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26,702 | Gland Instance Segmentation Using Deep Multichannel Neural Networks | cs.CV | Objective: A new image instance segmentation method is proposed to segment
individual glands (instances) in colon histology images. This process is
challenging since the glands not only need to be segmented from a complex
background, they must also be individually identified. Methods: We leverage the
idea of image-to-i... | computer science |
26,703 | Estimation of respiratory pattern from video using selective ensemble
aggregation | cs.CV | Non-contact estimation of respiratory pattern (RP) and respiration rate (RR)
has multiple applications. Existing methods for RP and RR measurement fall into
one of the three categories - (i) estimation through nasal air flow
measurement, (ii) estimation from video-based remote photoplethysmography, and
(iii) estimation... | computer science |
26,704 | Deep Temporal Linear Encoding Networks | cs.CV | The CNN-encoding of features from entire videos for the representation of
human actions has rarely been addressed. Instead, CNN work has focused on
approaches to fuse spatial and temporal networks, but these were typically
limited to processing shorter sequences. We present a new video representation,
called temporal l... | computer science |
26,705 | Covariate conscious approach for Gait recognition based upon Zernike
moment invariants | cs.CV | Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we prop... | computer science |
26,706 | Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for
Isolated Gesture Recognition | cs.CV | Recently, the popularity of depth-sensors such as Kinect has made depth
videos easily available while its advantages have not been fully exploited.
This paper investigates, for gesture recognition, to explore the spatial and
temporal information complementarily embedded in RGB and depth sequences. We
propose a convolut... | computer science |
26,707 | Crowd Counting by Adapting Convolutional Neural Networks with Side
Information | cs.CV | Computer vision tasks often have side information available that is helpful
to solve the task. For example, for crowd counting, the camera perspective
(e.g., camera angle and height) gives a clue about the appearance and scale of
people in the scene. While side information has been shown to be useful for
counting syste... | computer science |
26,708 | Efficient Convolutional Neural Network with Binary Quantization Layer | cs.CV | In this paper we introduce a novel method for segmentation that can benefit
from general semantics of Convolutional Neural Network (CNN). Our segmentation
proposes visually and semantically coherent image segments. We use binary
encoding of CNN features to overcome the difficulty of the clustering on the
high-dimension... | computer science |
26,709 | TextBoxes: A Fast Text Detector with a Single Deep Neural Network | cs.CV | This paper presents an end-to-end trainable fast scene text detector, named
TextBoxes, which detects scene text with both high accuracy and efficiency in a
single network forward pass, involving no post-process except for a standard
non-maximum suppression. TextBoxes outperforms competing methods in terms of
text local... | computer science |
26,710 | SANet: Structure-Aware Network for Visual Tracking | cs.CV | Convolutional neural network (CNN) has drawn increasing interest in visual
tracking owing to its powerfulness in feature extraction. Most existing
CNN-based trackers treat tracking as a classification problem. However, these
trackers are sensitive to similar distractors because their CNN models mainly
focus on inter-cl... | computer science |
26,711 | The subset-matched Jaccard index for evaluation of Segmentation for
Plant Images | cs.CV | We describe a new measure for the evaluation of region level segmentation of
objects, as applied to evaluating the accuracy of leaf-level segmentation of
plant images. The proposed approach enforces the rule that a region (e.g. a
leaf) in either the image being evaluated or the ground truth image evaluated
against can ... | computer science |
26,712 | Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley
Localization | cs.CV | Ridge and valley enhancing filters are widely used in applications such as
vessel detection in medical image computing. When images are degraded by noise
or include vessels at different scales, such filters are an essential step for
meaningful and stable vessel localization. In this work, we propose a novel
multi-scale... | computer science |
26,713 | Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR
Images using Deep Learning | cs.CV | Objective: Several studies have associated codeletion of chromosome arms
1p/19q in low-grade gliomas (LGG) with positive response to treatment and
longer progression free survival. Therefore, predicting 1p/19q status is
crucial for effective treatment planning of LGG. In this study, we predict the
1p/19q status from MR... | computer science |
26,714 | Dense Captioning with Joint Inference and Visual Context | cs.CV | Dense captioning is a newly emerging computer vision topic for understanding
images with dense language descriptions. The goal is to densely detect visual
concepts (e.g., objects, object parts, and interactions between them) from
images, labeling each with a short descriptive phrase. We identify two key
challenges of d... | computer science |
26,715 | Sampled Image Tagging and Retrieval Methods on User Generated Content | cs.CV | Traditional image tagging and retrieval algorithms have limited value as a
result of being trained with heavily curated datasets. These limitations are
most evident when arbitrary search words are used that do not intersect with
training set labels. Weak labels from user generated content (UGC) found in the
wild (e.g.,... | computer science |
26,716 | Kernel Cross-View Collaborative Representation based Classification for
Person Re-Identification | cs.CV | Person re-identification aims at the maintenance of a global identity as a
person moves among non-overlapping surveillance cameras. It is a hard task due
to different illumination conditions, viewpoints and the small number of
annotated individuals from each pair of cameras (small-sample-size problem).
