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24,102 | Microscopic Muscle Image Enhancement | cs.CV | We propose a robust image enhancement algorithm dedicated for muscle fiber
specimen images captured by optical microscopes. Blur or out of focus problems
are prevalent in muscle images during the image acquisition stage. Traditional
image deconvolution methods do not work since they assume the blur kernels are
known an... | computer science |
24,103 | 3D Shape Induction from 2D Views of Multiple Objects | cs.CV | In this paper we investigate the problem of inducing a distribution over
three-dimensional structures given two-dimensional views of multiple objects
taken from unknown viewpoints. Our approach called "projective generative
adversarial networks" (PrGANs) trains a deep generative model of 3D shapes
whose projections mat... | computer science |
24,104 | Deep Learning on Lie Groups for Skeleton-based Action Recognition | cs.CV | In recent years, skeleton-based action recognition has become a popular 3D
classification problem. State-of-the-art methods typically first represent each
motion sequence as a high-dimensional trajectory on a Lie group with an
additional dynamic time warping, and then shallowly learn favorable Lie group
features. In th... | computer science |
24,105 | Learning a No-Reference Quality Metric for Single-Image Super-Resolution | cs.CV | Numerous single-image super-resolution algorithms have been proposed in the
literature, but few studies address the problem of performance evaluation based
on visual perception. While most super-resolution images are evaluated by
fullreference metrics, the effectiveness is not clear and the required
ground-truth images... | computer science |
24,106 | Parsing Images of Overlapping Organisms with Deep Singling-Out Networks | cs.CV | This work is motivated by the mostly unsolved task of parsing biological
images with multiple overlapping articulated model organisms (such as worms or
larvae). We present a general approach that separates the two main challenges
associated with such data, individual object shape estimation and object groups
disentangl... | computer science |
24,107 | Dual Deep Network for Visual Tracking | cs.CV | Visual tracking addresses the problem of identifying and localizing an
unknown target in a video given the target specified by a bounding box in the
first frame. In this paper, we propose a dual network to better utilize
features among layers for visual tracking. It is observed that features in
higher layers encode sem... | computer science |
24,108 | X-ray In-Depth Decomposition: Revealing The Latent Structures | cs.CV | X-ray radiography is the most readily available imaging modality and has a
broad range of applications that spans from diagnosis to intra-operative
guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation
of the hidden and obscured anatomy in X-ray images remains a challenge and
often requires hig... | computer science |
24,109 | Cross-Modal Manifold Learning for Cross-modal Retrieval | cs.CV | This paper presents a new scalable algorithm for cross-modal similarity
preserving retrieval in a learnt manifold space. Unlike existing approaches
that compromise between preserving global and local geometries, the proposed
technique respects both simultaneously during manifold alignment. The global
topologies are mai... | computer science |
24,110 | Active and Continuous Exploration with Deep Neural Networks and Expected
Model Output Changes | cs.CV | The demands on visual recognition systems do not end with the complexity
offered by current large-scale image datasets, such as ImageNet. In
consequence, we need curious and continuously learning algorithms that actively
acquire knowledge about semantic concepts which are present in available
unlabeled data. As a step ... | computer science |
24,111 | Few-Shot Object Recognition from Machine-Labeled Web Images | cs.CV | With the tremendous advances of Convolutional Neural Networks (ConvNets) on
object recognition, we can now obtain reliable enough machine-labeled
annotations easily by predictions from off-the-shelf ConvNets. In this work, we
present an abstraction memory based framework for few-shot learning, building
upon machine-lab... | computer science |
24,112 | Photo-Quality Evaluation based on Computational Aesthetics: Review of
Feature Extraction Techniques | cs.CV | Researchers try to model the aesthetic quality of photographs into low and
high- level features, drawing inspiration from art theory, psychology and
marketing. We attempt to describe every feature extraction measure employed in
the above process. The contribution of this literature review is the taxonomy
