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31,302 | Brain Tumor Type Classification via Capsule Networks | cs.CV | Brain tumor is considered as one of the deadliest and most common form of
cancer both in children and in adults. Consequently, determining the correct
type of brain tumor in early stages is of significant importance to devise a
precise treatment plan and predict patient's response to the adopted treatment.
In this rega... | computer science |
31,303 | Improved Explainability of Capsule Networks: Relevance Path by Agreement | cs.CV | Recent advancements in signal processing and machine learning domains have
resulted in an extensive surge of interest in deep learning models due to their
unprecedented performance and high accuracy for different and challenging
problems of significant engineering importance. However, when such deep
learning architectu... | computer science |
31,304 | Neural Aesthetic Image Reviewer | cs.CV | Recently, there is a rising interest in perceiving image aesthetics. The
existing works deal with image aesthetics as a classification or regression
problem. To extend the cognition from rating to reasoning, a deeper
understanding of aesthetics should be based on revealing why a high- or
low-aesthetic score should be a... | computer science |
31,305 | IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully
Residual Convolutional-Deconvolutional Network | cs.CV | In this paper we tackle a very novel problem, namely height estimation from a
single monocular remote sensing image, which is inherently ambiguous, and a
technically ill-posed problem, with a large source of uncertainty coming from
the overall scale. We propose a fully convolutional-deconvolutional network
architecture... | computer science |
31,306 | Joint Event Detection and Description in Continuous Video Streams | cs.CV | As a fine-grained video understanding task, dense video captioning involves
first localizing events in a video and then generating captions for the
identified events. We present the Joint Event Detection and Description Network
(JEDDi-Net) that solves the dense captioning task in an end-to-end fashion. Our
model contin... | computer science |
31,307 | $L_p$-Norm Constrained Coding With Frank-Wolfe Network | cs.CV | We investigate the problem of $L_p$-norm constrained coding, i.e. converting
signal into code that lies inside the $L_p$-ball and most faithfully
reconstructs the signal. While previous works known as sparse coding have
addressed the cases of $\ell_0$ "norm" and $L_1$-norm, more general cases with
other $p$ values, esp... | computer science |
31,308 | A Model for Medical Diagnosis Based on Plantar Pressure | cs.CV | The process of determining which disease or condition explains a person's
symptoms and signs can be very complicated and may be inaccurate in some cases.
The general belief is that diagnosing diseases relies on doctors' keen
intuition, rich experience and professional equipment. In this work, we employ
ideas from recen... | computer science |
31,309 | Learning to Adapt Structured Output Space for Semantic Segmentation | cs.CV | Convolutional neural network-based approaches for semantic segmentation rely
on supervision with pixel-level ground truth, but may not generalize well to
unseen image domains. As the labeling process is tedious and labor intensive,
developing algorithms that can adapt source ground truth labels to the target
domain is ... | computer science |
31,310 | Compressing Neural Networks using the Variational Information Bottleneck | cs.CV | Neural networks can be compressed to reduce memory and computational
requirements, or to increase accuracy by facilitating the use of a larger base
architecture. In this paper we focus on pruning individual neurons, which can
simultaneously trim model size, FLOPs, and run-time memory. To improve upon the
performance of... | computer science |
31,311 | Convolutional Neural Networks with Alternately Updated Clique | cs.CV | Improving information flow in deep networks helps to ease the training
difficulties and utilize parameters more efficiently. Here we propose a new
convolutional neural network architecture with alternately updated clique
(CliqueNet). In contrast to prior networks, there are both forward and backward
connections between... | computer science |
31,312 | Fine-grained wound tissue analysis using deep neural network | cs.CV | Tissue assessment for chronic wounds is the basis of wound grading and
selection of treatment approaches. While several image processing approaches
have been proposed for automatic wound tissue analysis, there has been a
shortcoming in these approaches for clinical practices. In particular,
seemingly, all previous appr... | computer science |
31,313 | A Simple Method to improve Initialization Robustness for Active Contours
driven by Local Region Fitting Energy | cs.CV | Active contour models based on local region fitting energy can segment images
with intensity inhomogeneity effectively, but their segmentation results are
easy to error if the initial contour is inappropriate. In this paper, we
present a simple and universal method of improving the robustness of initial
contour for the... | computer science |
31,314 | HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image | cs.CV | With the development of deep learning, the performance of hyperspectral image
(HSI) classification has been greatly improved in recent years. The shortage of
training samples has become a bottleneck for further improvement of
performance. In this paper, we propose a novel convolutional neural network
framework for the ... | computer science |
31,315 | Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution
to the BRATS 2017 Challenge | cs.CV | Quantitative analysis of brain tumors is critical for clinical decision
making. While manual segmentation is tedious, time consuming and subjective,
this task is at the same time very challenging to solve for automatic