Collaborative Re... | computer science |
26,717 | Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity
Problems | cs.CV | In this work we show how sublabel-accurate multilabeling approaches can be
derived by approximating a classical label-continuous convex relaxation of
nonconvex free-discontinuity problems. This insight allows to extend these
sublabel-accurate approaches from total variation to general convex and
nonconvex regularizatio... | computer science |
26,718 | Image-to-Image Translation with Conditional Adversarial Networks | cs.CV | We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems th... | computer science |
26,719 | Cascaded Neural Networks with Selective Classifiers and its evaluation
using Lung X-ray CT Images | cs.CV | Lung nodule detection is a class imbalanced problem because nodules are found
with much lower frequency than non-nodules. In the class imbalanced problem,
conventional classifiers tend to be overwhelmed by the majority class and
ignore the minority class. We therefore propose cascaded convolutional neural
networks to c... | computer science |
26,720 | Learning Multi-level Features For Sensor-based Human Action Recognition | cs.CV | This paper proposes a multi-level feature learning framework for human action
recognition using a single body-worn inertial sensor. The framework consists of
three phases, respectively designed to analyze signal-based (low-level),
components (mid-level) and semantic (high-level) information. Low-level
features capture ... | computer science |
26,721 | Learning Multi-level Deep Representations for Image Emotion
Classification | cs.CV | In this paper, we propose a new deep network that learns multi-level deep
representations for image emotion classification (MldrNet). Image emotion can
be recognized through image semantics, image aesthetics and low-level visual
features from both global and local views. Existing image emotion
classification works usin... | computer science |
26,722 | Recurrent Attention Models for Depth-Based Person Identification | cs.CV | We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning tas... | computer science |
26,723 | A Spatial and Temporal Non-Local Filter Based Data Fusion | cs.CV | The trade-off in remote sensing instruments that balances the spatial
resolution and temporal frequency limits our capacity to monitor spatial and
temporal dynamics effectively. The spatiotemporal data fusion technique is
considered as a cost-effective way to obtain remote sensing data with both high
spatial resolution... | computer science |
26,724 | Active learning with version spaces for object detection | cs.CV | Given an image, we would like to learn to detect objects belonging to
particular object categories. Common object detection methods train on large
annotated datasets which are annotated in terms of bounding boxes that contain
the object of interest. Previous works on object detection model the problem as
a structured r... | computer science |
26,725 | PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of
Fetal MRI | cs.CV | In this paper we present a novel method for the correction of motion
artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of
the whole uterus. Contrary to current slice-to-volume registration (SVR)
methods, requiring an inflexible anatomical enclosure of a single investigated
organ, the proposed p... | computer science |
26,726 | Smart Library: Identifying Books in a Library using Richly Supervised
Deep Scene Text Reading | cs.CV | Physical library collections are valuable and long standing resources for
knowledge and learning. However, managing books in a large bookshelf and
finding books on it often leads to tedious manual work, especially for large
book collections where books might be missing or misplaced. Recently, deep
neural models, such a... | computer science |
26,727 | Scene Labeling using Gated Recurrent Units with Explicit Long Range
Conditioning | cs.CV | Recurrent neural network (RNN), as a powerful contextual dependency modeling
framework, has been widely applied to scene labeling problems. However, this
work shows that directly applying traditional RNN architectures, which unfolds
a 2D lattice grid into a sequence, is not sufficient to model structure
dependencies in... | computer science |
26,728 | Self-learning Scene-specific Pedestrian Detectors using a Progressive
Latent Model | cs.CV | In this paper, a self-learning approach is proposed towards solving
scene-specific pedestrian detection problem without any human' annotation
involved. The self-learning approach is deployed as progressive steps of object
discovery, object enforcement, and label propagation. In the learning
procedure, object locations ... | computer science |
26,729 | Sar image despeckling based on nonlocal similarity sparse decomposition | cs.CV | This letter presents a method of synthetic aperture radar (SAR) image
despeckling aimed to preserve the detail information while suppressing speckle
noise. This method combines the nonlocal self-similarity partition and a
proposed modified sparse decomposition. The nonlocal partition method groups a