of each featur... | computer science |
24,113 | Large-Scale Image Retrieval with Attentive Deep Local Features | cs.CV | We propose an attentive local feature descriptor suitable for large-scale
image retrieval, referred to as DELF (DEep Local Feature). The new feature is
based on convolutional neural networks, which are trained only with image-level
annotations on a landmark image dataset. To identify semantically useful local
features ... | computer science |
24,114 | Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic
Images | cs.CV | Distinguishing subtle differences in attributes is valuable, yet learning to
make visual comparisons remains non-trivial. Not only is the number of possible
comparisons quadratic in the number of training images, but also access to
images adequately spanning the space of fine-grained visual differences is
limited. We p... | computer science |
24,115 | Asynchronous Temporal Fields for Action Recognition | cs.CV | Actions are more than just movements and trajectories: we cook to eat and we
hold a cup to drink from it. A thorough understanding of videos requires going
beyond appearance modeling and necessitates reasoning about the sequence of
activities, as well as the higher-level constructs such as intentions. But how
do we mod... | computer science |
24,116 | Feature Encoding in Band-limited Distributed Surveillance Systems | cs.CV | Distributed surveillance systems have become popular in recent years due to
security concerns. However, transmitting high dimensional data in
bandwidth-limited distributed systems becomes a major challenge. In this paper,
we address this issue by proposing a novel probabilistic algorithm based on the
divergence between... | computer science |
24,117 | Fractal Descriptors of Texture Images Based on the Triangular Prism
Dimension | cs.CV | This work presents a novel descriptor for texture images based on fractal
geometry and its application to image analysis. The descriptors are provided by
estimating the triangular prism fractal dimension under different scales with a
weight exponential parameter, followed by dimensionality reduction using
Karhunen-Lo\`... | computer science |
24,118 | Binary Distance Transform to Improve Feature Extraction | cs.CV | To recognize textures many methods have been developed along the years.
However, texture datasets may be hard to be classified due to artefacts such as
a variety of scale, illumination and noise. This paper proposes the application
of binary distance transform on the original dataset to add information to
texture repre... | computer science |
24,119 | Exploring Structure for Long-Term Tracking of Multiple Objects in Sports
Videos | cs.CV | In this paper, we propose a novel approach for exploiting structural
relations to track multiple objects that may undergo long-term occlusion and
abrupt motion. We use a model-free approach that relies only on annotations
given in the first frame of the video to track all the objects online, i.e.
without knowledge from... | computer science |
24,120 | High Performance Software in Multidimensional Reduction Methods for
Image Processing with Application to Ancient Manuscripts | cs.CV | Multispectral imaging is an important technique for improving the readability
of written or printed text where the letters have faded, either due to
deliberate erasing or simply due to the ravages of time. Often the text can be
read simply by looking at individual wavelengths, but in some cases the images
need further ... | computer science |
24,121 | Efficiently Computing Piecewise Flat Embeddings for Data Clustering and
Image Segmentation | cs.CV | Image segmentation is a popular area of research in computer vision that has
many applications in automated image processing. A recent technique called
piecewise flat embeddings (PFE) has been proposed for use in image
segmentation; PFE transforms image pixel data into a lower dimensional
representation where similar p... | computer science |
24,122 | Deeply Aggregated Alternating Minimization for Image Restoration | cs.CV | Regularization-based image restoration has remained an active research topic
in computer vision and image processing. It often leverages a guidance signal
captured in different fields as an additional cue. In this work, we present a
general framework for image restoration, called deeply aggregated alternating
minimizat... | computer science |
24,123 | 3D Human Pose Estimation = 2D Pose Estimation + Matching | cs.CV | We explore 3D human pose estimation from a single RGB image. While many
approaches try to directly predict 3D pose from image measurements, we explore
a simple architecture that reasons through intermediate 2D pose predictions.
