes the attempt in
integrating deep learning and hashing into one framework to evaluate the
efficiency and accur... | computer science |
27,969 | Unified Embedding and Metric Learning for Zero-Exemplar Event Detection | cs.CV | Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
Related works train a bank... | computer science |
27,970 | S-OHEM: Stratified Online Hard Example Mining for Object Detection | cs.CV | One of the major challenges in object detection is to propose detectors with
highly accurate localization of objects. The online sampling of high-loss
region proposals (hard examples) uses the multitask loss with equal weight
settings across all loss types (e.g, classification and localization, rigid and
non-rigid cate... | computer science |
27,971 | Face Detection, Bounding Box Aggregation and Pose Estimation for Robust
Facial Landmark Localisation in the Wild | cs.CV | We present a framework for robust face detection and landmark localisation of
faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark
Localisation Competition'. The framework has four stages: face detection,
bounding box aggregation, pose estimation and landmark localisation. To achieve
a high d... | computer science |
27,972 | DeepCorrect: Correcting DNN models against Image Distortions | cs.CV | In recent years, the widespread use of deep neural networks (DNNs) has
facilitated great improvements in performance for computer vision tasks like
image classification and object recognition. In most realistic computer vision
applications, an input image undergoes some form of image distortion such as
blur and additiv... | computer science |
27,973 | Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation | cs.CV | Human pose estimation using deep neural networks aims to map input images
with large variations into multiple body keypoints which must satisfy a set of
geometric constraints and inter-dependency imposed by the human body model.
This is a very challenging nonlinear manifold learning process in a very high
dimensional f... | computer science |
27,974 | Deep Patch Learning for Weakly Supervised Object Classification and
Discovery | cs.CV | Patch-level image representation is very important for object classification
and detection, since it is robust to spatial transformation, scale variation,
and cluttered background. Many existing methods usually require fine-grained
supervisions (e.g., bounding-box annotations) to learn patch features, which
requires a ... | computer science |
27,975 | Sparse Representation-based Open Set Recognition | cs.CV | We propose a generalized Sparse Representation- based Classification (SRC)
algorithm for open set recognition where not all classes presented during
testing are known during training. The SRC algorithm uses class reconstruction
errors for classification. As most of the discriminative information for open
set recognitio... | computer science |
27,976 | On human motion prediction using recurrent neural networks | cs.CV | Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent wor... | computer science |
27,977 | Image Annotation using Multi-Layer Sparse Coding | cs.CV | Automatic annotation of images with descriptive words is a challenging
problem with vast applications in the areas of image search and retrieval. This
problem can be viewed as a label-assignment problem by a classifier dealing
with a very large set of labels, i.e., the vocabulary set. We propose a novel
annotation meth... | computer science |
27,978 | A Study and Comparison of Human and Deep Learning Recognition
Performance Under Visual Distortions | cs.CV | Deep neural networks (DNNs) achieve excellent performance on standard
classification tasks. However, under image quality distortions such as blur and
noise, classification accuracy becomes poor. In this work, we compare the
performance of DNNs with human subjects on distorted images. We show that,
although DNNs perform... | computer science |
27,979 | Context-Aware Trajectory Prediction | cs.CV | Human motion and behaviour in crowded spaces is influenced by several
factors, such as the dynamics of other moving agents in the scene, as well as
the static elements that might be perceived as points of attraction or
obstacles. In this work, we present a new model for human trajectory prediction
which is able to take... | computer science |
27,980 | Deep Visual Attention Prediction | cs.CV | In this work, we aim to predict human eye fixation with view-free scenes
based on an end-to-end deep learning architecture. Although Convolutional
Neural Networks (CNNs) have made substantial improvement on human attention
prediction, it is still needed to improve CNN based attention models by
efficiently leveraging mu... | computer science |
27,981 | Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical
Images using Weakly-Supervised Joint Convolutional Sparse Coding | cs.CV | Magnetic Resonance Imaging (MRI) offers high-resolution \emph{in vivo}
imaging and rich functional and anatomical multimodality tissue contrast. In
practice, however, there are challenges associated with considerations of
scanning costs, patient comfort, and scanning time that constrain how much data
can be acquired in... | computer science |
27,982 | Towards Applying the OPRA Theory to Shape Similarity | cs.CV | The motivation for using qualitative shape descriptions is as follows:
qualitative shape descriptions can implicitly act as a schema for measuring the
similarity of shapes, which has the potential to be cognitively adequate. Then,
shapes which are similar to each other would also be similar for a pattern
recognition al... | computer science |
27,983 | Large scale digital prostate pathology image analysis combining feature
extraction and deep neural network | cs.CV | Histopathological assessments, including surgical resection and core needle
biopsy, are the standard procedures in the diagnosis of the prostate cancer.