series of structure... | computer science |
26,730 | Relaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and
their use as a Loss Function in Deep Learning | cs.CV | The Earth Mover's Distance (EMD) computes the optimal cost of transforming
one distribution into another, given a known transport metric between them. In
deep learning, the EMD loss allows us to embed information during training
about the output space structure like hierarchical or semantic relations. This
helps in ach... | computer science |
26,731 | Alternating Direction Graph Matching | cs.CV | In this paper, we introduce a graph matching method that can account for
constraints of arbitrary order, with arbitrary potential functions. Unlike
previous decomposition approaches that rely on the graph structures, we
introduce a decomposition of the matching constraints. Graph matching is then
reformulated as a non-... | computer science |
26,732 | Learning Joint Feature Adaptation for Zero-Shot Recognition | cs.CV | Zero-shot recognition (ZSR) aims to recognize target-domain data instances of
unseen classes based on the models learned from associated pairs of seen-class
source and target domain data. One of the key challenges in ZSR is the relative
scarcity of source-domain features (e.g. one feature vector per class), which
do no... | computer science |
26,733 | Fast Fourier Color Constancy | cs.CV | We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm
which solves illuminant estimation by reducing it to a spatial localization
task on a torus. By operating in the frequency domain, FFCC produces lower
error rates than the previous state-of-the-art by 13-20% while being 250-3000
times faster. T... | computer science |
26,734 | T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory
Prediction | cs.CV | Precise destination prediction of taxi trajectories can benefit many
intelligent location based services such as accurate ad for passengers.
Traditional prediction approaches, which treat trajectories as one-dimensional
sequences and process them in single scale, fail to capture the diverse
two-dimensional patterns of ... | computer science |
26,735 | Video Captioning with Transferred Semantic Attributes | cs.CV | Automatically generating natural language descriptions of videos plays a
fundamental challenge for computer vision community. Most recent progress in
this problem has been achieved through employing 2-D and/or 3-D Convolutional
Neural Networks (CNN) to encode video content and Recurrent Neural Networks
(RNN) to decode ... | computer science |
26,736 | UniMiB SHAR: a new dataset for human activity recognition using
acceleration data from smartphones | cs.CV | Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are
being increasingly used to monitor human activities. Data acquired by the
hosted sensors are usually processed by machine-learning-based algorithms to
classify human activities. The success of those algorithms mostly depends on
the availabilit... | computer science |
26,737 | 3D Menagerie: Modeling the 3D shape and pose of animals | cs.CV | There has been significant work on learning realistic, articulated, 3D models
of the human body. In contrast, there are few such models of animals, despite
many applications. The main challenge is that animals are much less cooperative
than humans. The best human body models are learned from thousands of 3D scans
of pe... | computer science |
26,738 | 'Part'ly first among equals: Semantic part-based benchmarking for
state-of-the-art object recognition systems | cs.CV | An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on wh... | computer science |
26,739 | Fully Convolutional Instance-aware Semantic Segmentation | cs.CV | We present the first fully convolutional end-to-end solution for
instance-aware semantic segmentation task. It inherits all the merits of FCNs
for semantic segmentation and instance mask proposal. It performs instance mask
prediction and classification jointly. The underlying convolutional
representation is fully share... | computer science |
26,740 | Deep Feature Flow for Video Recognition | cs.CV | Deep convolutional neutral networks have achieved great success on image
recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art
image recognition networks to videos as per-frame evaluation is too slow and
unaffordable. We present deep feature flow, a fast and accurate framework for
video recognition... | computer science |
26,741 | Deep Convolutional Neural Networks with Merge-and-Run Mappings | cs.CV | A deep residual network, built by stacking a sequence of residual blocks, is
easy to train, because identity mappings skip residual branches and thus
improve information flow. To further reduce the training difficulty, we present
a simple network architecture, deep merge-and-run neural networks. The novelty
lies in a m... | computer science |
26,742 | PoseTrack: Joint Multi-Person Pose Estimation and Tracking | cs.CV | In this work, we introduce the challenging problem of joint multi-person pose
estimation and tracking of an unknown number of persons in unconstrained
