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25,002 | Feature Sampling Strategies for Action Recognition | cs.CV | Although dense local spatial-temporal features with bag-of-features
representation achieve state-of-the-art performance for action recognition, the
huge feature number and feature size prevent current methods from scaling up to
real size problems. In this work, we investigate different types of feature
sampling strateg... | computer science |
25,003 | End-to-End Photo-Sketch Generation via Fully Convolutional
Representation Learning | cs.CV | Sketch-based face recognition is an interesting task in vision and multimedia
research, yet it is quite challenging due to the great difference between face
photos and sketches. In this paper, we propose a novel approach for
photo-sketch generation, aiming to automatically transform face photos into
detail-preserving p... | computer science |
25,004 | On Vectorization of Deep Convolutional Neural Networks for Vision Tasks | cs.CV | We recently have witnessed many ground-breaking results in machine learning
and computer vision, generated by using deep convolutional neural networks
(CNN). While the success mainly stems from the large volume of training data
and the deep network architectures, the vector processing hardware (e.g. GPU)
undisputedly p... | computer science |
25,005 | Learning And-Or Models to Represent Context and Occlusion for Car
Detection and Viewpoint Estimation | cs.CV | This paper presents a method for learning And-Or models to represent context
and occlusion for car detection and viewpoint estimation. The learned And-Or
model represents car-to-car context and occlusion configurations at three
levels: (i) spatially-aligned cars, (ii) single car under different occlusion
configurations... | computer science |
25,006 | Weakly Supervised Learning for Salient Object Detection | cs.CV | Recent advances in supervised salient object detection has resulted in
significant performance on benchmark datasets. Training such models, however,
requires expensive pixel-wise annotations of salient objects. Moreover, many
existing salient object detection models assume that at least one salient
object exists in the... | computer science |
25,007 | Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous
Landscapes using Holistic Structure based Models | cs.CV | In this study, a novel machine learning algorithm is presented for
disaggregation of satellite soil moisture (SM) based on self-regularized
regressive models (SRRM) using high-resolution correlated information from
auxiliary sources. It includes regularized clustering that assigns soft
memberships to each pixel at fine... | computer science |
25,008 | Vector Quantization by Minimizing Kullback-Leibler Divergence | cs.CV | This paper proposes a new method for vector quantization by minimizing the
Kullback-Leibler Divergence between the class label distributions over the
quantization inputs, which are original vectors, and the output, which is the
quantization subsets of the vector set. In this way, the vector quantization
output can keep... | computer science |
25,009 | Downscaling Microwave Brightness Temperatures Using Self Regularized
Regressive Models | cs.CV | A novel algorithm is proposed to downscale microwave brightness temperatures
($\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture
Active Passive mission to a resolution meaningful for hydrological and
agricultural applications. This algorithm, called Self-Regularized Regressive
Models (SRRM), us... | computer science |
25,010 | Blob indentation identification via curvature measurement | cs.CV | This paper presents a novel method for identifying indentations on the
boundary of solid 2D shape. It uses the signed curvature at a set of points
along the boundary to identify indentations and provides one parameter for
tuning the selection mechanism for discriminating indentations from other
boundary irregularities.... | computer science |
25,011 | Co-Regularized Deep Representations for Video Summarization | cs.CV | Compact keyframe-based video summaries are a popular way of generating
viewership on video sharing platforms. Yet, creating relevant and compelling
summaries for arbitrarily long videos with a small number of keyframes is a
challenging task. We propose a comprehensive keyframe-based summarization
framework combining de... | computer science |
25,012 | A Proximal Bregman Projection Approach to Continuous Max-Flow Problems
Using Entropic Distances | cs.CV | One issue limiting the adaption of large-scale multi-region segmentation is
the sometimes prohibitive memory requirements. This is especially troubling
considering advances in massively parallel computing and commercial graphics
processing units because of their already limited memory compared to the
current random acc... | computer science |
25,013 | An Analytical Study of different Document Image Binarization Methods | cs.CV | Document image has been the area of research for a couple of decades because
of its potential application in the area of text recognition, line recognition
or any other shape recognition from the image. For most of these purposes
binarization of image becomes mandatory as far as recognition is concerned.
