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25,902 | A New Approach of Gray Images Binarization with Threshold Methods | cs.CV | The paper presents some aspects of the (gray level) image binarization
methods used in artificial vision systems. It is introduced a new approach of
gray level image binarization for artificial vision systems dedicated to
industrial automation temporal thresholding. In the first part of the paper are
extracted some lim... | computer science |
25,903 | Improving Human Activity Recognition Through Ranking and Re-ranking | cs.CV | We propose two well-motivated ranking-based methods to enhance the
performance of current state-of-the-art human activity recognition systems.
First, as an improvement over the classic power normalization method, we
propose a parameter-free ranking technique called rank normalization (RaN). RaN
normalizes each dimensio... | computer science |
25,904 | RNN Fisher Vectors for Action Recognition and Image Annotation | cs.CV | Recurrent Neural Networks (RNNs) have had considerable success in classifying
and predicting sequences. We demonstrate that RNNs can be effectively used in
order to encode sequences and provide effective representations. The
methodology we use is based on Fisher Vectors, where the RNNs are the
generative probabilistic ... | computer science |
25,905 | Action Recognition with Image Based CNN Features | cs.CV | Most of human actions consist of complex temporal compositions of more simple
actions. Action recognition tasks usually relies on complex handcrafted
structures as features to represent the human action model. Convolutional
Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for
designing handcra... | computer science |
25,906 | Deep Tracking: Visual Tracking Using Deep Convolutional Networks | cs.CV | In this paper, we study a discriminatively trained deep convolutional network
for the task of visual tracking. Our tracker utilizes both motion and
appearance features that are extracted from a pre-trained dual stream deep
convolution network. We show that the features extracted from our dual-stream
network can provide... | computer science |
25,907 | Cross-dimensional Weighting for Aggregated Deep Convolutional Features | cs.CV | We propose a simple and straightforward way of creating powerful image
representations via cross-dimensional weighting and aggregation of deep
convolutional neural network layer outputs. We first present a generalized
framework that encompasses a broad family of approaches and includes
cross-dimensional pooling and wei... | computer science |
25,908 | Learning the Correction for Multi-Path Deviations in Time-of-Flight
Cameras | cs.CV | The Multipath effect in Time-of-Flight(ToF) cameras still remains to be a
challenging problem that hinders further processing of 3D data information.
Based on the evidence from previous literature, we explored the possibility of
using machine learning techniques to correct this effect. Firstly, we created
two new datas... | computer science |
25,909 | Deep Learning-Based Image Kernel for Inductive Transfer | cs.CV | We propose a method to classify images from target classes with a small
number of training examples based on transfer learning from non-target classes.
Without using any more information than class labels for samples from
non-target classes, we train a Siamese net to estimate the probability of two
images to belong to ... | computer science |
25,910 | Deep Relative Attributes | cs.CV | Visual attributes are great means of describing images or scenes, in a way
both humans and computers understand. In order to establish a correspondence
between images and to be able to compare the strength of each property between
images, relative attributes were introduced. However, since their introduction,
hand-craf... | computer science |
25,911 | Unsupervised Temporal Segmentation of Repetitive Human Actions Based on
Kinematic Modeling and Frequency Analysis | cs.CV | In this paper, we propose a method for temporal segmentation of human
repetitive actions based on frequency analysis of kinematic parameters,
zero-velocity crossing detection, and adaptive k-means clustering. Since the
human motion data may be captured with different modalities which have
different temporal sampling ra... | computer science |
25,912 | Articulated Pose Estimation Using Hierarchical Exemplar-Based Models | cs.CV | Exemplar-based models have achieved great success on localizing the parts of
semi-rigid objects. However, their efficacy on highly articulated objects such
as humans is yet to be explored. Inspired by hierarchical object representation
and recent application of Deep Convolutional Neural Networks (DCNNs) on human
pose e... | computer science |
25,913 | Inside-Outside Net: Detecting Objects in Context with Skip Pooling and
Recurrent Neural Networks | cs.CV | It is well known that contextual and multi-scale representations are
important for accurate visual recognition. In this paper we present the
Inside-Outside Net (ION), an object detector that exploits information both
inside and outside the region of interest. Contextual information outside the
region of interest is int... | computer science |
25,914 | Learning Deep Features for Discriminative Localization | cs.CV | In this work, we revisit the global average pooling layer proposed in [13],
and shed light on how it explicitly enables the convolutional neural network to
have remarkable localization ability despite being trained on image-level
labels. While this technique was previously proposed as a means for
regularizing training,... | computer science |
25,915 | Compressed Dynamic Mode Decomposition for Background Modeling | cs.CV | We introduce the method of compressed dynamic mode decomposition (cDMD) for
background modeling. The dynamic mode decomposition (DMD) is a regression
technique that integrates two of the leading data analysis methods in use
today: Fourier transforms and singular value decomposition. Borrowing ideas
from compressed sens... | computer science |
25,916 | On the Relation between two Rotation Metrics | cs.CV | In their work "Global Optimization through Rotation Space Search", Richard
Hartley and Fredrik Kahl introduce a global optimization strategy for problems
in geometric computer vision, based on rotation space search using a
branch-and-bound algorithm. In its core, Lemma 2 of their publication is the
important foundation... | computer science |
25,917 | Instance-aware Semantic Segmentation via Multi-task Network Cascades | cs.CV | Semantic segmentation research has recently witnessed rapid progress, but
many leading methods are unable to identify object instances. In this paper, we
present Multi-task Network Cascades for instance-aware semantic segmentation.