videos. Existing methods for multi-person pose estimation in images cannot be
applied directly to this problem, since it also requires to solve the problem
of person ass... | computer science |
26,743 | Convergence Analysis of MAP based Blur Kernel Estimation | cs.CV | One popular approach for blind deconvolution is to formulate a maximum a
posteriori (MAP) problem with sparsity priors on the gradients of the latent
image, and then alternatingly estimate the blur kernel and the latent image.
While several successful MAP based methods have been proposed, there has been
much controvers... | computer science |
26,744 | Multi-View 3D Object Detection Network for Autonomous Driving | cs.CV | This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view repre... | computer science |
26,745 | Object Detection using Image Processing | cs.CV | An Unmanned Ariel vehicle (UAV) has greater importance in the army for border
security. The main objective of this article is to develop an OpenCV-Python
code using Haar Cascade algorithm for object and face detection. Currently,
UAVs are used for detecting and attacking the infiltrated ground targets. The
main drawbac... | computer science |
26,746 | Learning Invariant Representations Of Planar Curves | cs.CV | We propose a metric learning framework for the construction of invariant
geometric functions of planar curves for the Eucledian and Similarity group of
transformations. We leverage on the representational power of convolutional
neural networks to compute these geometric quantities. In comparison with
axiomatic construc... | computer science |
26,747 | A dataset and exploration of models for understanding video data through
fill-in-the-blank question-answering | cs.CV | While deep convolutional neural networks frequently approach or exceed
human-level performance at benchmark tasks involving static images, extending
this success to moving images is not straightforward. Having models which can
learn to understand video is of interest for many applications, including
content recommendat... | computer science |
26,748 | Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose | cs.CV | This paper addresses the challenge of 3D human pose estimation from a single
color image. Despite the general success of the end-to-end learning paradigm,
top performing approaches employ a two-step solution consisting of a
Convolutional Network (ConvNet) for 2D joint localization and a subsequent
optimization step to ... | computer science |
26,749 | Controlling Perceptual Factors in Neural Style Transfer | cs.CV | Neural Style Transfer has shown very exciting results enabling new forms of
image manipulation. Here we extend the existing method to introduce control
over spatial location, colour information and across spatial scale. We
demonstrate how this enhances the method by allowing high-resolution controlled
stylisation and h... | computer science |
26,750 | The World of Fast Moving Objects | cs.CV | The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely ... | computer science |
26,751 | Image-based localization using LSTMs for structured feature correlation | cs.CV | In this work we propose a new CNN+LSTM architecture for camera pose
regression for indoor and outdoor scenes. CNNs allow us to learn suitable
feature representations for localization that are robust against motion blur
and illumination changes. We make use of LSTM units on the CNN output, which
play the role of a struc... | computer science |
26,752 | Image Segmentation Using Overlapping Group Sparsity | cs.CV | Sparse decomposition has been widely used for different applications, such as
source separation, image classification and image denoising. This paper
presents a new algorithm for segmentation of an image into background and
foreground text and graphics using sparse decomposition. First, the background
is represented us... | computer science |
26,753 | Straight to Shapes: Real-time Detection of Encoded Shapes | cs.CV | Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
repre... | computer science |
26,754 | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | cs.CV | We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-u... | computer science |
26,755 | Recalling Holistic Information for Semantic Segmentation | cs.CV | Semantic segmentation requires a detailed labeling of image pixels by object
category. Information derived from local image patches is necessary to describe
the detailed shape of individual objects. However, this information is
ambiguous and can result in noisy labels. Global inference of image content can
instead capt... | computer science |
26,756 | Deep Joint Face Hallucination and Recognition | cs.CV | Deep models have achieved impressive performance for face hallucination
tasks. However, we observe that directly feeding the hallucinated facial images
into recog- nition models can even degrade the recognition performance despite
the much better visualization quality. In this paper, we address this problem
by jointly ... | computer science |
26,757 | Geometric deep learning: going beyond Euclidean data | cs.CV | Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In man... | computer science |