segmentation methods. In this paper we present our most recent effort on
developing a robust segmenta... | computer science |
31,316 | Retrieval and Registration of Long-Range Overlapping Frames for Scalable
Mosaicking of In Vivo Fetoscopy | cs.CV | Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome
consists in the photo-coagulation of undesired anastomoses located on the
placenta which are responsible to a blood transfer between the two twins. While
being the standard of care procedure, fetoscopy suffers from a limited
field-of-view of ... | computer science |
31,317 | Novelty Detection with GAN | cs.CV | The ability of a classifier to recognize unknown inputs is important for many
classification-based systems. We discuss the problem of simultaneous
classification and novelty detection, i.e. determining whether an input is from
the known set of classes and from which specific class, or from an unknown
domain and does no... | computer science |
31,318 | Stereoscopic Neural Style Transfer | cs.CV | This paper presents the first attempt at stereoscopic neural style transfer,
which responds to the emerging demand for 3D movies or AR/VR. We start with a
careful examination of applying existing monocular style transfer methods to
left and right views of stereoscopic images separately. This reveals that the
original d... | computer science |
31,319 | Speeding Up the Bilateral Filter: A Joint Acceleration Way | cs.CV | Computational complexity of the brute-force implementation of the bilateral
filter (BF) depends on its filter kernel size. To achieve the constant-time BF
whose complexity is irrelevant to the kernel size, many techniques have been
proposed, such as 2D box filtering, dimension promotion, and shiftability
property. Alth... | computer science |
31,320 | Hardware-Efficient Guided Image Filtering For Multi-Label Problem | cs.CV | The Guided Filter (GF) is well-known for its linear complexity. However, when
filtering an image with an n-channel guidance, GF needs to invert an n x n
matrix for each pixel. To the best of our knowledge existing matrix inverse
algorithms are inefficient on current hardwares. This shortcoming limits
applications of mu... | computer science |
31,321 | A Feature Clustering Approach Based on Histogram of Oriented Optical
Flow and Superpixels | cs.CV | Visual feature clustering is one of the cost-effective approaches to segment
objects in videos. However, the assumptions made for developing the existing
algorithms prevent them from being used in situations like segmenting an
unknown number of static and moving objects under heavy camera movements. This
paper addresse... | computer science |
31,322 | A Retinal Image Enhancement Technique for Blood Vessel Segmentation
Algorithm | cs.CV | The morphology of blood vessels in retinal fundus images is an important
indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The
accuracy of retinal blood vessels segmentation affects the quality of retinal
image analysis which is used in diagnosis methods in modern ophthalmology.
Contrast enhan... | computer science |
31,323 | Invariant properties of a locally salient dither pattern with a
spatial-chromatic histogram | cs.CV | Compacted Dither Pattern Code (CDPC) is a recently found feature which is
successful in irregular shapes based visual depiction. Locally salient dither
pattern feature is an attempt to expand the capability of CDPC for both regular
and irregular shape based visual depiction. This paper presents an analysis of
rotationa... | computer science |
31,324 | Super-Efficient Spatially Adaptive Contrast Enhancement Algorithm for
Superficial Vein Imaging | cs.CV | This paper presents a super-efficient spatially adaptive contrast enhancement
algorithm for enhancing infrared (IR) radiation based superficial vein images
in real-time. The super-efficiency permits the algorithm to run in
consumer-grade handheld devices, which ultimately reduces the cost of vein
imaging equipment. The... | computer science |
31,325 | Joint Pixel and Feature-level Domain Adaptation in the Wild | cs.CV | Recent developments in deep domain adaptation have allowed knowledge transfer
from a labeled source domain to an unlabeled target domain at the level of
intermediate features or input pixels. We propose that advantages may be
derived by combining them, in the form of different insights that lead to a
novel design and c... | computer science |
31,326 | Chinese Text in the Wild | cs.CV | We introduce Chinese Text in the Wild, a very large dataset of Chinese text
in street view images. While optical character recognition (OCR) in document
images is well studied and many commercial tools are available, detection and
recognition of text in natural images is still a challenging problem,
especially for more... | computer science |
31,327 | Ring loss: Convex Feature Normalization for Face Recognition | cs.CV | We motivate and present Ring loss, a simple and elegant feature normalization
approach for deep networks designed to augment standard loss functions such as
Softmax. We argue that deep feature normalization is an important aspect of
supervised classification problems where we require the model to represent each
class i... | computer science |
31,328 | A Class-Incremental Learning Method Based on One Class Support Vector
Machine | cs.CV | A method based on one class support vector machine (OCSVM) is proposed for
class incremental learning. Several OCSVM models divide the input space into
several parts. Then, the 1VS1 classifiers are constructed for the confuse part
by using the support vectors. During the class incremental learning process,
the OCSVM of... | computer science |
31,329 | Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian
Perspective | cs.CV | This paper addresses the task of dense non-rigid structure from motion
(NRSfM) using multiple images. State-of-the-art methods to this problem are
often hurdled by scalability, expensive computations, and noisy measurements.