Throughout cou... | computer science |
25,014 | Multi-task Image Classification via Collaborative, Hierarchical
Spike-and-Slab Priors | cs.CV | Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particu... | computer science |
25,015 | Weakly Supervised Learning of Objects, Attributes and their Associations | cs.CV | When humans describe images they tend to use combinations of nouns and
adjectives, corresponding to objects and their associated attributes
respectively. To generate such a description automatically, one needs to model
objects, attributes and their associations. Conventional methods require strong
annotation of object ... | computer science |
25,016 | Efficient piecewise training of deep structured models for semantic
segmentation | cs.CV | Recent advances in semantic image segmentation have mostly been achieved by
training deep convolutional neural networks (CNNs). We show how to improve
semantic segmentation through the use of contextual information; specifically,
we explore `patch-patch' context between image regions, and `patch-background'
context. Fo... | computer science |
25,017 | Discriminative and Efficient Label Propagation on Complementary Graphs
for Multi-Object Tracking | cs.CV | Given a set of detections, detected at each time instant independently, we
investigate how to associate them across time. This is done by propagating
labels on a set of graphs, each graph capturing how either the spatio-temporal
or the appearance cues promote the assignment of identical or distinct labels
to a pair of ... | computer science |
25,018 | Matching-CNN Meets KNN: Quasi-Parametric Human Parsing | cs.CV | Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision... | computer science |
25,019 | Knowledge driven Offline to Online Script Conversion | cs.CV | The problem of offline to online script conversion is a challenging and an
ill-posed problem. The interest in offline to online conversion exists because
there are a plethora of robust algorithms in online script literature which can
not be used on offline scripts. In this paper, we propose a method, based on
heuristic... | computer science |
25,020 | Locally Non-rigid Registration for Mobile HDR Photography | cs.CV | Image registration for stack-based HDR photography is challenging. If not
properly accounted for, camera motion and scene changes result in artifacts in
the composite image. Unfortunately, existing methods to address this problem
are either accurate, but too slow for mobile devices, or fast, but prone to
failing. We pr... | computer science |
25,021 | Mobile Phone Based Vehicle License Plate Recognition for Road Policing | cs.CV | Identity of a vehicle is done through the vehicle license plate by traffic
police in general. Au- tomatic vehicle license plate recognition has several
applications in intelligent traffic management systems. The security situation
across the globe and particularly in India demands a need to equip the traffic
police wit... | computer science |
25,022 | On-line Handwritten Devanagari Character Recognition using Fuzzy
Directional Features | cs.CV | This paper describes a new feature set for use in the recognition of on-line
handwritten Devanagari script based on Fuzzy Directional Features. Experiments
are conducted for the automatic recognition of isolated handwritten character
primitives (sub-character units). Initially we describe the proposed feature
set, call... | computer science |
25,023 | Design and Implementation of a 3D Undersea Camera System | cs.CV | In this paper, we present the design and development of an undersea camera
system. The goal of our system is to provide a 3D model of the undersea habitat
in a long-term continuous manner. The most important feature of our system is
the use of multiple cameras and multiple projectors, which is able to provide
accurate ... | computer science |
25,024 | Heterogeneous Tensor Decomposition for Clustering via Manifold
Optimization | cs.CV | Tensors or multiarray data are generalizations of matrices. Tensor clustering
has become a very important research topic due to the intrinsically rich
structures in real-world multiarray datasets. Subspace clustering based on
vectorizing multiarray data has been extensively researched. However,
vectorization of tensori... | computer science |
25,025 | A Multicomponent Approach to Nonrigid Registration of Diffusion Tensor
Images | cs.CV | We propose a nonrigid registration approach for diffusion tensor images using
a multicomponent information-theoretic measure. Explicit orientation
optimization is enabled by incorporating tensor reorientation, which is
necessary for wrapping diffusion tensor images. Experimental results on
diffusion tensor images indic... | computer science |
25,026 | Kernelized Low Rank Representation on Grassmann Manifolds | cs.CV | Low rank representation (LRR) has recently attracted great interest due to
its pleasing efficacy in exploring low-dimensional subspace structures embedded
in data. One of its successful applications is subspace clustering which means
data are clustered according to the subspaces they belong to. In this paper, at
a high... | computer science |
25,027 | Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective | cs.CV | Many computer vision algorithms employ subspace models to represent data. The
Low-rank representation (LRR) has been successfully applied in subspace
clustering for which data are clustered according to their subspace structures.