26,758 | Automatically Building Face Datasets of New Domains from Weakly Labeled
Data with Pretrained Models | cs.CV | Training data are critical in face recognition systems. However, labeling a
large scale face data for a particular domain is very tedious. In this paper,
we propose a method to automatically and incrementally construct datasets from
massive weakly labeled data of the target domain which are readily available on
the Int... | computer science |
26,759 | Extraction of airway trees using multiple hypothesis tracking and
template matching | cs.CV | Knowledge of airway tree morphology has important clinical applications in
diagnosis of chronic obstructive pulmonary disease. We present an automatic
tree extraction method based on multiple hypothesis tracking and template
matching for this purpose and evaluate its performance on chest CT images. The
method is adapte... | computer science |
26,760 | Comparative study of histogram distance measures for re-identification | cs.CV | Color based re-identification methods usually rely on a distance function to
measure the similarity between individuals. In this paper we study the behavior
of several histogram distance measures in different color spaces. We wonder
whether there is a particular histogram distance measure better than others,
likewise a... | computer science |
26,761 | Interferences in match kernels | cs.CV | We consider the design of an image representation that embeds and aggregates
a set of local descriptors into a single vector. Popular representations of
this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When
two such image representations are compared with the dot-product, the
image-to-image si... | computer science |
26,762 | Domain Adaptation by Mixture of Alignments of Second- or Higher-Order
Scatter Tensors | cs.CV | In this paper, we propose an approach to the domain adaptation, dubbed
Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of
alignments of second- or higher-order scatter statistics between the source and
target domains. The human ability to learn from few labeled samples is a
recurring motiva... | computer science |
26,763 | AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for
Human Action Recognition in Videos | cs.CV | We propose a novel method for temporally pooling frames in a video for the
task of human action recognition. The method is motivated by the observation
that there are only a small number of frames which, together, contain
sufficient information to discriminate an action class present in a video, from
the rest. The prop... | computer science |
26,764 | Weakly Supervised Cascaded Convolutional Networks | cs.CV | Object detection is a challenging task in visual understanding domain, and
even more so if the supervision is to be weak. Recently, few efforts to handle
the task without expensive human annotations is established by promising deep
neural network. A new architecture of cascaded networks is proposed to learn a
convoluti... | computer science |
26,765 | InstanceCut: from Edges to Instances with MultiCut | cs.CV | This work addresses the task of instance-aware semantic segmentation. Our key
motivation is to design a simple method with a new modelling-paradigm, which
therefore has a different trade-off between advantages and disadvantages
compared to known approaches. Our approach, we term InstanceCut, represents the
problem by t... | computer science |
26,766 | Deep Watershed Transform for Instance Segmentation | cs.CV | Most contemporary approaches to instance segmentation use complex pipelines
involving conditional random fields, recurrent neural networks, object
proposals, or template matching schemes. In our paper, we present a simple yet
powerful end-to-end convolutional neural network to tackle this task. Our
approach combines in... | computer science |
26,767 | Full-Resolution Residual Networks for Semantic Segmentation in Street
Scenes | cs.CV | Semantic image segmentation is an essential component of modern autonomous
driving systems, as an accurate understanding of the surrounding scene is
crucial to navigation and action planning. Current state-of-the-art approaches
in semantic image segmentation rely on pre-trained networks that were initially
developed fo... | computer science |
26,768 | Learning an Invariant Hilbert Space for Domain Adaptation | cs.CV | This paper introduces a learning scheme to construct a Hilbert space (i.e., a
vector space along its inner product) to address both unsupervised and
semi-supervised domain adaptation problems. This is achieved by learning
projections from each domain to a latent space along the Mahalanobis metric of
the latent space to... | computer science |
26,769 | Deep Video Deblurring | cs.CV | Motion blur from camera shake is a major problem in videos captured by
hand-held devices. Unlike single-image deblurring, video-based approaches can
take advantage of the abundant information that exists across neighboring
frames. As a result the best performing methods rely on aligning nearby frames.