Further, recent methods to NRSfM usually either assume a small number of sparse
feature points ... | computer science |
31,330 | Detecting Volcano Deformation in InSAR using Deep learning | cs.CV | Globally 800 million people live within 100 km of a volcano and currently
1500 volcanoes are considered active, but half of these have no ground-based
monitoring. Alternatively, satellite radar (InSAR) can be employed to observe
volcanic ground deformation, which has shown a significant statistical link to
eruptions. M... | computer science |
31,331 | MAGAN: Aligning Biological Manifolds | cs.CV | It is increasingly common in many types of natural and physical systems
(especially biological systems) to have different types of measurements
performed on the same underlying system. In such settings, it is important to
align the manifolds arising from each measurement in order to integrate such
data and gain an impr... | computer science |
31,332 | Context-Aware Learning using Transferable Features for Classification of
Breast Cancer Histology Images | cs.CV | Convolutional neural networks (CNNs) have been recently used for a variety of
histology image analysis. However, availability of a large dataset is a major
prerequisite for training a CNN which limits its use by the computational
pathology community. In previous studies, CNNs have demonstrated their
potential in terms ... | computer science |
31,333 | Learning Filter Scale and Orientation In CNNs | cs.CV | Convolutional neural networks have many hyperparameters such as the filter
size, number of filters, and pooling size, which require manual tuning. Though
deep stacked structures are able to create multi-scale and hierarchical
representations, manually fixed filter sizes limit the scale of representations
that can be le... | computer science |
31,334 | Poisson Image Denoising Using Best Linear Prediction: A Post-processing
Framework | cs.CV | In this paper, we address the problem of denoising images degraded by Poisson
noise. We propose a new patch-based approach based on best linear prediction to
estimate the underlying clean image. A simplified prediction formula is derived
for Poisson observations, which requires the covariance matrix of the
underlying c... | computer science |
31,335 | Image Dataset for Visual Objects Classification in 3D Printing | cs.CV | The rapid development in additive manufacturing (AM), also known as 3D
printing, has brought about potential risk and security issues along with
significant benefits. In order to enhance the security level of the 3D printing
process, the present research aims to detect and recognize illegal components
using deep learni... | computer science |
31,336 | Robust positioning of drones for land use monitoring in strong terrain
relief using vision-based navigation | cs.CV | For land use monitoring, the main problems are robust positioning in urban
canyons and strong terrain reliefs with the use of GPS system only. Indeed,
satellite signal reflection and shielding in urban canyons and strong terrain
relief results in problems with correct positioning. Using GNSS-RTK does not
solve the prob... | computer science |
31,337 | Unravelling Robustness of Deep Learning based Face Recognition Against
Adversarial Attacks | cs.CV | Deep neural network (DNN) architecture based models have high expressive
power and learning capacity. However, they are essentially a black box method
since it is not easy to mathematically formulate the functions that are learned
within its many layers of representation. Realizing this, many researchers have
started t... | computer science |
31,338 | DeepDefense: Training Deep Neural Networks with Improved Robustness | cs.CV | Despite the efficacy on a variety of computer vision tasks, deep neural
networks (DNNs) are vulnerable to adversarial attacks, limiting their
applications in security-critical systems. Recent works have shown the
possibility of generating imperceptibly perturbed image inputs (a.k.a.,
adversarial examples) to fool well-... | computer science |
31,339 | Left ventricle segmentation By modelling uncertainty in prediction of
deep convolutional neural networks and adaptive thresholding inference | cs.CV | Deep neural networks have shown great achievements in solving complex
problems. However, there are fundamental problems that limit their real world
applications. Lack of measurable criteria for estimating uncertainty in the
network outputs is one of these problems. In this paper, we address this
limitation by introduci... | computer science |
31,340 | Graph Kernels based on High Order Graphlet Parsing and Hashing | cs.CV | Graph-based methods are known to be successful in many machine learning and
pattern classification tasks. These methods consider semi-structured data as
graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives.