Our approach is based on two key observations (1) Deep neural nets have
revolutionized 2D po... | computer science |
24,124 | Wide-Slice Residual Networks for Food Recognition | cs.CV | Food diary applications represent a tantalizing market. Such applications,
based on image food recognition, opened to new challenges for computer vision
and pattern recognition algorithms. Recent works in the field are focusing
either on hand-crafted representations or on learning these by exploiting deep
neural networ... | computer science |
24,125 | End-to-End Pedestrian Collision Warning System based on a Convolutional
Neural Network with Semantic Segmentation | cs.CV | Traditional pedestrian collision warning systems sometimes raise alarms even
when there is no danger (e.g., when all pedestrians are walking on the
sidewalk). These false alarms can make it difficult for drivers to concentrate
on their driving. In this paper, we propose a novel framework for an end-to-end
pedestrian co... | computer science |
24,126 | Deep Motion Features for Visual Tracking | cs.CV | Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite ... | computer science |
24,127 | Dynamic Action Recognition: A convolutional neural network model for
temporally organized joint location data | cs.CV | Motivation: Recognizing human actions in a video is a challenging task which
has applications in various fields. Previous works in this area have either
used images from a 2D or 3D camera. Few have used the idea that human actions
can be easily identified by the movement of the joints in the 3D space and
instead used a... | computer science |
24,128 | Two decades of local binary patterns: A survey | cs.CV | Texture is an important characteristic for many types of images. In recent
years very discriminative and computationally efficient local texture
descriptors based on local binary patterns (LBP) have been developed, which has
led to significant progress in applying texture methods to different problems
and applications.... | computer science |
24,129 | Center-Focusing Multi-task CNN with Injected Features for Classification
of Glioma Nuclear Images | cs.CV | Classifying the various shapes and attributes of a glioma cell nucleus is
crucial for diagnosis and understanding the disease. We investigate automated
classification of glioma nuclear shapes and visual attributes using
Convolutional Neural Networks (CNNs) on pathology images of automatically
segmented nuclei. We propo... | computer science |
24,130 | From Images to 3D Shape Attributes | cs.CV | Our goal in this paper is to investigate properties of 3D shape that can be
determined from a single image. We define 3D shape attributes -- generic
properties of the shape that capture curvature, contact and occupied space. Our
first objective is to infer these 3D shape attributes from a single image. A
second objecti... | computer science |
24,131 | A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking
for Head-Mounted Virtual Reality Systems | cs.CV | We present a novel, automatic eye gaze tracking scheme inspired by smooth
pursuit eye motion while playing mobile games or watching virtual reality
contents. Our algorithm continuously calibrates an eye tracking system for a
head mounted display. This eliminates the need for an explicit calibration step
and automatical... | computer science |
24,132 | Unsupervised Place Discovery for Visual Place Classification | cs.CV | In this study, we explore the use of deep convolutional neural networks
(DCNNs) in visual place classification for robotic mapping and localization. An
open question is how to partition the robot's workspace into places to maximize
the performance (e.g., accuracy, precision, recall) of potential DCNN
classifiers. This ... | computer science |
24,133 | Temporal Tessellation: A Unified Approach for Video Analysis | cs.CV | We present a general approach to video understanding, inspired by semantic
transfer techniques that have been successfully used for 2D image analysis. Our
method considers a video to be a 1D sequence of clips, each one associated with
its own semantics. The nature of these semantics -- natural language captions
or othe... | computer science |
24,134 | Image biomarker standardisation initiative | cs.CV | While analysis of medical images has practically taken place since the first
image was recorded, high throughput analysis of medical images is a more recent
phenomenon. The aim of such a radiomics process is to provide decision support
based on medical imaging. Part of the radiomics process is the conversion of
image d... | computer science |
24,135 | Trilaminar Multiway Reconstruction Tree for Efficient Large Scale
Structure from Motion | cs.CV | Accuracy and efficiency are two key problems in large scale incremental
Structure from Motion (SfM). In this paper, we propose a unified framework to
divide the image set into clusters suitable for reconstruction as well as find
multiple reliable and stable starting points. Image partitioning performs in
two steps. Fir... | computer science |
24,136 | A Unified Framework for Tumor Proliferation Score Prediction in Breast
Histopathology | cs.CV | We present a unified framework to predict tumor proliferation scores from
breast histopathology whole slide images. Our system offers a fully automated
solution to predicting both a molecular data-based, and a mitosis
counting-based tumor proliferation score. The framework integrates three
modules, each fine-tuned to m... | computer science |
24,137 | Learning Motion Patterns in Videos | cs.CV | The problem of determining whether an object is in motion, irrespective of
camera motion, is far from being solved. We address this challenging task by
learning motion patterns in videos. The core of our approach is a fully
convolutional network, which is learned entirely from synthetic video
sequences, and their groun... | computer science |
24,138 | Beyond Holistic Object Recognition: Enriching Image Understanding with
Part States | cs.CV | Important high-level vision tasks such as human-object interaction, image
captioning and robotic manipulation require rich semantic descriptions of
objects at part level. Based upon previous work on part localization, in this
paper, we address the problem of inferring rich semantics imparted by an object
part in still ... | computer science |
24,139 | Top-down Visual Saliency Guided by Captions | cs.CV | Neural image/video captioning models can generate accurate descriptions, but
their internal process of mapping regions to words is a black box and therefore
difficult to explain. Top-down neural saliency methods can find important
regions given a high-level semantic task such as object classification, but
cannot use a ... | computer science |
24,140 | Automatic Identification of Scenedesmus Polymorphic Microalgae from
Microscopic Images | cs.CV | Microalgae counting is used to measure biomass quantity. Usually, it is
performed in a manual way using a Neubauer chamber and expert criterion, with
the risk of a high error rate. This paper addresses the methodology for
automatic identification of Scenedesmus microalgae (used in the methane
production and food indust... | computer science |
24,141 | Physically-Based Rendering for Indoor Scene Understanding Using
Convolutional Neural Networks | cs.CV | Indoor scene understanding is central to applications such as robot
navigation and human companion assistance. Over the last years, data-driven
deep neural networks have outperformed many traditional approaches thanks to
their representation learning capabilities. One of the bottlenecks in training
for better represent... | computer science |
24,142 | Deep Blind Compressed Sensing | cs.CV | This work addresses the problem of extracting deeply learned features
directly from compressive measurements. There has been no work in this area.
