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29,402 | A Deep Structured Learning Approach Towards Automating Connectome
Reconstruction from 3D Electron Micrographs | cs.CV | We present a deep structured learning method for neuron segmentation from 3D
electron microscopy (EM) which improves significantly upon the state of the art
in terms of accuracy and scalability. Our method consists of a 3D U-Net
classifier predicting affinity graphs on voxels, followed by iterative region
agglomeration... | computer science |
29,403 | Can you tell a face from a HEVC bitstream? | cs.CV | Image and video analytics are being increasingly used on a massive scale. Not
only is the amount of data growing, but the complexity of the data processing
pipelines is also increasing, thereby exacerbating the problem. It is becoming
increasingly important to save computational resources wherever possible. We
focus on... | computer science |
29,404 | Optimal Transport for Deep Joint Transfer Learning | cs.CV | Training a Deep Neural Network (DNN) from scratch requires a large amount of
labeled data. For a classification task where only small amount of training
data is available, a common solution is to perform fine-tuning on a DNN which
is pre-trained with related source data. This consecutive training process is
time consum... | computer science |
29,405 | A Product Shape Congruity Measure via Entropy in Shape Scale Space | cs.CV | Product shape is one of the factors that trigger preference decisions of
customers. Congruity of shape elements and deformation of shape from the
prototype are two factors that are found to influence aesthetic response, hence
preference. We propose a measure to indirectly quantify congruity of different
parts of the sh... | computer science |
29,406 | A Detail Based Method for Linear Full Reference Image Quality Prediction | cs.CV | In this paper, a novel Full Reference method is proposed for image quality
assessment, using the combination of two separate metrics to measure the
perceptually distinct impact of detail losses and of spurious details. To this
purpose, the gradient of the impaired image is locally decomposed as a
predicted version of t... | computer science |
29,407 | DPC-Net: Deep Pose Correction for Visual Localization | cs.CV | We present a novel method to fuse the power of deep networks with the
computational efficiency of geometric and probabilistic localization
algorithms. In contrast to other methods that completely replace a classical
visual estimator with a deep network, we propose an approach that uses a
convolutional neural network to... | computer science |
29,408 | Fully Convolutional Neural Networks for Dynamic Object Detection in Grid
Maps (Masters Thesis) | cs.CV | One of the most important parts of environment perception is the detection of
obstacles in the surrounding of the vehicle. To achieve that, several sensors
like radars, LiDARs and cameras are installed in autonomous vehicles. The
produced sensor data is fused to a general representation of the surrounding.
In this thes... | computer science |
29,409 | Fully Convolutional Neural Networks for Dynamic Object Detection in Grid
Maps | cs.CV | Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a m... | computer science |
29,410 | An Iterative Regression Approach for Face Pose Estimation from RGB
Images | cs.CV | This paper presents a iterative optimization method, explicit shape
regression, for face pose detection and localization. The regression function
is learnt to find out the entire facial shape and minimize the alignment
errors. A cascaded learning framework is employed to enhance shape constraint
during detection. A com... | computer science |
29,411 | Deep multi-frame face super-resolution | cs.CV | Face verification and recognition problems have seen rapid progress in recent
years, however recognition from small size images remains a challenging task
that is inherently intertwined with the task of face super-resolution. Tackling
this problem using multiple frames is an attractive idea, yet requires solving
the al... | computer science |
29,412 | 3D Densely Convolutional Networks for Volumetric Segmentation | cs.CV | In the isointense stage, the accurate volumetric image segmentation is a
challenging task due to the low contrast between tissues. In this paper, we
propose a novel very deep network architecture based on a densely convolutional
network for volumetric brain segmentation. The proposed network architecture
provides a den... | computer science |
29,413 | Recurrent neural networks based Indic word-wise script identification
using character-wise training | cs.CV | This paper presents a novel methodology of Indic handwritten script
recognition using Recurrent Neural Networks and addresses the problem of script
recognition in poor data scenarios, such as when only character level online
data is available. It is based on the hypothesis that curves of online
character data comprise ... | computer science |
29,414 | Fused Text Segmentation Networks for Multi-oriented Scene Text Detection | cs.CV | In this paper, we introduce a novel end-end framework for multi-oriented
scene text detection from an instance-aware semantic segmentation perspective.