However, these ... | computer science |
31,341 | LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images | cs.CV | We propose an end-to-end architecture for joint 2D and 3D human pose
estimation in natural images. Key to our approach is the generation and scoring
of a number of pose proposals per image, which allows us to predict 2D and 3D
poses of multiple people simultaneously. Hence, our approach does not require
an approximate ... | computer science |
31,342 | The 2018 DAVIS Challenge on Video Object Segmentation | cs.CV | We present the 2018 DAVIS Challenge on Video Object Segmentation, a public
competition specifically designed for the task of video object segmentation. It
builds upon the DAVIS 2017 dataset, which was presented in the previous edition
of the DAVIS Challenge, and added 100 videos with multiple objects per sequence
to th... | computer science |
31,343 | SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis | cs.CV | Breast cancer is the second leading cause of cancer death among women
worldwide. Nevertheless, it is also one of the most treatable malignances if
detected early. Screening for breast cancer with digital mammography (DM) has
been widely used. However it demonstrates limited sensitivity for women with
dense breasts. An ... | computer science |
31,344 | Contained Neural Style Transfer for Decorated Logo Generation | cs.CV | Making decorated logos requires image editing skills, without sufficient
skills, it could be a time-consuming task. While there are many on-line web
services to make new logos, they have limited designs and duplicates can be
made. We propose using neural style transfer with clip art and text for the
creation of new and... | computer science |
31,345 | Aspl{ü}nd's metric defined in the Logarithmic Image Processing (LIP)
framework for colour and multivariate images | cs.CV | Aspl{\"u}nd's metric, which is useful for pattern matching, consists in a
double-sided probing, i.e. the over-graph and the sub-graph of a function are
probed jointly. It has previously been defined for grey-scale images using the
Logarithmic Image Processing (LIP) framework. LIP is a non-linear model to
perform operat... | computer science |
31,346 | Deep Unsupervised Intrinsic Image Decomposition by Siamese Training | cs.CV | We harness modern intrinsic decomposition tools based on deep learning to
increase their applicability on realworld use cases. Traditional techniques are
derived from the Retinex theory: handmade prior assumptions constrain an
optimization to yield a unique solution that is qualitatively satisfying on a
limited set of ... | computer science |
31,347 | Pose-Robust Face Recognition via Deep Residual Equivariant Mapping | cs.CV | Face recognition achieves exceptional success thanks to the emergence of deep
learning. However, many contemporary face recognition models still perform
relatively poor in processing profile faces compared to frontal faces. A key
reason is that the number of frontal and profile training faces are highly
imbalanced - th... | computer science |
31,348 | Monocular Depth Estimation using Multi-Scale Continuous CRFs as
Sequential Deep Networks | cs.CV | Depth cues have been proved very useful in various computer vision and
robotic tasks. This paper addresses the problem of monocular depth estimation
from a single still image. Inspired by the effectiveness of recent works on
multi-scale convolutional neural networks (CNN), we propose a deep model which
fuses complement... | computer science |
31,349 | Tree Species Identification from Bark Images Using Convolutional Neural
Networks | cs.CV | Tree species identification using images of the bark is a challenging problem
that could help in tasks such as drone navigation in forest environment and
autonomous forest inventory management. It also brings more value to harvesting
operations as it leads to greater market values of trees. While the recent
progress in... | computer science |
31,350 | Multimodal Registration of Retinal Images Using Domain-Specific
Landmarks and Vessel Enhancement | cs.CV | The analysis of different image modalities is frequently performed in
ophthalmology as they provide complementary information for the diagnosis and
follow-up of relevant diseases, like hypertension or diabetes. This work
presents an hybrid method for the multimodal registration of color fundus
retinography and fluoresc... | computer science |
31,351 | Hashing with Mutual Information | cs.CV | Binary vector embeddings enable fast nearest neighbor retrieval in large
databases of high-dimensional objects, and play an important role in many
practical applications, such as image and video retrieval. We study the problem
of learning binary vector embeddings under a supervised setting, also known as
hashing. We pr... | computer science |
31,352 | High-Dynamic-Range Imaging for Cloud Segmentation | cs.CV | Sky/cloud images obtained from ground-based sky-cameras are usually captured
using a fish-eye lens with a wide field of view. However, the sky exhibits a
large dynamic range in terms of luminance, more than a conventional camera can
capture. It is thus difficult to capture the details of an entire scene with a
regular ... | computer science |
31,353 | Focal Loss Dense Detector for Vehicle Surveillance | cs.CV | Deep learning has been widely recognized as a promising approach in different