Existing deep learning tools only give good results when applied on the full
signal, that too usually after preprocessing. These techniques require the
signal to be reconstr... | computer science |
24,143 | Handwriting recognition using Cohort of LSTM and lexicon verification
with extremely large lexicon | cs.CV | State-of-the-art methods for handwriting recognition are based on Long Short
Term Memory (LSTM) recurrent neural networks (RNN), which now provides very
impressive character recognition performance. The character recognition is
generally coupled with a lexicon driven decoding process which integrates
dictionaries. Unfo... | computer science |
24,144 | A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search | cs.CV | Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many
areas of machine learning and data mining. During the past decade, numerous
hashing algorithms are proposed to solve this problem. Every proposed algorithm
claims outperform other state-of-the-art hashing algorithms. However, the
evaluation of ... | computer science |
24,145 | Hardware for Machine Learning: Challenges and Opportunities | cs.CV | Machine learning plays a critical role in extracting meaningful information
out of the zetabytes of sensor data collected every day. For some applications,
the goal is to analyze and understand the data to identify trends (e.g.,
surveillance, portable/wearable electronics); in other applications, the goal
is to take im... | computer science |
24,146 | Set2Model Networks: Learning Discriminatively To Learn Generative Models | cs.CV | We present a new "learning-to-learn"-type approach that enables rapid
learning of concepts from small-to-medium sized training sets and is primarily
designed for web-initialized image retrieval. At the core of our approach is a
deep architecture (a Set2Model network) that maps sets of examples to simple
generative prob... | computer science |
24,147 | Adversarial Examples Detection in Deep Networks with Convolutional
Filter Statistics | cs.CV | Deep learning has greatly improved visual recognition in recent years.
However, recent research has shown that there exist many adversarial examples
that can negatively impact the performance of such an architecture. This paper
focuses on detecting those adversarial examples by analyzing whether they come
from the same... | computer science |
24,148 | First-Person Activity Forecasting with Online Inverse Reinforcement
Learning | cs.CV | We address the problem of incrementally modeling and forecasting long-term
goals of a first-person camera wearer: what the user will do, where they will
go, and what goal they seek. In contrast to prior work in trajectory
forecasting, our algorithm, DARKO, goes further to reason about semantic states
(will I pick up an... | computer science |
24,149 | DARN: a Deep Adversial Residual Network for Intrinsic Image
Decomposition | cs.CV | We present a new deep supervised learning method for intrinsic decomposition
of a single image into its albedo and shading components. Our contributions are
based on a new fully convolutional neural network that estimates absolute
albedo and shading jointly. Our solution relies on a single end-to-end deep
sequence of r... | computer science |
24,150 | Understanding Non-optical Remote-sensed Images: Needs, Challenges and
Ways Forward | cs.CV | Non-optical remote-sensed images are going to be used more often in man-
aging disaster, crime and precision agriculture. With more small satellites and
unmanned air vehicles planning to carry radar and hyperspectral image sensors
there is going to be an abundance of such data in the recent future.