We present Fused Text Segmentation Networks, which combine multi-level features
during the feature extracting as text instance may rely on finer feature
expression comp... | computer science |
29,415 | Stack-Captioning: Coarse-to-Fine Learning for Image Captioning | cs.CV | The existing image captioning approaches typically train a one-stage sentence
decoder, which is difficult to generate rich fine-grained descriptions. On the
other hand, multi-stage image caption model is hard to train due to the
vanishing gradient problem. In this paper, we propose a coarse-to-fine
multi-stage predicti... | computer science |
29,416 | Low-memory GEMM-based convolution algorithms for deep neural networks | cs.CV | Deep neural networks (DNNs) require very large amounts of computation both
for training and for inference when deployed in the field. A common approach to
implementing DNNs is to recast the most computationally expensive operations as
general matrix multiplication (GEMM). However, as we demonstrate in this paper,
there... | computer science |
29,417 | Automated Identification of Trampoline Skills Using Computer Vision
Extracted Pose Estimation | cs.CV | A novel method to identify trampoline skills using a single video camera is
proposed herein. Conventional computer vision techniques are used for
identification, estimation, and tracking of the gymnast's body in a video
recording of the routine. For each frame, an open source convolutional neural
network is used to est... | computer science |
29,418 | Generic Sketch-Based Retrieval Learned without Drawing a Single Sketch | cs.CV | We cast the sketch-based retrieval as edge-map matching. A shared
convolutional network is trained to extract descriptors from edge maps and
sketches, which are treated as a special case of edge maps. The network is
fine-tuned solely from edge maps of landmark images. The training images are
acquired in a fully unsuper... | computer science |
29,419 | One-Shot Learning for Semantic Segmentation | cs.CV | Low-shot learning methods for image classification support learning from
sparse data. We extend these techniques to support dense semantic image
segmentation. Specifically, we train a network that, given a small set of
annotated images, produces parameters for a Fully Convolutional Network (FCN).
We use this FCN to per... | computer science |
29,420 | Why Do Deep Neural Networks Still Not Recognize These Images?: A
Qualitative Analysis on Failure Cases of ImageNet Classification | cs.CV | In a recent decade, ImageNet has become the most notable and powerful
benchmark database in computer vision and machine learning community. As
ImageNet has emerged as a representative benchmark for evaluating the
performance of novel deep learning models, its evaluation tends to include only
quantitative measures such ... | computer science |
29,421 | Deep Generative Filter for Motion Deblurring | cs.CV | Removing blur caused by camera shake in images has always been a challenging
problem in computer vision literature due to its ill-posed nature. Motion blur
caused due to the relative motion between the camera and the object in 3D space
induces a spatially varying blurring effect over the entire image. In this
paper, we... | computer science |
29,422 | Recovering Homography from Camera Captured Documents using Convolutional
Neural Networks | cs.CV | Removing perspective distortion from hand held camera captured document
images is one of the primitive tasks in document analysis, but unfortunately,
no such method exists that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a convolutional neural
network bas... | computer science |
29,423 | Exploring Geometric Property Thresholds For Filtering Non-Text Regions
In A Connected Component Based Text Detection Application | cs.CV | Automated text detection is a difficult computer vision task. In order to
accurately detect and identity text in an image or video, two major problems
must be addressed. The primary problem is implementing a robust and reliable
method for distinguishing text vs non-text regions in images and videos. Part
of the difficu... | computer science |
29,424 | Extracting Traffic Primitives Directly from Naturalistically Logged Data
for Self-Driving Applications | cs.CV | Developing an automated vehicle, that can handle the complicated driving
scenarios and appropriately interact with other road users, requires the
ability to semantically learn and understand the driving environment,
oftentimes, based on the analysis of massive amount of naturalistic driving
data. An important paradigm ... | computer science |
29,425 | Real-Time Multiple Object Tracking - A Study on the Importance of Speed | cs.CV | In this project, we implement a multiple object tracker, following the
tracking-by-detection paradigm, as an extension of an existing method. It works
by modelling the movement of objects by solving the filtering problem, and
associating detections with predicted new locations in new frames using the
Hungarian algorith... | computer science |
29,426 | On the definition of Shape Parts: a Dominant Sets Approach | cs.CV | In the present paper a novel graph-based approach to the shape decomposition
problem is addressed. The shape is appropriately transformed into a visibility
graph enriched with local neighborhood information. A two-step diffusion
process is then applied to the visibility graph that efficiently enhances the
information p... | computer science |
29,427 | Holistic, Instance-Level Human Parsing | cs.CV | Object parsing -- the task of decomposing an object into its semantic parts
-- has traditionally been formulated as a category-level segmentation problem.