computer vision applications. Specifically, one-stage object detector and
two-stage object detector are regarded as the most important two groups of
Convolutional Neural Network based object detection methods. One-stage object
detector could ... | computer science |
31,354 | Real-Time Deep Learning Method for Abandoned Luggage Detection in Video | cs.CV | Recent terrorist attacks in major cities around the world have brought many
casualties among innocent citizens. One potential threat is represented by
abandoned luggage items (that could contain bombs or biological warfare) in
public areas. In this paper, we describe an approach for real-time automatic
detection of aba... | computer science |
31,355 | The History Began from AlexNet: A Comprehensive Survey on Deep Learning
Approaches | cs.CV | Deep learning has demonstrated tremendous success in variety of application
domains in the past few years. This new field of machine learning has been
growing rapidly and applied in most of the application domains with some new
modalities of applications, which helps to open new opportunity. There are
different methods... | computer science |
31,356 | Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep
Learning | cs.CV | Semantic segmentation of robotic instruments is an important problem for the
robot-assisted surgery. One of the main challenges is to correctly detect an
instrument's position for the tracking and pose estimation in the vicinity of
surgical scenes. Accurate pixel-wise instrument segmentation is needed to
address this c... | computer science |
31,357 | A Benchmark for Iris Location and a Deep Learning Detector Evaluation | cs.CV | The iris is considered as the biometric trait with the highest unique
probability. The iris location is an important task for biometrics systems,
affecting directly the results obtained in specific applications such as iris
recognition, spoofing and contact lenses detection, among others. This work
defines the iris loc... | computer science |
31,358 | Unsupervised Learning of Face Representations | cs.CV | We present an approach for unsupervised training of CNNs in order to learn
discriminative face representations. We mine supervised training data by noting
that multiple faces in the same video frame must belong to different persons
and the same face tracked across multiple frames must belong to the same
person. We obta... | computer science |
31,359 | Egocentric Basketball Motion Planning from a Single First-Person Image | cs.CV | We present a model that uses a single first-person image to generate an
egocentric basketball motion sequence in the form of a 12D camera configuration
trajectory, which encodes a player's 3D location and 3D head orientation
throughout the sequence. To do this, we first introduce a future convolutional
neural network (... | computer science |
31,360 | Less Is More: Picking Informative Frames for Video Captioning | cs.CV | In video captioning task, the best practice has been achieved by
attention-based models which associate salient visual components with sentences
in the video. However, existing study follows a common procedure which includes
a frame-level appearance modeling and motion modeling on equal interval frame
sampling, which m... | computer science |
31,361 | Cross-Paced Representation Learning with Partial Curricula for
Sketch-based Image Retrieval | cs.CV | In this paper we address the problem of learning robust cross-domain
representations for sketch-based image retrieval (SBIR). While most SBIR
approaches focus on extracting low- and mid-level descriptors for direct
feature matching, recent works have shown the benefit of learning coupled
feature representations to desc... | computer science |
31,362 | A new stereo formulation not using pixel and disparity models | cs.CV | We introduce a new stereo formulation which does not use pixel and disparity
models. Many problems in vision are treated as assigning each pixel a label.
Disparities are labels for stereo. Such pixel-labeling problems are naturally
represented in terms of energy minimization, where the energy function has two
terms: on... | computer science |
31,363 | LSTD: A Low-Shot Transfer Detector for Object Detection | cs.CV | Recent advances in object detection are mainly driven by deep learning with
large-scale detection benchmarks. However, the fully-annotated training set is
often limited for a target detection task, which may deteriorate the
performance of deep detectors. To address this challenge, we propose a novel
low-shot transfer d... | computer science |
31,364 | Learning-Based Dequantization For Image Restoration Against Extremely
Poor Illumination | cs.CV | All existing image enhancement methods, such as HDR tone mapping, cannot
recover A/D quantization losses due to insufficient or excessive lighting,
(underflow and overflow problems). The loss of image details due to A/D
quantization is complete and it cannot be recovered by traditional image
processing methods, but the... | computer science |
31,365 | Path Aggregation Network for Instance Segmentation | cs.CV | The way that information propagates in neural networks is of great
importance. In this paper, we propose Path Aggregation Network (PANet) aiming
at boosting information flow in proposal-based instance segmentation framework.