Understanding these ... | computer science |
24,151 | Two-stream convolutional neural network for accurate RGB-D fingertip
detection using depth and edge information | cs.CV | Accurate detection of fingertips in depth image is critical for
human-computer interaction. In this paper, we present a novel two-stream
convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge
image is extracted from raw depth image using random forest. Then the edge
information is combined with ... | computer science |
24,152 | Validation, comparison, and combination of algorithms for automatic
detection of pulmonary nodules in computed tomography images: the LUNA16
challenge | cs.CV | Automatic detection of pulmonary nodules in thoracic computed tomography (CT)
scans has been an active area of research for the last two decades. However,
there have only been few studies that provide a comparative performance
evaluation of different systems on a common database. We have therefore set up
the LUNA16 cha... | computer science |
24,153 | Active Learning and Proofreading for Delineation of Curvilinear
Structures | cs.CV | Many state-of-the-art delineation methods rely on supervised machine learning
algorithms. As a result, they require manually annotated training data, which
is tedious to obtain. Furthermore, even minor classification errors may
significantly affect the topology of the final result. In this paper we propose
a generic ap... | computer science |
24,154 | Blind restoration for non-uniform aerial images using non-local Retinex
model and shearlet-based higher-order regularization | cs.CV | Aerial images are often degraded by space-varying motion blur and
simultaneous uneven illumination. To recover high-quality aerial image from its
non-uniform version, we propose a novel patch-wise restoration approach based
on a key observation that the degree of blurring is inevitably affected by the
illuminated condi... | computer science |
24,155 | Correlation Preserving Sparse Coding Over Multi-level Dictionaries for
Image Denoising | cs.CV | In this letter, we propose a novel image denoising method based on
correlation preserving sparse coding. Because the instable and unreliable
correlations among basis set can limit the performance of the dictionary-driven
denoising methods, two effective regularized strategies are employed in the
coding process. Specifi... | computer science |
24,156 | Unsupervised Video Segmentation via Spatio-Temporally Nonlocal
Appearance Learning | cs.CV | Video object segmentation is challenging due to the factors like rapidly fast
motion, cluttered backgrounds, arbitrary object appearance variation and shape
deformation. Most existing methods only explore appearance information between
two consecutive frames, which do not make full use of the usefully long-term
nonloca... | computer science |
24,157 | PixelCNN Models with Auxiliary Variables for Natural Image Modeling | cs.CV | We study probabilistic models of natural images and extend the autoregressive
family of PixelCNN architectures by incorporating auxiliary variables.
Subsequently, we describe two new generative image models that exploit
different image transformations as auxiliary variables: a quantized grayscale
view of the image or a... | computer science |
24,158 | A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans | cs.CV | Deep neural networks have been widely adopted for automatic organ
segmentation from abdominal CT scans. However, the segmentation accuracy of
some small organs (e.g., the pancreas) is sometimes below satisfaction,
arguably because deep networks are easily disrupted by the complex and variable
background regions which o... | computer science |
24,159 | YOLO9000: Better, Faster, Stronger | cs.CV | We introduce YOLO9000, a state-of-the-art, real-time object detection system
that can detect over 9000 object categories. First we propose various
improvements to the YOLO detection method, both novel and drawn from prior
work. The improved model, YOLOv2, is state-of-the-art on standard detection
tasks like PASCAL VOC ... | computer science |
24,160 | Globally Optimal Object Tracking with Fully Convolutional Networks | cs.CV | Tracking is one of the most important but still difficult tasks in computer
vision and pattern recognition. The main difficulties in the tracking field are
appearance variation and occlusion. Most traditional tracking methods set the
parameters or templates to track target objects in advance and should be
modified acco... | computer science |
24,161 | Extracting Sub-Exposure Images from a Single Capture Through
Fourier-based Optical Modulation | cs.CV | Through pixel-wise optical coding of images during exposure time, it is
possible to extract sub-exposure images from a single capture. Such a
capability can be used for different purposes, including high-speed imaging,
high-dynamic-range imaging and compressed sensing. In this paper, we
demonstrate a sub-exposure image... | computer science |
24,162 | Signature of Geometric Centroids for 3D Local Shape Description and
Partial Shape Matching | cs.CV | Depth scans acquired from different views may contain nuisances such as
noise, occlusion, and varying point density. We propose a novel Signature of
Geometric Centroids descriptor, supporting direct shape matching on the scans,
without requiring any preprocessing such as scan denoising or converting into a
mesh. First,... | computer science |
24,163 | An Automated CNN Recommendation System for Image Classification Tasks | cs.CV | Nowadays the CNN is widely used in practical applications for image
classification task. However the design of the CNN model is very professional
work and which is very difficult for ordinary users. Besides, even for experts
of CNN, to select an optimal model for specific task may still need a lot of
time (to train man... | computer science |
24,164 | End-to-End Data Visualization by Metric Learning and Coordinate
Transformation | cs.CV | This paper presents a deep nonlinear metric learning framework for data
visualization on an image dataset. We propose the Triangular Similarity and
prove its equivalence to the Cosine Similarity in measuring a data pair. Based
on this novel similarity, a geometrically motivated loss function - the
triangular loss - is ... | computer science |
24,165 | Learning Non-Lambertian Object Intrinsics across ShapeNet Categories | cs.CV | We consider the non-Lambertian object intrinsic problem of recovering diffuse
albedo, shading, and specular highlights from a single image of an object.