The possibility of extending LRR on Grassmann manifold is explored in this
paper. Rather t... | computer science |
25,028 | Robust real time face recognition and tracking on gpu using fusion of
rgb and depth image | cs.CV | This paper presents a real-time face recognition system using kinect sensor.
The algorithm is implemented on GPU using opencl and significant speed
improvements are observed. We use kinect depth image to increase the robustness
and reduce computational cost of conventional LBP based face recognition. The
main objective... | computer science |
25,029 | Evaluating Two-Stream CNN for Video Classification | cs.CV | Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets f... | computer science |
25,030 | MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking | cs.CV | In the recent past, the computer vision community has developed centralized
benchmarks for the performance evaluation of a variety of tasks, including
generic object and pedestrian detection, 3D reconstruction, optical flow,
single-object short-term tracking, and stereo estimation. Despite potential
pitfalls of such be... | computer science |
25,031 | Image Subset Selection Using Gabor Filters and Neural Networks | cs.CV | An automatic method for the selection of subsets of images, both modern and
historic, out of a set of landmark large images collected from the Internet is
presented in this paper. This selection depends on the extraction of dominant
features using Gabor filtering. Features are selected carefully from a
preliminary imag... | computer science |
25,032 | Extraction of Protein Sequence Motif Information using PSO K-Means | cs.CV | The main objective of the paper is to find the motif information.The
functionalities of the proteins are ideally found from their motif information
which is extracted using various techniques like clustering with k-means,
hybrid k-means, self-organising maps, etc., in the literature. In this work
protein sequence infor... | computer science |
25,033 | Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor | cs.CV | In this paper, we focus on the two key aspects of multiple target tracking
problem: 1) designing an accurate affinity measure to associate detections and
2) implementing an efficient and accurate (near) online multiple target
tracking algorithm. As the first contribution, we introduce a novel Aggregated
Local Flow Desc... | computer science |
25,034 | Predicting Complete 3D Models of Indoor Scenes | cs.CV | One major goal of vision is to infer physical models of objects, surfaces,
and their layout from sensors. In this paper, we aim to interpret indoor scenes
from one RGBD image. Our representation encodes the layout of walls, which must
conform to a Manhattan structure but is otherwise flexible, and the layout and
extent... | computer science |
25,035 | What Do Deep CNNs Learn About Objects? | cs.CV | Deep convolutional neural networks learn extremely powerful image
representations, yet most of that power is hidden in the millions of deep-layer
parameters. What exactly do these parameters represent? Recent work has started
to analyse CNN representations, finding that, e.g., they are invariant to some
2D transformati... | computer science |
25,036 | HEp-2 Cell Image Classification with Deep Convolutional Neural Networks | cs.CV | Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate
the diagnosis of many autoimmune diseases. This paper presents an automatic
framework for this classification task, by utilizing the deep convolutional
neural networks (CNNs) which have recently attracted intensive attention in
visual recogni... | computer science |
25,037 | A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category
Recognition | cs.CV | Despite the fact that object detection, 3D pose estimation, and sub-category
recognition are highly correlated tasks, they are usually addressed
independently from each other because of the huge space of parameters. To
jointly model all of these tasks, we propose a coarse-to-fine hierarchical
representation, where each... | computer science |
25,038 | Car that Knows Before You Do: Anticipating Maneuvers via Learning
Temporal Driving Models | cs.CV | Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers... | computer science |
25,039 | siftservice.com - Turning a Computer Vision algorithm into a World Wide
Web Service | cs.CV | Image features detection and description is a longstanding topic in computer
vision and pattern recognition areas. The Scale Invariant Feature Transform
(SIFT) is probably the most popular and widely demanded feature descriptor
which facilitates a variety of computer vision applications such as image
registration, obje... | computer science |
25,040 | High Density Noise Removal by Cascading Algorithms | cs.CV | An advanced non-linear cascading filter algorithm for the removal of high