Consequently, when there are multiple objects in an image, current methods
cannot count the number of objects in the scene, nor can they determine which
part belongs... | computer science |
29,428 | Anti-Makeup: Learning A Bi-Level Adversarial Network for
Makeup-Invariant Face Verification | cs.CV | Makeup is widely used to improve facial attractiveness and is well accepted
by the public. However, different makeup styles will result in significant
facial appearance changes. It remains a challenging problem to match makeup and
non-makeup face images. This paper proposes a learning from generation approach
for makeu... | computer science |
29,429 | Learning Gating ConvNet for Two-Stream based Methods in Action
Recognition | cs.CV | For the two-stream style methods in action recognition, fusing the two
streams' predictions is always by the weighted averaging scheme. This fusion
method with fixed weights lacks of pertinence to different action videos and
always needs trial and error on the validation set. In order to enhance the
adaptability of two... | computer science |
29,430 | Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace
Clustering | cs.CV | Due to the existence of various views or representations in many real-world
data, multi-view learning has drawn much attention recently. Multi-view
spectral clustering methods based on similarity matrixes or graphs are pretty
popular. Generally, these algorithms learn informative graphs by directly
utilizing original d... | computer science |
29,431 | Adversarial Discriminative Heterogeneous Face Recognition | cs.CV | The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This fram... | computer science |
29,432 | Joint Dictionaries for Zero-Shot Learning | cs.CV | A classic approach toward zero-shot learning (ZSL) is to map the input domain
to a set of semantically meaningful attributes that could be used later on to
classify unseen classes of data (e.g. visual data). In this paper, we propose
to learn a visual feature dictionary that has semantically meaningful atoms.
Such dict... | computer science |
29,433 | Automatic Ground Truths: Projected Image Annotations for Omnidirectional
Vision | cs.CV | We present a novel data set made up of omnidirectional video of multiple
objects whose centroid positions are annotated automatically. Omnidirectional
vision is an active field of research focused on the use of spherical imagery
in video analysis and scene understanding, involving tasks such as object
detection, tracki... | computer science |
29,434 | Construction of Latent Descriptor Space and Inference Model of
Hand-Object Interactions | cs.CV | Appearance-based generic object recognition is a challenging problem because
all possible appearances of objects cannot be registered, especially as new
objects are produced every day. Function of objects, however, has a
comparatively small number of prototypes. Therefore, function-based
classification of new objects c... | computer science |
29,435 | Transform Invariant Auto-encoder | cs.CV | The auto-encoder method is a type of dimensionality reduction method. A
mapping from a vector to a descriptor that represents essential information can
be automatically generated from a set of vectors without any supervising
information. However, an image and its spatially shifted version are encoded
into different des... | computer science |
29,436 | Efficient Online Surface Correction for Real-time Large-Scale 3D
Reconstruction | cs.CV | State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for... | computer science |
29,437 | Sparse Representation Based Augmented Multinomial Logistic Extreme
Learning Machine with Weighted Composite Features for Spectral Spatial
Hyperspectral Image Classification | cs.CV | Although extreme learning machine (ELM) has been successfully applied to a
number of pattern recognition problems, it fails to pro-vide sufficient good
results in hyperspectral image (HSI) classification due to two main drawbacks.