We build a large-scale object intrinsics database based on existing 3D models
in the ShapeNet database. Rendered with realistic environment maps, millions of
synthet... | computer science |
24,166 | Robust LSTM-Autoencoders for Face De-Occlusion in the Wild | cs.CV | Face recognition techniques have been developed significantly in recent
years. However, recognizing faces with partial occlusion is still challenging
for existing face recognizers which is heavily desired in real-world
applications concerning surveillance and security. Although much research
effort has been devoted to ... | computer science |
24,167 | Superpixel Segmentation Using Gaussian Mixture Model | cs.CV | Superpixel segmentation algorithms are to partition an image into
perceptually coherence atomic regions by assigning every pixel a superpixel
label. Those algorithms have been wildly used as a preprocessing step in
computer vision works, as they can enormously reduce the number of entries of
subsequent algorithms. In t... | computer science |
24,168 | Symbolic Representation and Classification of Logos | cs.CV | In this paper, a model for classification of logos based on symbolic
representation of features is presented. The proposed model makes use of global
features of logo images such as color, texture, and shape features for
classification. The logo images are broadly classified into three different
classes, viz., logo imag... | computer science |
24,169 | Multivariate mixture model for myocardium segmentation combining
multi-source images | cs.CV | This paper proposes a method for simultaneous segmentation of multi-source
images, using the multivariate mixture model (MvMM) and maximum of
log-likelihood (LL) framework. The segmentation is a procedure of texture
classification, and the MvMM is used to model the joint intensity distribution
of the images. Specifical... | computer science |
24,170 | Semantic Video Segmentation by Gated Recurrent Flow Propagation | cs.CV | Semantic video segmentation is challenging due to the sheer amount of data
that needs to be processed and labeled in order to construct accurate models.
In this paper we present a deep, end-to-end trainable methodology to video
segmentation that is capable of leveraging information present in unlabeled
data in order to... | computer science |
24,171 | MARTA GANs: Unsupervised Representation Learning for Remote Sensing
Image Classification | cs.CV | With the development of deep learning, supervised learning has frequently
been adopted to classify remotely sensed images using convolutional networks
(CNNs). However, due to the limited amount of labeled data available,
supervised learning is often difficult to carry out. Therefore, we proposed an
unsupervised model c... | computer science |
24,172 | Unsupervised domain adaptation in brain lesion segmentation with
adversarial networks | cs.CV | Significant advances have been made towards building accurate automatic
segmentation systems for a variety of biomedical applications using machine
learning. However, the performance of these systems often degrades when they
are applied on new data that differ from the training data, for example, due to
variations in i... | computer science |
24,173 | Learning Visual N-Grams from Web Data | cs.CV | Real-world image recognition systems need to recognize tens of thousands of
classes that constitute a plethora of visual concepts. The traditional approach
of annotating thousands of images per class for training is infeasible in such
a scenario, prompting the use of webly supervised data. This paper explores the
train... | computer science |
24,174 | Deep Learning Logo Detection with Data Expansion by Synthesising Context | cs.CV | Logo detection in unconstrained images is challenging, particularly when only
very sparse labelled training images are accessible due to high labelling
costs. In this work, we describe a model training image synthesising method
capable of improving significantly logo detection performance when only a
handful of (e.g., ... | computer science |
24,175 | Rotation equivariant vector field networks | cs.CV | In many computer vision tasks, we expect a particular behavior of the output
with respect to rotations of the input image. If this relationship is
explicitly encoded, instead of treated as any other variation, the complexity
of the problem is decreased, leading to a reduction in the size of the required
model. In this ... | computer science |
24,176 | Action Recognition Based on Joint Trajectory Maps with Convolutional
Neural Networks | cs.CV | Convolutional Neural Networks (ConvNets) have recently shown promising
performance in many computer vision tasks, especially image-based recognition.