density salt and pepper noise from the digital images is proposed. The proposed
method consists of two stages. The first stage Decision base Median Filter
(DMF) acts as the preliminary noise removal algorithm. The second stage is
either Modified ... | computer science |
25,041 | Appearance-Based Gaze Estimation in the Wild | cs.CV | Appearance-based gaze estimation is believed to work well in real-world
settings, but existing datasets have been collected under controlled laboratory
conditions and methods have been not evaluated across multiple datasets. In
this work we study appearance-based gaze estimation in the wild. We present the
MPIIGaze dat... | computer science |
25,042 | Joint Learning of Distributed Representations for Images and Texts | cs.CV | This technical report provides extra details of the deep multimodal
similarity model (DMSM) which was proposed in (Fang et al. 2015,
arXiv:1411.4952). The model is trained via maximizing global semantic
similarity between images and their captions in natural language using the
public Microsoft COCO database, which cons... | computer science |
25,043 | Multiple Measurements and Joint Dimensionality Reduction for Large Scale
Image Search with Short Vectors - Extended Version | cs.CV | This paper addresses the construction of a short-vector (128D) image
representation for large-scale image and particular object retrieval. In
particular, the method of joint dimensionality reduction of multiple
vocabularies is considered. We study a variety of vocabulary generation
techniques: different k-means initial... | computer science |
25,044 | Improving Object Detection with Deep Convolutional Networks via Bayesian
Optimization and Structured Prediction | cs.CV | Object detection systems based on the deep convolutional neural network (CNN)
have recently made ground- breaking advances on several object detection
benchmarks. While the features learned by these high-capacity neural networks
are discriminative for categorization, inaccurate localization is still a major
source of e... | computer science |
25,045 | A Novel Approach to Develop a New Hybrid Technique for Trademark Image
Retrieval | cs.CV | Trademark Image Retrieval is playing a vital role as a part of CBIR System.
Trademark is of great significance because it carries the status value of any
company. To retrieve such a fake or copied trademark we design a retrieval
system which is based on hybrid techniques. It contains a mixture of two
different feature ... | computer science |
25,046 | Clustering Assisted Fundamental Matrix Estimation | cs.CV | In computer vision, the estimation of the fundamental matrix is a basic
problem that has been extensively studied. The accuracy of the estimation
imposes a significant influence on subsequent tasks such as the camera
trajectory determination and 3D reconstruction. In this paper we propose a new
method for fundamental m... | computer science |
25,047 | Simultaneous Feature Learning and Hash Coding with Deep Neural Networks | cs.CV | Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes.... | computer science |
25,048 | Image Denoising Using Low Rank Minimization With Modified Noise
Estimation | cs.CV | Recently, the application of low rank minimization to image denoising has
shown remarkable denoising results which are equivalent or better than those of
the existing state-of-the-art algorithms. However, due to iterative nature of
low rank optimization, estimation of residual noise is an essential requirement
after ea... | computer science |
25,049 | Sketch-based 3D Shape Retrieval using Convolutional Neural Networks | cs.CV | Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections ... | computer science |
25,050 | Efficient Scene Text Localization and Recognition with Local Character
Refinement | cs.CV | An unconstrained end-to-end text localization and recognition method is
presented. The method detects initial text hypothesis in a single pass by an
efficient region-based method and subsequently refines the text hypothesis
using a more robust local text model, which deviates from the common assumption
of region-based ... | computer science |
25,051 | Background Subtraction via Generalized Fused Lasso Foreground Modeling | cs.CV | Background Subtraction (BS) is one of the key steps in video analysis. Many
background models have been proposed and achieved promising performance on
public data sets. However, due to challenges such as illumination change,
dynamic background etc. the resulted foreground segmentation often consists of
holes as well as... | computer science |
25,052 | Text Localization in Video Using Multiscale Weber's Local Descriptor | cs.CV | In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the lo... | computer science |