The first is due to the random weights and bias of ELM, which may lead to
ill-posed probl... | computer science |
29,438 | Can Deep Neural Networks Match the Related Objects?: A Survey on
ImageNet-trained Classification Models | cs.CV | Deep neural networks (DNNs) have shown the state-of-the-art level of
performances in wide range of complicated tasks. In recent years, the studies
have been actively conducted to analyze the black box characteristics of DNNs
and to grasp the learning behaviours, tendency, and limitations of DNNs. In
this paper, we inve... | computer science |
29,439 | Emotion Recognition in the Wild using Deep Neural Networks and Bayesian
Classifiers | cs.CV | Group emotion recognition in the wild is a challenging problem, due to the
unstructured environments in which everyday life pictures are taken. Some of
the obstacles for an effective classification are occlusions, variable lighting
conditions, and image quality. In this work we present a solution based on a
novel combi... | computer science |
29,440 | ExprGAN: Facial Expression Editing with Controllable Expression
Intensity | cs.CV | Facial expression editing is a challenging task as it needs a high-level
semantic understanding of the input face image. In conventional methods, either
paired training data is required or the synthetic face resolution is low.
Moreover, only the categories of facial expression can be changed. To address
these limitatio... | computer science |
29,441 | A Deep Cascade Network for Unaligned Face Attribute Classification | cs.CV | Humans focus attention on different face regions when recognizing face
attributes. Most existing face attribute classification methods use the whole
image as input. Moreover, some of these methods rely on fiducial landmarks to
provide defined face parts. In this paper, we propose a cascade network that
simultaneously l... | computer science |
29,442 | Improving precision and recall of face recognition in SIPP with
combination of modified mean search and LSH | cs.CV | Although face recognition has been improved much as the development of Deep
Neural Networks, SIPP(Single Image Per Person) problem in face recognition has
not been better solved, especially in practical applications where searching
over complicated database. In this paper, a combination of modified mean search
and LSH ... | computer science |
29,443 | Image Matching: An Application-oriented Benchmark | cs.CV | Image matching approaches have been widely used in computer vision
applications in which the image-level matching performance of matchers is
critical. However, it has not been well investigated by previous works which
place more emphases on evaluating local features. To this end, we present a
uniform benchmark with nov... | computer science |
29,444 | Unsupervised Deep Homography: A Fast and Robust Homography Estimation
Model | cs.CV | Homography estimation between multiple aerial images can provide relative
pose estimation for collaborative autonomous exploration and monitoring. The
usage on a robotic system requires a fast and robust homography estimation
algorithm. In this study, we propose an unsupervised learning algorithm that
trains a Deep Con... | computer science |
29,445 | Bridge the Gap Between Group Sparse Coding and Rank Minimization via
Adaptive Dictionary Learning | cs.CV | Both sparse coding and rank minimization have led to great successes in
various image processing tasks. Though the underlying principles of these two
approaches are similar, no theory is available to demonstrate the
correspondence. In this paper, starting by designing an adaptive dictionary for
each group of image patc... | computer science |
29,446 | Multi-scale Forest Species Recognition Systems for Reduced Cost | cs.CV | This work focuses on cost reduction methods for forest species recognition
systems. Current state-of-the-art shows that the accuracy of these systems have
increased considerably in the past years, but the cost in time to perform the
recognition of input samples has also increased proportionally. For this
reason, in thi... | computer science |
29,447 | Streamlined Deployment for Quantized Neural Networks | cs.CV | Running Deep Neural Network (DNN) models on devices with limited
computational capability is a challenge due to large compute and memory
requirements. Quantized Neural Networks (QNNs) have emerged as a potential
solution to this problem, promising to offer most of the DNN accuracy benefits
with much lower computational... | computer science |
29,448 | Joint Learning of Set Cardinality and State Distribution | cs.CV | We present a novel approach for learning to predict sets using deep learning.
In recent years, deep neural networks have shown remarkable results in computer
vision, natural language processing and other related problems. Despite their
success, traditional architectures suffer from a serious limitation in that
they are... | computer science |
29,449 | Meta Networks for Neural Style Transfer | cs.CV | In this paper we propose a new method to get the specified network parameters
through one time feed-forward propagation of the meta networks and explore the
application to neural style transfer. Recent works on style transfer typically
need to train image transformation networks for every new style, and the style
is en... | computer science |
29,450 | Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories | cs.CV | Sketch is an important media for human to communicate ideas, which reflects
the superiority of human intelligence. Studies on sketch can be roughly
summarized into recognition and generation. Existing models on image
recognition failed to obtain satisfying performance on sketch classification.