How to effectively apply ConvNets to sequence-based data is still an open
problem. This paper proposes an effective yet simple method to represent
spatio-temporal informa... | computer science |
24,177 | Shape Estimation from Defocus Cue for Microscopy Images via Belief
Propagation | cs.CV | In recent years, the usefulness of 3D shape estimation is being realized in
microscopic or close-range imaging, as the 3D information can further be used
in various applications. Due to limited depth of field at such small distances,
the defocus blur induced in images can provide information about the 3D shape
of the o... | computer science |
24,178 | Feedback Networks | cs.CV | Currently, the most successful learning models in computer vision are based
on learning successive representations followed by a decision layer. This is
usually actualized through feedforward multilayer neural networks, e.g.
ConvNets, where each layer forms one of such successive representations.
However, an alternativ... | computer science |
24,179 | A Unified Tensor-based Active Appearance Face Model | cs.CV | Appearance variations result in many difficulties in face image analysis. To
deal with this challenge, we present a Unified Tensor-based Active Appearance
Model (UT-AAM) for jointly modelling the geometry and texture information of 2D
faces. For each type of face information, namely shape and texture, we
construct a un... | computer science |
24,180 | Text Line Segmentation of Historical Documents: a Survey | cs.CV | There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specif... | computer science |
24,181 | Scalable Large-Margin Mahalanobis Distance Metric Learning | cs.CV | For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN)
classifiers and $ k $-means clustering, often their success heavily depends on
the metric used to calculate distances between different data points.
An effective solution for defining such a metric is to learn it from a set of
labeled training... | computer science |
24,182 | Text Region Extraction from Business Card Images for Mobile Devices | cs.CV | Designing a Business Card Reader (BCR) for mobile devices is a challenge to
the researchers because of huge deformation in acquired images, multiplicity in
nature of the business cards and most importantly the computational constraints
of the mobile devices. This paper presents a text extraction method designed in
our ... | computer science |
24,183 | Binarizing Business Card Images for Mobile Devices | cs.CV | Business card images are of multiple natures as these often contain graphics,
pictures and texts of various fonts and sizes both in background and
foreground. So, the conventional binarization techniques designed for document
images can not be directly applied on mobile devices. In this paper, we have
presented a fast ... | computer science |
24,184 | Properties of the Discrete Pulse Transform for Multi-Dimensional Arrays | cs.CV | This report presents properties of the Discrete Pulse Transform on
multi-dimensional arrays introduced by the authors two or so years ago. The
main result given here in Lemma 2.1 is also formulated in a paper to appear in
IEEE Transactions on Image Processing. However, the proof, being too technical,
was omitted there ... | computer science |
24,185 | An Offline Technique for Localization of License Plates for Indian
Commercial Vehicles | cs.CV | Automatic License Plate Recognition (ALPR) is a challenging area of research
due to its importance to variety of commercial applications. The overall
problem may be subdivided into two key modules, firstly, localization of
license plates from vehicle images, and secondly, optical character recognition
of extracted lice... | computer science |
24,186 | Clinical gait data analysis based on Spatio-Temporal features | cs.CV | Analysing human gait has found considerable interest in recent computer
vision research. So far, however, contributions to this topic exclusively dealt
with the tasks of person identification or activity recognition. In this paper,
we consider a different application for gait analysis and examine its use as a
means of ... | computer science |
24,187 | Nonlinear Filter Based Image Denoising Using AMF Approach | cs.CV | This paper proposes a new technique based on nonlinear Adaptive Median filter
(AMF) for image restoration. Image denoising is a common procedure in digital
image processing aiming at the removal of noise, which may corrupt an image
during its acquisition or transmission, while retaining its quality. This
procedure is t... | computer science |
24,188 | Facial Gesture Recognition Using Correlation And Mahalanobis Distance | cs.CV | Augmenting human computer interaction with automated analysis and synthesis
of facial expressions is a goal towards which much research effort has been
devoted recently. Facial gesture recognition is one of the important component
of natural human-machine interfaces; it may also be used in behavioural
science, security... | computer science |
24,189 | A GA based Window Selection Methodology to Enhance Window based Multi
wavelet transformation and thresholding aided CT image denoising technique | cs.CV | Image denoising is getting more significance, especially in Computed
Tomography (CT), which is an important and most common modality in medical
imaging. This is mainly due to that the effectiveness of clinical diagnosis
using CT image lies on the image quality. The denoising technique for CT images
using window-based M... | computer science |
24,190 | Investigation and Assessment of Disorder of Ultrasound B-mode Images | cs.CV | Digital image plays a vital role in the early detection of cancers, such as
prostate cancer, breast cancer, lungs cancer, cervical cancer. Ultrasound
imaging method is also suitable for early detection of the abnormality of
fetus. The accurate detection of region of interest in ultrasound image is
crucial. Since the re... | computer science |
24,191 | Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron | cs.CV | Handwritten numeral recognition is in general a benchmark problem of Pattern
Recognition and Artificial Intelligence. Compared to the problem of printed
numeral recognition, the problem of handwritten numeral recognition is
compounded due to variations in shapes and sizes of handwritten characters.