However, aligning... | computer science |
26,770 | Color Constancy with Derivative Colors | cs.CV | Information about the illuminant color is well contained in both achromatic
regions and the specular components of highlight regions. In this paper, we
propose a novel way to achieve color constancy by exploiting such clues. The
key to our approach lies in the use of suitably extracted derivative colors,
which are able... | computer science |
26,771 | Geometric deep learning on graphs and manifolds using mixture model CNNs | cs.CV | Deep learning has achieved a remarkable performance breakthrough in several
fields, most notably in speech recognition, natural language processing, and
computer vision. In particular, convolutional neural network (CNN)
architectures currently produce state-of-the-art performance on a variety of
image analysis tasks su... | computer science |
26,772 | Semantic Segmentation using Adversarial Networks | cs.CV | Adversarial training has been shown to produce state of the art results for
generative image modeling. In this paper we propose an adversarial training
approach to train semantic segmentation models. We train a convolutional
semantic segmentation network along with an adversarial network that
discriminates segmentation... | computer science |
26,773 | Discriminative Correlation Filter with Channel and Spatial Reliability | cs.CV | Short-term tracking is an open and challenging problem for which
discriminative correlation filters (DCF) have shown excellent performance. We
introduce the channel and spatial reliability concepts to DCF tracking and
provide a novel learning algorithm for its efficient and seamless integration
in the filter update and... | computer science |
26,774 | Multimodal Latent Variable Analysis | cs.CV | Consider a set of multiple, multimodal sensors capturing a complex system or
a physical phenomenon of interest. Our primary goal is to distinguish the
underlying sources of variability manifested in the measured data. The first
step in our analysis is to find the common source of variability present in all
sensor measu... | computer science |
26,775 | Person Re-Identification by Unsupervised Video Matching | cs.CV | Most existing person re-identification (ReID) methods rely only on the
spatial appearance information from either one or multiple person images,
whilst ignore the space-time cues readily available in video or image-sequence
data. Moreover, they often assume the availability of exhaustively labelled
cross-view pairwise ... | computer science |
26,776 | Clickstream analysis for crowd-based object segmentation with confidence | cs.CV | With the rapidly increasing interest in machine learning based solutions for
automatic image annotation, the availability of reference annotations for
algorithm training is one of the major bottlenecks in the field. Crowdsourcing
has evolved as a valuable option for low-cost and large-scale data annotation;
however, qu... | computer science |
26,777 | Online Real-time Multiple Spatiotemporal Action Localisation and
Prediction | cs.CV | We present a deep-learning framework for real-time multiple spatio-temporal
(S/T) action localisation, classification and early prediction. Current
state-of-the-art approaches work offline and are too slow to be useful in real-
world settings. To overcome their limitations we introduce two major
developments. Firstly, ... | computer science |
26,778 | Learning from Maps: Visual Common Sense for Autonomous Driving | cs.CV | Today's autonomous vehicles rely extensively on high-definition 3D maps to
navigate the environment. While this approach works well when these maps are
completely up-to-date, safe autonomous vehicles must be able to corroborate the
map's information via a real time sensor-based system. Our goal in this work is
to devel... | computer science |
26,779 | PVANet: Lightweight Deep Neural Networks for Real-time Object Detection | cs.CV | In object detection, reducing computational cost is as important as improving
accuracy for most practical usages. This paper proposes a novel network
structure, which is an order of magnitude lighter than other state-of-the-art
networks while maintaining the accuracy. Based on the basic principle of more
layers with le... | computer science |
26,780 | Fast deterministic tourist walk for texture analysis | cs.CV | Deterministic tourist walk (DTW) has attracted increasing interest in