But for sketch generation... | computer science |
29,451 | Densely tracking sequences of 3D face scans | cs.CV | 3D face dense tracking aims to find dense inter-frame correspondences in a
sequence of 3D face scans and constitutes a powerful tool for many face
analysis tasks, e.g., 3D dynamic facial expression analysis. The majority of
the existing methods just fit a 3D face surface or model to a 3D target surface
without consider... | computer science |
29,452 | Reading Scene Text with Attention Convolutional Sequence Modeling | cs.CV | Reading text in the wild is a challenging task in the field of computer
vision. Existing approaches mainly adopted Connectionist Temporal
Classification (CTC) or Attention models based on Recurrent Neural Network
(RNN), which is computationally expensive and hard to train. In this paper, we
present an end-to-end Attent... | computer science |
29,453 | GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval | cs.CV | The huge variance of human pose and the misalignment of detected human images
significantly increase the difficulty of person Re-Identification (Re-ID).
Moreover, efficient Re-ID systems are required to cope with the massive visual
data being produced by video surveillance systems. Targeting to solve these
problems, th... | computer science |
29,454 | End-to-End Audiovisual Fusion with LSTMs | cs.CV | Several end-to-end deep learning approaches have been recently presented
which simultaneously extract visual features from the input images and perform
visual speech classification. However, research on jointly extracting audio and
visual features and performing classification is very limited. In this work, we
present ... | computer science |
29,455 | Flexible Network Binarization with Layer-wise Priority | cs.CV | How to effectively approximate real-valued parameters with binary codes plays
a central role in neural network binarization. In this work, we reveal an
important fact that binarizing different layers has a widely-varied effect on
the compression ratio of network and the loss of performance. Based on this
fact, we propo... | computer science |
29,456 | Zoom Out-and-In Network with Map Attention Decision for Region Proposal
and Object Detection | cs.CV | In this paper, we propose a zoom-out-and-in network for generating object
proposals. A key observation is that it is difficult to classify anchors of
different sizes with the same set of features. Anchors of different sizes
should be placed accordingly based on different depth within a network: smaller
boxes on high-re... | computer science |
29,457 | An Efficient Evolutionary Based Method For Image Segmentation | cs.CV | The goal of this paper is to present a new efficient image segmentation
method based on evolutionary computation which is a model inspired from human
behavior. Based on this model, a four layer process for image segmentation is
proposed using the split/merge approach. In the first layer, an image is split
into numerous... | computer science |
29,458 | Exploiting skeletal structure in computer vision annotation with Benders
decomposition | cs.CV | Many annotation problems in computer vision can be phrased as integer linear
programs (ILPs). The use of standard industrial solvers does not to exploit the
underlying structure of such problems eg, the skeleton in pose estimation. The
leveraging of the underlying structure in conjunction with industrial solvers
promis... | computer science |
29,459 | An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR
Image Segmentation | cs.CV | Accurate segmentation of the heart is an important step towards evaluating
cardiac function. In this paper, we present a fully automated framework for
segmentation of the left (LV) and right (RV) ventricular cavities and the
myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and
3D convolutiona... | computer science |
29,460 | Recurrent Saliency Transformation Network: Incorporating Multi-Stage
Visual Cues for Small Organ Segmentation | cs.CV | We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used pre... | computer science |
29,461 | DeepVoting: An Explainable Framework for Semantic Part Detection under
Partial Occlusion | cs.CV | In this paper, we study the task of detecting semantic parts of an object.
This is very important in computer vision, as it provides the possibility to
parse an object as human do, and helps us better understand object detection
algorithms. Also, detecting semantic parts is very challenging especially when
the parts ar... | computer science |
29,462 | A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping | cs.CV | Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. ... | computer science |
29,463 | Learning to Segment Instances in Videos with Spatial Propagation Network | cs.CV | We propose a deep learning-based framework for instance-level object
segmentation. Our method mainly consists of three steps. First, We train a
generic model based on ResNet-101 for foreground/background segmentations.