Considering all thes... | computer science |
24,192 | A comparative study of different feature sets for recognition of
handwritten Arabic numerals using a Multi Layer Perceptron | cs.CV | The work presents a comparative assessment of seven different feature sets
for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
(MLP) based classifier. The seven feature sets employed here consist of shadow
features, octant centroids, longest runs, angular distances, effective spans,
dynamic ce... | computer science |
24,193 | Pattern recognition using inverse resonance filtration | cs.CV | An approach to textures pattern recognition based on inverse resonance
filtration (IRF) is considered. A set of principal resonance harmonics of
textured image signal fluctuations eigen harmonic decomposition (EHD) is used
for the IRF design. It was shown that EHD is invariant to textured image linear
shift. The recogn... | computer science |
24,194 | Sliding window approach based Text Binarisation from Complex Textual
images | cs.CV | Text binarisation process classifies individual pixels as text or background
in the textual images. Binarization is necessary to bridge the gap between
localization and recognition by OCR. This paper presents Sliding window method
to binarise text from textual images with textured background. Suitable
preprocessing tec... | computer science |
24,195 | System-theoretic approach to image interest point detection | cs.CV | Interest point detection is a common task in various computer vision
applications. Although a big variety of detector are developed so far
computational efficiency of interest point based image analysis remains to be
the problem. Current paper proposes a system-theoretic approach to interest
point detection. Starting f... | computer science |
24,196 | A Comprehensive Review of Image Enhancement Techniques | cs.CV | Principle objective of Image enhancement is to process an image so that
result is more suitable than original image for specific application. Digital
image enhancement techniques provide a multitude of choices for improving the
visual quality of images. Appropriate choice of such techniques is greatly
influenced by the... | computer science |
24,197 | Land-cover Classification and Mapping for Eastern Himalayan State Sikkim | cs.CV | Area of classifying satellite imagery has become a challenging task in
current era where there is tremendous growth in settlement i.e. construction of
buildings, roads, bridges, dam etc. This paper suggests an improvised k-means
and Artificial Neural Network (ANN) classifier for land-cover mapping of
Eastern Himalayan ... | computer science |
24,198 | Active Testing for Face Detection and Localization | cs.CV | We provide a novel search technique, which uses a hierarchical model and a
mutual information gain heuristic to efficiently prune the search space when
localizing faces in images. We show exponential gains in computation over
traditional sliding window approaches, while keeping similar performance
levels. | computer science |
24,199 | The Video Genome | cs.CV | Fast evolution of Internet technologies has led to an explosive growth of
video data available in the public domain and created unprecedented challenges
in the analysis, organization, management, and control of such content. The
problems encountered in video analysis such as identifying a video in a large
database (e.g... | computer science |
24,200 | Tuning CLD Maps | cs.CV | The Coherence Length Diagram and the related maps have been shown to
represent a useful tool for image analysis. Setting threshold parameters is one
of the most important issues when dealing with such applications, as they
affect both the computability, which is outlined by the support map, and the
appearance of the co... | computer science |
24,201 | Robust multi-camera view face recognition | cs.CV | This paper presents multi-appearance fusion of Principal Component Analysis
(PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera
view offline face recognition (verification) system. The generalization of LDA
has been extended to establish correlations between the face classes in the
transform... | computer science |
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