25,053 | Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos | cs.CV | Non-extractive fish abundance estimation with the aid of visual analysis has
drawn increasing attention. Unstable illumination, ubiquitous noise and low
frame rate video capturing in the underwater environment, however, make
conventional tracking methods unreliable. In this paper, we present a multiple
fish tracking sy... | computer science |
25,054 | Deep convolutional networks for pancreas segmentation in CT imaging | cs.CV | Automatic organ segmentation is an important prerequisite for many
computer-aided diagnosis systems. The high anatomical variability of organs in
the abdomen, such as the pancreas, prevents many segmentation methods from
achieving high accuracies when compared to other segmentation of organs like
the liver, heart or ki... | computer science |
25,055 | Anatomy-specific classification of medical images using deep
convolutional nets | cs.CV | Automated classification of human anatomy is an important prerequisite for
many computer-aided diagnosis systems. The spatial complexity and variability
of anatomy throughout the human body makes classification difficult. "Deep
learning" methods such as convolutional networks (ConvNets) outperform other
state-of-the-ar... | computer science |
25,056 | FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave
Infrared | cs.CV | Cameras for imaging in short and mid-wave infrared spectra are significantly
more expensive than their counterparts in visible imaging. As a result,
high-resolution imaging in those spectrum remains beyond the reach of most
consumers. Over the last decade, compressive sensing (CS) has emerged as a
potential means to re... | computer science |
25,057 | Segmentation of Subspaces in Sequential Data | cs.CV | We propose Ordered Subspace Clustering (OSC) to segment data drawn from a
sequentially ordered union of subspaces. Similar to Sparse Subspace Clustering
(SSC) we formulate the problem as one of finding a sparse representation but
include an additional penalty term to take care of sequential data. We test our
method on ... | computer science |
25,058 | Color Constancy Using CNNs | cs.CV | In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling,... | computer science |
25,059 | Biometrics for Child Vaccination and Welfare: Persistence of Fingerprint
Recognition for Infants and Toddlers | cs.CV | With a number of emerging applications requiring biometric recognition of
children (e.g., tracking child vaccination schedules, identifying missing
children and preventing newborn baby swaps in hospitals), investigating the
temporal stability of biometric recognition accuracy for children is important.
The persistence ... | computer science |
25,060 | Understanding the Fisher Vector: a multimodal part model | cs.CV | Fisher Vectors and related orderless visual statistics have demonstrated
excellent performance in object detection, sometimes superior to established
approaches such as the Deformable Part Models. However, it remains unclear how
these models can capture complex appearance variations using visual codebooks
of limited si... | computer science |
25,061 | Visual Recognition Using Directional Distribution Distance | cs.CV | In computer vision, an entity such as an image or video is often represented
as a set of instance vectors, which can be SIFT, motion, or deep learning
feature vectors extracted from different parts of that entity. Thus, it is
essential to design efficient and effective methods to compare two sets of
instance vectors. E... | computer science |
25,062 | DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets | cs.CV | Most of the approaches for discovering visual attributes in images demand
significant supervision, which is cumbersome to obtain. In this paper, we aim
to discover visual attributes in a weakly supervised setting that is commonly
encountered with contemporary image search engines. Deep Convolutional Neural
Networks (CN... | computer science |
25,063 | Learning discriminative trajectorylet detector sets for accurate
skeleton-based action recognition | cs.CV | The introduction of low-cost RGB-D sensors has promoted the research in
skeleton-based human action recognition. Devising a representation suitable for
characterising actions on the basis of noisy skeleton sequences remains a
challenge, however. We here provide two insights into this challenge. First, we
show that the ... | computer science |
25,064 | Weakly Supervised Fine-Grained Image Categorization | cs.CV | In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object... | computer science |
25,065 | Exploiting Local Features from Deep Networks for Image Retrieval | cs.CV | Deep convolutional neural networks have been successfully applied to image