Second, based on this generic model, we fine-tune it to learn instance-level
models and segment indiv... | computer science |
29,464 | Learning Multi-frame Visual Representation for Joint Detection and
Tracking of Small Objects | cs.CV | Deep convolutional and recurrent neural networks have delivered significant
advancements in object detection and tracking. However, current models handle
detection and tracking through separate networks, and deep-learning-based joint
detection and tracking has not yet been explored despite its potential benefits
to bot... | computer science |
29,465 | Unsupervised object discovery for instance recognition | cs.CV | Severe background clutter is challenging in many computer vision tasks,
including large-scale image retrieval. Global descriptors, that are popular due
to their memory and search efficiency, are especially prone to corruption by
such a clutter. Eliminating the impact of the clutter on the image descriptor
increases the... | computer science |
29,466 | Binary-decomposed DCNN for accelerating computation and compressing
model without retraining | cs.CV | Recent trends show recognition accuracy increasing even more profoundly.
Inference process of Deep Convolutional Neural Networks (DCNN) has a large
number of parameters, requires a large amount of computation, and can be very
slow. The large number of parameters also require large amounts of memory. This
is resulting i... | computer science |
29,467 | Exploring Food Detection using CNNs | cs.CV | One of the most common critical factors directly related to the cause of a
chronic disease is unhealthy diet consumption. In this sense, building an
automatic system for food analysis could allow a better understanding of the
nutritional information with respect to the food eaten and thus it could help
in taking correc... | computer science |
29,468 | MODNet: Moving Object Detection Network with Motion and Appearance for
Autonomous Driving | cs.CV | We propose a novel multi-task learning system that combines appearance and
motion cues for a better semantic reasoning of the environment. A unified
architecture for joint vehicle detection and motion segmentation is introduced.
In this architecture, a two-stream encoder is shared among both tasks. In order
to evaluate... | computer science |
29,469 | Food Recognition using Fusion of Classifiers based on CNNs | cs.CV | With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accu... | computer science |
29,470 | Benchmarking Super-Resolution Algorithms on Real Data | cs.CV | Over the past decades, various super-resolution (SR) techniques have been
developed to enhance the spatial resolution of digital images. Despite the
great number of methodical contributions, there is still a lack of comparative
validations of SR under practical conditions, as capturing real ground truth
data is a chall... | computer science |
29,471 | ImageNet Training in Minutes | cs.CV | Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU
takes 14 days. This training requires 10^18 single precision operations in
total. On the other hand, the world's current fastest supercomputer can finish
2 * 10^17 single precision operations per second (Dongarra et al 2017,
https://www.top500.o... | computer science |
29,472 | Feature-Fused SSD: Fast Detection for Small Objects | cs.CV | Small objects detection is a challenging task in computer vision due to its
limited resolution and information. In order to solve this problem, the
majority of existing methods sacrifice speed for improvement in accuracy. In
this paper, we aim to detect small objects at a fast speed, using the best
object detector Sing... | computer science |
29,473 | Asian Stamps Identification and Classification System | cs.CV | In this paper, we address the problem of stamp recognition. The goal is to
classify a given stamp to a certain country and also identify the year it is
published. We propose a new approach for stamp recognition based on describing
a given stamp image using color information and texture information. For color
informatio... | computer science |
29,474 | Joint Hierarchical Category Structure Learning and Large-Scale Image
Classification | cs.CV | We investigate the scalable image classification problem with a large number
of categories. Hierarchical visual data structures are helpful for improving
the efficiency and performance of large-scale multi-class classification. We
propose a novel image classification method based on learning hierarchical
inter-class st... | computer science |
29,475 | Robust Kernelized Multi-View Self-Representations for Clustering by
Tensor Multi-Rank Minimization | cs.CV | Most recently, tensor-SVD is implemented on multi-view self-representation
clustering and has achieved the promising results in many real-world
applications such as face clustering, scene clustering and generic object
clustering. However, tensor-SVD based multi-view self-representation clustering
is proposed originally... | computer science |
29,476 | Viewpoint Invariant Action Recognition using RGB-D Videos | cs.CV | In video-based action recognition, viewpoint variations often pose major
challenges because the same actions can appear different from different views.
We use the complementary RGB and Depth information from the RGB-D cameras to
address this problem. The proposed technique capitalizes on the spatio-temporal
information... | computer science |
29,477 | Multi-scale Deep Learning Architectures for Person Re-identification | cs.CV | Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location ... | computer science |
29,478 | Masquer Hunter: Adversarial Occlusion-aware Face Detection | cs.CV | Occluded face detection is a challenging detection task due to the large
appearance variations incurred by various real-world occlusions. This paper
introduces an Adversarial Occlusion-aware Face Detector (AOFD) by
simultaneously detecting occluded faces and segmenting occluded areas.