Specifically, we enhance the entire feature hierarchy with accurate
localization signals in low... | computer science |
31,366 | Relocalization, Global Optimization and Map Merging for Monocular
Visual-Inertial SLAM | cs.CV | The monocular visual-inertial system (VINS), which consists one camera and
one low-cost inertial measurement unit (IMU), is a popular approach to achieve
accurate 6-DOF state estimation. However, such locally accurate visual-inertial
odometry is prone to drift and cannot provide absolute pose estimation.
Leveraging his... | computer science |
31,367 | Beyond Context: Exploring Semantic Similarity for Tiny Face Detection | cs.CV | Tiny face detection aims to find faces with high degrees of variability in
scale, resolution and occlusion in cluttered scenes. Due to the very little
information available on tiny faces, it is not sufficient to detect them merely
based on the information presented inside the tiny bounding boxes or their
context. In th... | computer science |
31,368 | Spectral reflectance estimation from one RGB image using
self-interreflections in a concave object | cs.CV | Light interreflections occurring in a concave object generate a color
gradient which is characteristic of the object's spectral reflectance. In this
paper, we use this property in order to estimate the spectral reflectance of
matte, uniformly colored, V-shaped surfaces from a single RGB image taken under
directional li... | computer science |
31,369 | AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation | cs.CV | Supervised deep learning methods have shown promising results for the task of
monocular depth estimation; but acquiring ground truth is costly, and prone to
noise as well as inaccuracies. While synthetic datasets have been used to
circumvent above problems, the resultant models do not generalize well to
natural scenes ... | computer science |
31,370 | Using Visual Saliency to Improve Human Detection with Convolutional
Networks | cs.CV | In this paper, we demonstrate an approach based on visual saliency for
detection of humans. Using Deep Multi-Layer Network [1], we find the saliency
maps of an image having humans, multiply with the input image and fed to
Convolutional Neural Network (CNN). For detection purpose, we train DetectNet
on prepared two chal... | computer science |
31,371 | Resampling Forgery Detection Using Deep Learning and A-Contrario
Analysis | cs.CV | The amount of digital imagery recorded has recently grown exponentially, and
with the advancement of software, such as Photoshop or Gimp, it has become
easier to manipulate images. However, most images on the internet have not been
manipulated and any automated manipulation detection algorithm must carefully
control th... | computer science |
31,372 | A generalized parametric 3D shape representation for articulated pose
estimation | cs.CV | We present a novel parametric 3D shape representation, Generalized sum of
Gaussians (G-SoG), which is particularly suitable for pose estimation of
articulated objects. Compared with the original sum-of-Gaussians (SoG), G-SoG
can handle both isotropic and anisotropic Gaussians, leading to a more flexible
and adaptable s... | computer science |
31,373 | Abnormality Detection in Mammography using Deep Convolutional Neural
Networks | cs.CV | Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approa... | computer science |
31,374 | M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal}
satellite data fusion | cs.CV | Modern Earth Observation systems provide sensing data at different temporal
and spatial resolutions. Among optical sensors, today the Sentinel-2 program
supplies high-resolution temporal (every 5 days) and high spatial resolution
(10m) images that can be useful to monitor land cover dynamics. On the other
hand, Very Hi... | computer science |
31,375 | Segmentation of Drosophila Heart in Optical Coherence Microscopy Images
Using Convolutional Neural Networks | cs.CV | Convolutional neural networks are powerful tools for image segmentation and
classification. Here, we use this method to identify and mark the heart region
of Drosophila at different developmental stages in the cross-sectional images
acquired by a custom optical coherence microscopy (OCM) system. With our
well-trained c... | computer science |
31,376 | Learning Scene Gist with Convolutional Neural Networks to Improve Object
Recognition | cs.CV | Advancements in convolutional neural networks (CNNs) have made significant
strides toward achieving high performance levels on multiple object recognition
tasks. While some approaches utilize information from the entire scene to