computer vision. In the last years, different methods for analysis of dynamic
and static textures were proposed. So far, all works based on the DTW for
texture analysis use all image pixels as initial point of a walk. However, this
requires much runt... | computer science |
26,781 | Directional Mean Curvature for Textured Image Demixing | cs.CV | Approximation theory plays an important role in image processing, especially
image deconvolution and decomposition. For piecewise smooth images, there are
many methods that have been developed over the past thirty years. The goal of
this study is to devise similar and practical methodology for handling textured
images.... | computer science |
26,782 | Texture analysis using deterministic partially self-avoiding walk with
thresholds | cs.CV | In this paper, we propose a new texture analysis method using the
deterministic partially self-avoiding walk performed on maps modified with
thresholds. In this method, two pixels of the map are neighbors if the
Euclidean distance between them is less than $\sqrt{2}$ and the weight
(difference between its intensities) ... | computer science |
26,783 | Multi-Task Zero-Shot Action Recognition with Prioritised Data
Augmentation | cs.CV | Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on... | computer science |
26,784 | Semi-supervised Learning using Denoising Autoencoders for Brain Lesion
Detection and Segmentation | cs.CV | The work presented explores the use of denoising autoencoders (DAE) for brain
lesion detection, segmentation and false positive reduction. Stacked denoising
autoencoders (SDAE) were pre-trained using a large number of unlabeled patient
volumes and fine tuned with patches drawn from a limited number of patients
(n=20, 4... | computer science |
26,785 | Real-Time Video Highlights for Yahoo Esports | cs.CV | Esports has gained global popularity in recent years and several companies
have started offering live streaming videos of esports games and events. This
creates opportunities to develop large scale video understanding systems for
new product features and services. We present a technique for detecting
highlights from li... | computer science |
26,786 | Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional
Neural Net | cs.CV | Deformable registration is ubiquitous in medical image analysis. Many
deformable registration methods minimize sum of squared difference (SSD) as the
registration cost with respect to deformable model parameters. In this work, we
construct a tight upper bound of the SSD registration cost by using a fully
convolutional ... | computer science |
26,787 | Did Evolution get it right? An evaluation of Near-Infrared imaging in
semantic scene segmentation using deep learning | cs.CV | Animals have evolved to restrict their sensing capabilities to certain region
of electromagnetic spectrum. This is surprisingly a very narrow band on a vast
scale which makes one think if there is a systematic bias underlying such
selective filtration. The situation becomes even more intriguing when we find a
sharp cut... | computer science |
26,788 | Uniform Information Segmentation | cs.CV | Size uniformity is one of the main criteria of superpixel methods. But size
uniformity rarely conforms to the varying content of an image. The chosen size
of the superpixels therefore represents a compromise - how to obtain the fewest
superpixels without losing too much important detail. We propose that a more
appropri... | computer science |
26,789 | Voronoi-based compact image descriptors: Efficient Region-of-Interest
retrieval with VLAD and deep-learning-based descriptors | cs.CV | We investigate the problem of image retrieval based on visual queries when
the latter comprise arbitrary regions-of-interest (ROI) rather than entire
images. Our proposal is a compact image descriptor that combines the
state-of-the-art in content-based descriptor extraction with a multi-level,
Voronoi-based spatial par... | computer science |
26,790 | Semantic Scene Completion from a Single Depth Image | cs.CV | This paper focuses on semantic scene completion, a task for producing a
complete 3D voxel representation of volumetric occupancy and semantic labels
for a scene from a single-view depth map observation. Previous work has
considered scene completion and semantic labeling of depth maps separately.