Specifically, we employ an adversa... | computer science |
29,479 | Detecting Faces Using Region-based Fully Convolutional Networks | cs.CV | Face detection has achieved great success using the region-based methods. In
this report, we propose a region-based face detector applying deep networks in
a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully
Convolutional Networks (R-FCN), our face detector is more accurate and
computational ef... | computer science |
29,480 | Correlating Satellite Cloud Cover with Sky Cameras | cs.CV | The role of clouds is manifold in understanding the various events in the
atmosphere, and also in studying the radiative balance of the earth. The
conventional manner of such cloud analysis is performed mainly via satellite
images. However, because of its low temporal- and spatial- resolutions,
ground-based sky cameras... | computer science |
29,481 | Top-Down Saliency Detection Driven by Visual Classification | cs.CV | This paper presents an approach for top-down saliency detection guided by
visual classification tasks. We first learn how to compute visual saliency when
a specific visual task has to be accomplished, as opposed to most
state-of-the-art methods which assess saliency merely through bottom-up
principles. Afterwards, we i... | computer science |
29,482 | Video Synopsis Generation Using Spatio-Temporal Groups | cs.CV | Millions of surveillance cameras operate at 24x7 generating huge amount of
visual data for processing. However, retrieval of important activities from
such a large data can be time consuming. Thus, researchers are working on
finding solutions to present hours of visual data in a compressed, but
meaningful way. Video sy... | computer science |
29,483 | Cystoid macular edema segmentation of Optical Coherence Tomography
images using fully convolutional neural networks and fully connected CRFs | cs.CV | In this paper we present a new method for cystoid macular edema (CME)
segmentation in retinal Optical Coherence Tomography (OCT) images, using a
fully convolutional neural network (FCN) and a fully connected conditional
random fields (dense CRFs). As a first step, the framework trains the FCN model
to extract features ... | computer science |
29,484 | Zero-Shot Learning to Manage a Large Number of Place-Specific
Compressive Change Classifiers | cs.CV | With recent progress in large-scale map maintenance and long-term map
learning, the task of change detection on a large-scale map from a visual image
captured by a mobile robot has become a problem of increasing criticality.
Previous approaches for change detection are typically based on image
differencing and require ... | computer science |
29,485 | NIMA: Neural Image Assessment | cs.CV | Automatically learned quality assessment for images has recently become a hot
topic due to its usefulness in a wide variety of applications such as
evaluating image capture pipelines, storage techniques and sharing media.
Despite the subjective nature of this problem, most existing methods only
predict the mean opinion... | computer science |
29,486 | Long-Term Ensemble Learning of Visual Place Classifiers | cs.CV | This paper addresses the problem of cross-season visual place classification
(VPC) from a novel perspective of long-term map learning. Our goal is to enable
transfer learning efficiently from one season to the next, at a small constant
cost, and without wasting the robot's available long-term-memory by memorizing
very ... | computer science |
29,487 | The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in
Retinal Images | cs.CV | Enhancement, followed by segmentation, quantification and modelling, of blood
vessels in retinal images plays an essential role in computer-aid retinopathy
diagnosis. In this paper, we introduce a new vessel enhancement method which is
the bowler-hat transform based on mathematical morphology. The proposed method
combi... | computer science |
29,488 | An Improved Fatigue Detection System Based on Behavioral Characteristics
of Driver | cs.CV | In recent years, road accidents have increased significantly. One of the
major reasons for these accidents, as reported is driver fatigue. Due to
continuous and longtime driving, the driver gets exhausted and drowsy which may
lead to an accident. Therefore, there is a need for a system to measure the
fatigue level of d... | computer science |
29,489 | Organizing Multimedia Data in Video Surveillance Systems Based on Face
Verification with Convolutional Neural Networks | cs.CV | In this paper we propose the two-stage approach of organizing information in
video surveillance systems. At first, the faces are detected in each frame and
a video stream is split into sequences of frames with face region of one
person. Secondly, these sequences (tracks) that contain identical faces are
grouped using f... | computer science |
29,490 | Facial Feature Tracking under Varying Facial Expressions and Face Poses
based on Restricted Boltzmann Machines | cs.CV | Facial feature tracking is an active area in computer vision due to its