propose regions of interest, the task of interpreting a particular region or
object is stil... | computer science |
31,377 | MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal
Invasive Surgery Based on Heterogeneous Computing | cs.CV | Real-time simultaneously localization and dense mapping is very helpful for
providing Virtual Reality and Augmented Reality for surgeons or even surgical
robots. In this paper, we propose MIS-SLAM: a complete real-time large scale
dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based
on hetero... | computer science |
31,378 | A Non-Technical Survey on Deep Convolutional Neural Network
Architectures | cs.CV | Artificial neural networks have recently shown great results in many
disciplines and a variety of applications, including natural language
understanding, speech processing, games and image data generation. One
particular application in which the strong performance of artificial neural
networks was demonstrated is the r... | computer science |
31,379 | 2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with
Convolutional Networks | cs.CV | In this paper, we tackle the classification of gender in facial images with
deep learning. Our convolutional neural networks (CNN) use the VGG-16
architecture [1] and are pretrained on ImageNet for image classification. Our
proposed method (2^B3^C) first detects the face in the facial image, increases
the margin of a d... | computer science |
31,380 | DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild | cs.CV | In this work we use deep learning to establish dense correspondences between
a 3D object model and an image "in the wild". We introduce "DenseReg", a
fully-convolutional neural network (F-CNN) that densely regresses at every
foreground pixel a pair of U-V template coordinates in a single feedforward
pass. To train Dens... | computer science |
31,381 | Depth Information Guided Crowd Counting for Complex Crowd Scenes | cs.CV | It is important to monitor and analyze crowd events for the sake of city
safety. In an EDOF (extended depth of field) image with a crowded scene, the
distribution of people is highly imbalanced. People far away from the camera
look much smaller and often occlude each other heavily, while people close to
the camera look... | computer science |
31,382 | Personalized Attention-Aware Exposure Control Using Reinforcement
Learning | cs.CV | We propose a reinforcement learning approach for real-time exposure control
of a mobile camera that is personalizable. Our approach is based on Markov
Decision Process (MDP). In the camera viewfinder or live preview mode, given
the current frame, our system predicts the change in exposure so as to optimize
the trade-of... | computer science |
31,383 | GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera
Pose | cs.CV | We propose GeoNet, a jointly unsupervised learning framework for monocular
depth, optical flow and ego-motion estimation from videos. The three components
are coupled by the nature of 3D scene geometry, jointly learned by our
framework in an end-to-end manner. Specifically, geometric relationships are
extracted over th... | computer science |
31,384 | Zero-Shot Sketch-Image Hashing | cs.CV | Recent studies show that large-scale sketch-based image retrieval (SBIR) can
be efficiently tackled by cross-modal binary representation learning methods,
where Hamming distance matching significantly speeds up the process of
similarity search. Providing training and test data subjected to a fixed set of
pre-defined ca... | computer science |
31,385 | Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray
Classification | cs.CV | The increased availability of X-ray image archives (e.g. the ChestX-ray14
dataset from the NIH Clinical Center) has triggered a growing interest in deep
learning techniques. To provide better insight into the different approaches,
and their applications to chest X-ray classification, we investigate a powerful
network a... | computer science |
31,386 | Comparison of various image fusion methods for impervious surface
classification from VNREDSat-1 | cs.CV | Impervious surface is an important indicator for urban development
monitoring. Accurate urban impervious surfaces mapping with VNREDSat-1 remains
challenging due to their spectral diversity not captured by individual PAN
image. In this artical, five multi-resolution image fusion techniques were
compared for classificat... | computer science |
31,387 | PI-VIO: Robust and Efficient Stereo Visual Inertial Odometry using
Points and Lines | cs.CV | In this paper, we present the PerceptIn Visual Inertial Odometry (PI-VIO), a
tightly-coupled filtering-based stereo VIO system using both points and lines.