classification tasks. When these same networks have been applied to image
retrieval, the assumption has been made that the last layers would give the
best performance, as they do in classification. We show that for instance-level
image retrieval... | computer science |
25,066 | Deep Spatial Pyramid: The Devil is Once Again in the Details | cs.CV | In this paper we show that by carefully making good choices for various
detailed but important factors in a visual recognition framework using deep
learning features, one can achieve a simple, efficient, yet highly accurate
image classification system. We first list 5 important factors, based on both
existing researche... | computer science |
25,067 | Viewpoint distortion compensation in practical surveillance systems | cs.CV | Our aim is to estimate the perspective-effected geometric distortion of a
scene from a video feed. In contrast to all previous work we wish to achieve
this using from low-level, spatio-temporally local motion features used in
commercial semi-automatic surveillance systems. We: (i) describe a dense
algorithm which uses ... | computer science |
25,068 | Groupwise registration of aerial images | cs.CV | This paper addresses the task of time separated aerial image registration.
The ability to solve this problem accurately and reliably is important for a
variety of subsequent image understanding applications. The principal challenge
lies in the extent and nature of transient appearance variation that a land
area can und... | computer science |
25,069 | The adaptable buffer algorithm for high quantile estimation in
non-stationary data streams | cs.CV | The need to estimate a particular quantile of a distribution is an important
problem which frequently arises in many computer vision and signal processing
applications. For example, our work was motivated by the requirements of many
semi-automatic surveillance analytics systems which detect abnormalities in
close-circu... | computer science |
25,070 | Automatic Face Recognition from Video | cs.CV | The objective of this work is to automatically recognize faces from video
sequences in a realistic, unconstrained setup in which illumination conditions
are extreme and greatly changing, viewpoint and user motion pattern have a wide
variability, and video input is of low quality. At the centre of focus are face
appeara... | computer science |
25,071 | Key-Pose Prediction in Cyclic Human Motion | cs.CV | In this paper we study the problem of estimating innercyclic time intervals
within repetitive motion sequences of top-class swimmers in a swimming channel.
Interval limits are given by temporal occurrences of key-poses, i.e.
distinctive postures of the body. A key-pose is defined by means of only one or
two specific fe... | computer science |
25,072 | Adaptive Compressive Tracking via Online Vector Boosting Feature
Selection | cs.CV | Recently, the compressive tracking (CT) method has attracted much attention
due to its high efficiency, but it cannot well deal with the large scale target
appearance variations due to its data-independent random projection matrix that
results in less discriminative features. To address this issue, in this paper
we pro... | computer science |
25,073 | A robust and efficient video representation for action recognition | cs.CV | This paper introduces a state-of-the-art video representation and applies it
to efficient action recognition and detection. We first propose to improve the
popular dense trajectory features by explicit camera motion estimation. More
specifically, we extract feature point matches between frames using SURF
descriptors an... | computer science |
25,074 | Median and Mode Ellipse Parameterization for Robust Contour Fitting | cs.CV | Problems that require the parameterization of closed contours arise
frequently in computer vision applications. This article introduces a new curve
parameterization algorithm that is able to fit a closed curve to a set of
points while being robust to the presence of outliers and occlusions in the
data. This robustness ... | computer science |
25,075 | Combining local regularity estimation and total variation optimization
for scale-free texture segmentation | cs.CV | Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regula... | computer science |
25,076 | LOAD: Local Orientation Adaptive Descriptor for Texture and Material
Classification | cs.CV | In this paper, we propose a novel local feature, called Local Orientation
Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD,
we proposed to define point description on an Adaptive Coordinate System (ACS),
adopt a binary sequence descriptor to capture relationships between one point
and its ne... | computer science |