However, we observe tha... | computer science |
26,791 | Range Loss for Deep Face Recognition with Long-tail | cs.CV | Convolutional neural networks have achieved great improvement on face
recognition in recent years because of its extraordinary ability in learning
discriminative features of people with different identities. To train such a
well-designed deep network, tremendous amounts of data is indispensable. Long
tail distribution ... | computer science |
26,792 | Analyzing the group sparsity based on the rank minimization methods | cs.CV | Sparse coding has achieved a great success in various image processing
studies. However, there is not any benchmark to measure the sparsity of image
patch/group because sparse discriminant conditions cannot keep unchanged. This
paper analyzes the sparsity of group based on the strategy of the rank
minimization. Firstly... | computer science |
26,793 | Improving Fully Convolution Network for Semantic Segmentation | cs.CV | Fully Convolution Networks (FCN) have achieved great success in dense
prediction tasks including semantic segmentation. In this paper, we start from
discussing FCN by understanding its architecture limitations in building a
strong segmentation network. Next, we present our Improved Fully Convolution
Network (IFCN). In ... | computer science |
26,794 | Object Detection Free Instance Segmentation With Labeling
Transformations | cs.CV | Instance segmentation has attracted recent attention in computer vision and
existing methods in this domain mostly have an object detection stage. In this
paper, we study the intrinsic challenge of the instance segmentation problem,
the presence of a quotient space (swapping the labels of different instances
leads to t... | computer science |
26,795 | Hyperspectral CNN Classification with Limited Training Samples | cs.CV | Hyperspectral imaging sensors are becoming increasingly popular in robotics
applications such as agriculture and mining, and allow per-pixel thematic
classification of materials in a scene based on their unique spectral
signatures. Recently, convolutional neural networks have shown remarkable
performance for classifica... | computer science |
26,796 | 3D Human Pose Estimation from a Single Image via Distance Matrix
Regression | cs.CV | This paper addresses the problem of 3D human pose estimation from a single
image. We follow a standard two-step pipeline by first detecting the 2D
position of the $N$ body joints, and then using these observations to infer 3D
pose. For the first step, we use a recent CNN-based detector. For the second
step, most existi... | computer science |
26,797 | Awesome Typography: Statistics-Based Text Effects Transfer | cs.CV | In this work, we explore the problem of generating fantastic special-effects
for the typography. It is quite challenging due to the model diversities to
illustrate varied text effects for different characters. To address this issue,
our key idea is to exploit the analytics on the high regularity of the spatial
distribu... | computer science |
26,798 | Deep, Dense, and Low-Rank Gaussian Conditional Random Fields | cs.CV | In this work we introduce a fully-connected graph structure in the Deep
Gaussian Conditional Random Field (G-CRF) model. For this we express the
pairwise interactions between pixels as the inner-products of low-dimensional
embeddings, delivered by a new subnetwork of a deep architecture. We
efficiently minimize the res... | computer science |
26,799 | Bidirectional Multirate Reconstruction for Temporal Modeling in Videos | cs.CV | Despite the recent success of neural networks in image feature learning, a
major problem in the video domain is the lack of sufficient labeled data for
learning to model temporal information. In this paper, we propose an
unsupervised temporal modeling method that learns from untrimmed videos. The
speed of motion varies... | computer science |
26,800 | Social Scene Understanding: End-to-End Multi-Person Action Localization
and Collective Activity Recognition | cs.CV | We present a unified framework for understanding human social behaviors in
raw image sequences. Our model jointly detects multiple individuals, infers
their social actions, and estimates the collective actions with a single
feed-forward pass through a neural network. We propose a single architecture
that does not rely ... | computer science |
26,801 | On the Role and the Importance of Features for Background Modeling and
Foreground Detection | cs.CV | Background modeling has emerged as a popular foreground detection technique
for various applications in video surveillance. Background modeling methods
have become increasing efficient in robustly modeling the background and hence
detecting moving objects in any visual scene. Although several background
subtraction and... | computer science |
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