Line features help improve system robustness in challenging scenarios when
point features cannot be reliably detected or tracked, e.g. low-texture
environment or li... | computer science |
31,388 | Categorical Mixture Models on VGGNet activations | cs.CV | In this project, I use unsupervised learning techniques in order to cluster a
set of yelp restaurant photos under meaningful topics. In order to do this, I
extract layer activations from a pre-trained implementation of the popular
VGGNet convolutional neural network. First, I explore using LDA with the
activations of c... | computer science |
31,389 | Rigid Point Registration with Expectation Conditional Maximization | cs.CV | This paper addresses the issue of matching rigid 3D object points with 2D
image points through point registration based on maximum likelihood principle
in computer simulated images. Perspective projection is necessary when
transforming 3D coordinate into 2D. The problem then recasts into a missing
data framework where ... | computer science |
31,390 | Sparse Adversarial Perturbations for Videos | cs.CV | Although adversarial samples of deep neural networks (DNNs) have been
intensively studied on static images, their extensions in videos are never
explored. Compared with images, attacking a video needs to consider not only
spatial cues but also temporal cues. Moreover, to improve the imperceptibility
as well as reduce t... | computer science |
31,391 | Pyramid Person Matching Network for Person Re-identification | cs.CV | In this work, we present a deep convolutional pyramid person matching network
(PPMN) with specially designed Pyramid Matching Module to address the problem
of person re-identification. The architecture takes a pair of RGB images as
input, and outputs a similiarity value indicating whether the two input images
represent... | computer science |
31,392 | Object cosegmentation using deep Siamese network | cs.CV | Object cosegmentation addresses the problem of discovering similar objects
from multiple images and segmenting them as foreground simultaneously. In this
paper, we propose a novel end-to-end pipeline to segment the similar objects
simultaneously from relevant set of images using supervised learning via
deep-learning fr... | computer science |
31,393 | Multi-Channel Pyramid Person Matching Network for Person
Re-Identification | cs.CV | In this work, we present a Multi-Channel deep convolutional Pyramid Person
Matching Network (MC-PPMN) based on the combination of the semantic-components
and the color-texture distributions to address the problem of person
re-identification. In particular, we learn separate deep representations for
semantic-components ... | computer science |
31,394 | Decoupled Spatial Neural Attention for Weakly Supervised Semantic
Segmentation | cs.CV | Weakly supervised semantic segmentation receives much research attention
since it alleviates the need to obtain a large amount of dense pixel-wise
ground-truth annotations for the training images. Compared with other forms of
weak supervision, image labels are quite efficient to obtain. In our work, we
focus on the wea... | computer science |
31,395 | Generating goal-directed visuomotor plans based on learning using a
predictive coding type deep visuomotor recurrent neural network model | cs.CV | The current paper presents how a predictive coding type deep recurrent neural
networks can generate vision-based goal-directed plans based on prior learning
experience by examining experiment results using a real arm robot. The proposed
deep recurrent neural network learns to predict visuo-proprioceptive sequences
by e... | computer science |
31,396 | Concurrent Spatial and Channel Squeeze & Excitation in Fully
Convolutional Networks | cs.CV | Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in
image segmentation for a plethora of applications. Architectural innovations
within F-CNNs have mainly focused on improving spatial encoding or network
connectivity to aid gradient flow. In this paper, we explore an alternate
direction of rec... | computer science |
31,397 | Single View Stereo Matching | cs.CV | Previous monocular depth estimation methods take a single view and directly
regress the expected results. Though recent advances are made by applying
geometrically inspired loss functions during training, the inference procedure
does not explicitly impose any geometrical constraint. Therefore these models
purely rely o... | computer science |
31,398 | Learning Spectral-Spatial-Temporal Features via a Recurrent
Convolutional Neural Network for Change Detection in Multispectral Imagery | cs.CV | Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framew... | computer science |
31,399 | CNN-Based Automatic Urinary Particles Recognition | cs.CV | The urine sediment analysis of particles in microscopic images can assist
physicians in evaluating patients with renal and urinary tract diseases. Manual
urine sediment examination is labor-intensive, subjective and time-consuming,
and the traditional automatic algorithms often extract the hand-crafted
features for rec... | computer science |
31,400 | Deep Back-Projection Networks For Super-Resolution | cs.CV | The feed-forward architectures of recently proposed deep super-resolution
networks learn representations of low-resolution inputs, and the non-linear
mapping from those to high-resolution output. However, this approach does not
fully address the mutual dependencies of low- and high-resolution images. We
propose Deep Ba... | computer science |
31,401 | HENet:A Highly Efficient Convolutional Neural Networks Optimized for
Accuracy, Speed and Storage | cs.CV | In order to enhance the real-time performance of convolutional neural
networks(CNNs), more and more researchers are focusing on improving the
efficiency of CNN. Based on the analysis of some CNN architectures, such as
ResNet, DenseNet, ShuffleNet and so on, we combined their advantages and
proposed a very efficient mod... | computer science |
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