25,077 | Edge Detection Based on Global and Local Parameters of the Image | cs.CV | This paper presents an edge detection method based on global and local
parameters of the image, which produces satisfactory results on the edge
detection of complex images and has a simple structure for execution. The local
and global parameters of the image are arithmetic means and standard
deviations, the former acqu... | computer science |
25,078 | Understanding and Diagnosing Visual Tracking Systems | cs.CV | Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker ... | computer science |
25,079 | Object Detection Networks on Convolutional Feature Maps | cs.CV | Most object detectors contain two important components: a feature extractor
and an object classifier. The feature extractor has rapidly evolved with
significant research efforts leading to better deep convolutional
architectures. The object classifier, however, has not received much attention
and many recent systems (l... | computer science |
25,080 | Online Adaptive Hidden Markov Model for Multi-Tracker Fusion | cs.CV | In this paper, we propose a novel method for visual object tracking called
HMMTxD. The method fuses observations from complementary out-of-the box
trackers and a detector by utilizing a hidden Markov model whose latent states
correspond to a binary vector expressing the failure of individual trackers.
The Markov model ... | computer science |
25,081 | Sparse Radial Sampling LBP for Writer Identification | cs.CV | In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer ... | computer science |
25,082 | Robust Principal Component Analysis on Graphs | cs.CV | Principal Component Analysis (PCA) is the most widely used tool for linear
dimensionality reduction and clustering. Still it is highly sensitive to
outliers and does not scale well with respect to the number of data samples.
Robust PCA solves the first issue with a sparse penalty term. The second issue
can be handled w... | computer science |
25,083 | High-for-Low and Low-for-High: Efficient Boundary Detection from Deep
Object Features and its Applications to High-Level Vision | cs.CV | Most of the current boundary detection systems rely exclusively on low-level
features, such as color and texture. However, perception studies suggest that
humans employ object-level reasoning when judging if a particular pixel is a
boundary. Inspired by this observation, in this work we show how to predict
boundaries b... | computer science |
25,084 | An Elastic Image Registration Approach for Wireless Capsule Endoscope
Localization | cs.CV | Wireless Capsule Endoscope (WCE) is an innovative imaging device that permits
physicians to examine all the areas of the Gastrointestinal (GI) tract. It is
especially important for the small intestine, where traditional invasive
endoscopies cannot reach. Although WCE represents an extremely important
advance in medical... | computer science |
25,085 | Person Re-identification with Correspondence Structure Learning | cs.CV | This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera p... | computer science |
25,086 | Evolving Fuzzy Image Segmentation with Self-Configuration | cs.CV | Current image segmentation techniques usually require that the user tune
several parameters in order to obtain maximum segmentation accuracy, a
computationally inefficient approach, especially when a large number of images
must be processed sequentially in daily practice. The use of evolving fuzzy
systems for designing... | computer science |
25,087 | Holistically-Nested Edge Detection | cs.CV | We develop a new edge detection algorithm that tackles two important issues
in this long-standing vision problem: (1) holistic image training and
prediction; and (2) multi-scale and multi-level feature learning. Our proposed
method, holistically-nested edge detection (HED), performs image-to-image
prediction by means o... | computer science |
25,088 | Depth-based hand pose estimation: methods, data, and challenges | cs.CV | Hand pose estimation has matured rapidly in recent years. The introduction of
commodity depth sensors and a multitude of practical applications have spurred
new advances. We provide an extensive analysis of the state-of-the-art,
focusing on hand pose estimation from a single depth frame. To do so, we have
implemented a... | computer science |
25,089 | Situational Object Boundary Detection | cs.CV | Intuitively, the appearance of true object boundaries varies from image to
image. Hence the usual monolithic approach of training a single boundary
predictor and applying it to all images regardless of their content is bound to
be suboptimal. In this paper we therefore propose situational object boundary
detection: We ... | computer science |
25,090 | Local Variation as a Statistical Hypothesis Test | cs.CV | The goal of image oversegmentation is to divide an image into several pieces,