relevance to many applications. It is a nontrivial task, since faces may have
varying facial expressions, poses or occlusions. In this paper, we address this
problem by proposing a face shape prior model that is constructed based on the
Restricted ... | computer science |
29,491 | A Hierarchical Probabilistic Model for Facial Feature Detection | cs.CV | Facial feature detection from facial images has attracted great attention in
the field of computer vision. It is a nontrivial task since the appearance and
shape of the face tend to change under different conditions. In this paper, we
propose a hierarchical probabilistic model that could infer the true locations
of fac... | computer science |
29,492 | Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution
from a Blurred Image Sequence | cs.CV | The conventional methods for estimating camera poses and scene structures
from severely blurry or low resolution images often result in failure. The
off-the-shelf deblurring or super-resolution methods may show visually pleasing
results. However, applying each technique independently before matching is
generally unprof... | computer science |
29,493 | Where to Focus: Deep Attention-based Spatially Recurrent Bilinear
Networks for Fine-Grained Visual Recognition | cs.CV | Fine-grained visual recognition typically depends on modeling subtle
difference from object parts. However, these parts often exhibit dramatic
visual variations such as occlusions, viewpoints, and spatial transformations,
making it hard to detect. In this paper, we present a novel attention-based
model to automatically... | computer science |
29,494 | Social Style Characterization from Egocentric Photo-streams | cs.CV | This paper proposes a system for automatic social pattern characterization
using a wearable photo-camera. The proposed pipeline consists of three major
steps. First, detection of people with whom the camera wearer interacts and,
second, categorization of the detected social interactions into formal and
informal. These ... | computer science |
29,495 | StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection | cs.CV | One-stage object detectors such as SSD or YOLO already have shown promising
accuracy with small memory footprint and fast speed. However, it is widely
recognized that one-stage detectors have difficulty in detecting small objects
while they are competitive with two-stage methods on large objects. In this
paper, we inve... | computer science |
29,496 | Direct Pose Estimation with a Monocular Camera | cs.CV | We present a direct method to calculate a 6DoF pose change of a monocular
camera for mobile navigation. The calculated pose is estimated up to a constant
unknown scale parameter that is kept constant over the entire reconstruction
process. This method allows a direct cal- culation of the metric position and
rotation wi... | computer science |
29,497 | Beyond SIFT using Binary features for Loop Closure Detection | cs.CV | In this paper a binary feature based Loop Closure Detection (LCD) method is
proposed, which for the first time achieves higher precision-recall (PR)
performance compared with state-of-the-art SIFT feature based approaches. The
proposed system originates from our previous work Multi-Index hashing for Loop
closure Detect... | computer science |
29,498 | Microscopy Cell Segmentation via Adversarial Neural Networks | cs.CV | We present a novel method for cell segmentation in microscopy images which is
inspired by the Generative Adversarial Neural Network (GAN) approach. Our
framework is built on a pair of two competitive artificial neural networks,
with a unique architecture, termed Rib Cage, which are trained simultaneously
and together d... | computer science |
29,499 | Combinational neural network using Gabor filters for the classification
of handwritten digits | cs.CV | A classification algorithm that combines the components of k-nearest
neighbours and multilayer neural networks has been designed and tested. With
this method the computational time required for training the dataset has been
reduced substancially. Gabor filters were used for the feature extraction to
ensure a better per... | computer science |
29,500 | E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional
Neural Network | cs.CV | Traditional Bag-of-visual Words (BoWs) model is commonly generated with many
steps including local feature extraction, codebook generation, and feature
quantization, etc. Those steps are relatively independent with each other and
are hard to be jointly optimized. Moreover, the dependency on hand-crafted
local feature m... | computer science |
29,501 | Multi-Task Learning for Segmentation of Building Footprints with Deep
Neural Networks | cs.CV | The increased availability of high resolution satellite imagery allows to
sense very detailed structures on the surface of our planet. Access to such
information opens up new directions in the analysis of remote sensing imagery.
However, at the same time this raises a set of new challenges for existing
pixel-based pred... | computer science |
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