Current interpretation of the histopathology images includes the determination
of the tumor area, Gleason grading, and identification of certain
prognosis-critical fea... | computer science |
27,984 | Handwritten Bangla Digit Recognition Using Deep Learning | cs.CV | In spite of the advances in pattern recognition technology, Handwritten
Bangla Character Recognition (HBCR) (such as alpha-numeric and special
characters) remains largely unsolved due to the presence of many perplexing
characters and excessive cursive in Bangla handwriting. Even the best existing
recognizers do not lea... | computer science |
27,985 | Automatic Recognition of Mammal Genera on Camera-Trap Images using
Multi-Layer Robust Principal Component Analysis and Mixture Neural Networks | cs.CV | The segmentation and classification of animals from camera-trap images is due
to the conditions under which the images are taken, a difficult task. This work
presents a method for classifying and segmenting mammal genera from camera-trap
images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA)
for... | computer science |
27,986 | ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition | cs.CV | In this paper, we introduce a new and challenging large-scale food image
dataset called "ChineseFoodNet", which aims to automatically recognizing
pictured Chinese dishes. Most of the existing food image datasets collected
food images either from recipe pictures or selfie. In our dataset, images of
each food category of... | computer science |
27,987 | High-Level Concepts for Affective Understanding of Images | cs.CV | This paper aims to bridge the affective gap between image content and the
emotional response of the viewer it elicits by using High-Level Concepts
(HLCs). In contrast to previous work that relied solely on low-level features
or used convolutional neural network (CNN) as a black-box, we use HLCs
generated by pretrained ... | computer science |
27,988 | What Can Help Pedestrian Detection? | cs.CV | Aggregating extra features has been considered as an effective approach to
boost traditional pedestrian detection methods. However, there is still a lack
of studies on whether and how CNN-based pedestrian detectors can benefit from
these extra features. The first contribution of this paper is exploring this
issue by ag... | computer science |
27,989 | Face Recognition Machine Vision System Using Eigenfaces | cs.CV | Face Recognition is a common problem in Machine Learning. This technology has
already been widely used in our lives. For example, Facebook can automatically
tag people's faces in images, and also some mobile devices use face recognition
to protect private security. Face images comes with different background,
variant i... | computer science |
27,990 | Video Processing for Barycenter Trajectory Identification in Diving | cs.CV | The aim of this paper is to show a procedure for identify the barycentre of a
diver by means of video processing. This procedure is aimed to introduce
quantitative analysis tools and diving performance measurement and therefore in
diving training. Sport performance analysis is a trend that is growing
exponentially for ... | computer science |
27,991 | A Dual-Source Approach for 3D Human Pose Estimation from a Single Image | cs.CV | In this work we address the challenging problem of 3D human pose estimation
from single images. Recent approaches learn deep neural networks to regress 3D
pose directly from images. One major challenge for such methods, however, is
the collection of training data. Specifically, collecting large amounts of
training data... | computer science |
27,992 | Generative Cooperative Net for Image Generation and Data Augmentation | cs.CV | How to build a good model for image generation given an abstract concept is a
fundamental problem in computer vision. In this paper, we explore a generative
model for the task of generating unseen images with desired features. We
propose the Generative Cooperative Net (GCN) for image generation. The idea is
similar to ... | computer science |
27,993 | Multi Resolution LSTM For Long Term Prediction In Neural Activity Video | cs.CV | Epileptic seizures are caused by abnormal, overly syn- chronized, electrical
activity in the brain. The abnor- mal electrical activity manifests as waves,
propagating across the brain. Accurate prediction of the propagation velocity
and direction of these waves could enable real- time responsive brain
stimulation to su... | computer science |
27,994 | Object Detection by Spatio-Temporal Analysis and Tracking of the
Detected Objects in a Video with Variable Background | cs.CV | In this paper we propose a novel approach for detecting and tracking objects
in videos with variable background i.e. videos captured by moving cameras
without any additional sensor. In a video captured by a moving camera, both the
background and foreground are changing in each frame of the image sequence. So
for these ... | computer science |
27,995 | Learning non-maximum suppression | cs.CV | Object detectors have hugely profited from moving towards an end-to-end
learning paradigm: proposals, features, and the classifier becoming one neural
network improved results two-fold on general object detection. One
indispensable component is non-maximum suppression (NMS), a post-processing
algorithm responsible for ... | computer science |
27,996 | Temporal Segment Networks for Action Recognition in Videos | cs.CV | Deep convolutional networks have achieved great success for image
recognition. However, for action recognition in videos, their advantage over
traditional methods is not so evident. We present a general and flexible
video-level framework for learning action models in videos. This method, called
temporal segment network... | computer science |
27,997 | You said that? | cs.CV | We present a method for generating a video of a talking face. The method
takes as inputs: (i) still images of the target face, and (ii) an audio speech
segment; and outputs a video of the target face lip synched with the audio. The
method runs in real time and is applicable to faces and audio not seen at
training time.... | computer science |
27,998 | Residual Squeeze VGG16 | cs.CV | Deep learning has given way to a new era of machine learning, apart from
computer vision. Convolutional neural networks have been implemented in image
classification, segmentation and object detection. Despite recent advancements,
we are still in the very early stages and have yet to settle on best practices
for networ... | computer science |
27,999 | A simple yet effective baseline for 3d human pose estimation | cs.CV | Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a l... | computer science |
28,000 | CAD Priors for Accurate and Flexible Instance Reconstruction | cs.CV | We present an efficient and automatic approach for accurate reconstruction of
instances of big 3D objects from multiple, unorganized and unstructured point
clouds, in presence of dynamic clutter and occlusions. In contrast to
conventional scanning, where the background is assumed to be rather static, we
aim at handling... | computer science |
28,001 | CHAM: action recognition using convolutional hierarchical attention
model | cs.CV | Recently, the soft attention mechanism, which was originally proposed in
language processing, has been applied in computer vision tasks like image
captioning. This paper presents improvements to the soft attention model by
combining a convolutional LSTM with a hierarchical system architecture to
recognize action catego... | computer science |
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