each of which should ideally be part of an object. One of the simplest and yet
most effective oversegmentation algorithms is known as local variation (LV)
(Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and
show th... | computer science |
25,091 | Semantic Motion Segmentation Using Dense CRF Formulation | cs.CV | While the literature has been fairly dense in the areas of scene
understanding and semantic labeling there have been few works that make use of
motion cues to embellish semantic performance and vice versa. In this paper, we
address the problem of semantic motion segmentation, and show how semantic and
motion priors aug... | computer science |
25,092 | Object Level Deep Feature Pooling for Compact Image Representation | cs.CV | Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we de... | computer science |
25,093 | WxBS: Wide Baseline Stereo Generalizations | cs.CV | We have presented a new problem -- the wide multiple baseline stereo (WxBS)
-- which considers matching of images that simultaneously differ in more than
one image acquisition factor such as viewpoint, illumination, sensor type or
where object appearance changes significantly, e.g. over time. A new dataset
with the gro... | computer science |
25,094 | Differential Recurrent Neural Networks for Action Recognition | cs.CV | The long short-term memory (LSTM) neural network is capable of processing
complex sequential information since it utilizes special gating schemes for
learning representations from long input sequences. It has the potential to
model any sequential time-series data, where the current hidden state has to be
considered in ... | computer science |
25,095 | Adaptive Locally Affine-Invariant Shape Matching | cs.CV | Matching deformable objects using their shapes is an important problem in
computer vision since shape is perhaps the most distinguishable characteristic
of an object. The problem is difficult due to many factors such as intra-class
variations, local deformations, articulations, viewpoint changes and missed and
extraneo... | computer science |
25,096 | SIFT Vs SURF: Quantifying the Variation in Transformations | cs.CV | This paper studies the robustness of SIFT and SURF against different image
transforms (rigid body, similarity, affine and projective) by quantitatively
analyzing the variations in the extent of transformations. Previous studies
have been comparing the two techniques on absolute transformations rather than
the specific ... | computer science |
25,097 | TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking | cs.CV | Traditional eye tracking requires specialized hardware, which means
collecting gaze data from many observers is expensive, tedious and slow.
Therefore, existing saliency prediction datasets are order-of-magnitudes
smaller than typical datasets for other vision recognition tasks. The small
size of these datasets limits ... | computer science |
25,098 | Fast Dictionary Matching for Content-based Image Retrieval | cs.CV | This paper describes a method for searching for common sets of descriptors
between collections of images. The presented method operates on local interest
keypoints, which are generated using the SURF algorithm. The use of a
dictionary of descriptors allowed achieving good performance of the
content-based image retrieva... | computer science |
25,099 | Detection and Recognition of Malaysian Special License Plate Based On
SIFT Features | cs.CV | Automated car license plate recognition systems are developed and applied for
purpose of facilitating the surveillance, law enforcement, access control and
intelligent transportation monitoring with least human intervention. In this
paper, an algorithm based on SIFT feature points clustering and matching is
proposed to... | computer science |
25,100 | Compression Artifacts Reduction by a Deep Convolutional Network | cs.CV | Lossy compression introduces complex compression artifacts, particularly the
blocking artifacts, ringing effects and blurring. Existing algorithms either
focus on removing blocking artifacts and produce blurred output, or restores
sharpened images that are accompanied with ringing effects. Inspired by the
deep convolut... | computer science |
25,101 | SegSALSA-STR: A convex formulation to supervised hyperspectral image
segmentation using hidden fields and structure tensor regularization | cs.CV | We present a supervised hyperspectral image segmentation algorithm based on a
convex formulation of a marginal maximum a posteriori segmentation with hidden
fields and structure tensor regularization: Segmentation via the Constraint
Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization
(SegSALSA-STR).... | computer science |
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