Our model consists of three networks, respectively differentiating instances,
estimating... | computer science |
25,918 | Sparse Representation of a Blur Kernel for Blind Image Restoration | cs.CV | Blind image restoration is a non-convex problem which involves restoration of
images from an unknown blur kernel. The factors affecting the performance of
this restoration are how much prior information about an image and a blur
kernel are provided and what algorithm is used to perform the restoration task.
Prior infor... | computer science |
25,919 | Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised
Context | cs.CV | One of the prevalent learning tasks involving images is content-based image
classification. This is a difficult task especially because the low-level
features used to digitally describe images usually capture little information
about the semantics of the images. In this paper, we tackle this difficulty by
enriching the... | computer science |
25,920 | Context Driven Label Fusion for segmentation of Subcutaneous and
Visceral Fat in CT Volumes | cs.CV | Quantification of adipose tissue (fat) from computed tomography (CT) scans is
conducted mostly through manual or semi-automated image segmentation algorithms
with limited efficacy. In this work, we propose a completely unsupervised and
automatic method to identify adipose tissue, and then separate Subcutaneous
Adipose ... | computer science |
25,921 | Fine-grained Categorization and Dataset Bootstrapping using Deep Metric
Learning with Humans in the Loop | cs.CV | Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that h... | computer science |
25,922 | Shape and Spatially-Varying Reflectance Estimation From Virtual
Exemplars | cs.CV | This paper addresses the problem of estimating the shape of objects that
exhibit spatially-varying reflectance. We assume that multiple images of the
object are obtained under a fixed view-point and varying illumination, i.e.,
the setting of photometric stereo. At the core of our techniques is the
assumption that the B... | computer science |
25,923 | Multiregion Bilinear Convolutional Neural Networks for Person
Re-Identification | cs.CV | In this work we propose a new architecture for person re-identification. As
the task of re-identification is inherently associated with embedding learning
and non-rigid appearance description, our architecture is based on the deep
bilinear convolutional network (Bilinear-CNN) that has been proposed recently
for fine-gr... | computer science |
25,924 | Numerical Demultiplexing of Color Image Sensor Measurements via
Non-linear Random Forest Modeling | cs.CV | The simultaneous capture of imaging data at multiple wavelengths across the
electromagnetic spectrum is highly challenging, requiring complex and costly
multispectral image sensors. In this study, we introduce a comprehensive
framework for performing simultaneous multispectral imaging using conventional
image sensors w... | computer science |
25,925 | Large Scale Business Discovery from Street Level Imagery | cs.CV | Search with local intent is becoming increasingly useful due to the
popularity of the mobile device. The creation and maintenance of accurate
listings of local businesses worldwide is time consuming and expensive. In this
paper, we propose an approach to automatically discover businesses that are
visible on street leve... | computer science |
25,926 | Reconstruction of Enhanced Ultrasound Images From Compressed
Measurements Using Simultaneous Direction Method of Multipliers | cs.CV | High resolution ultrasound image reconstruction from a reduced number of
measurements is of great interest in ultrasound imaging, since it could enhance
both the frame rate and image resolution. Compressive deconvolution, combining
compressed sensing and image deconvolution, represents an interesting
possibility to con... | computer science |
25,927 | Effects of GIMP Retinex Filtering Evaluated by the Image Entropy | cs.CV | A GIMP Retinex filtering can be used for enhancing images, with good results
on foggy images, as recently discussed. Since this filter has some parameters
that can be adjusted to optimize the output image, several approaches can be
decided according to desired results. Here, as a criterion for optimizing the
filtering ... | computer science |
25,928 | Deformable Distributed Multiple Detector Fusion for Multi-Person
Tracking | cs.CV | This paper addresses fully automated multi-person tracking in complex
environments with challenging occlusion and extensive pose variations. Our
solution combines multiple detectors for a set of different regions of interest
(e.g., full-body and head) for multi-person tracking. The use of multiple
detectors leads to fe... | computer science |
25,929 | Face Hallucination using Linear Models of Coupled Sparse Support | cs.CV | Most face super-resolution methods assume that low-resolution and
high-resolution manifolds have similar local geometrical structure, hence learn
local models on the lowresolution manifolds (e.g. sparse or locally linear
embedding models), which are then applied on the high-resolution manifold.
However, the low-resolut... | computer science |
25,930 | Multiclass Classification of Cervical Cancer Tissues by Hidden Markov
Model | cs.CV | In this paper, we report a hidden Markov model based multiclass
classification of cervical cancer tissues. This model has been validated
directly over time series generated by the medium refractive index fluctuations
extracted from differential interference contrast images of healthy and
different stages of cancer tiss... | computer science |
25,931 | Modeling Colors of Single Attribute Variations with Application to Food
Appearance | cs.CV | This paper considers the intra-image color-space of an object or a scene when
these are subject to a dominant single-source of variation. The source of
variation can be intrinsic or extrinsic (i.e., imaging conditions) to the
object. We observe that the quantized colors for such objects typically lie on
a planar subspa... | computer science |
25,932 | Combining patch-based strategies and non-rigid registration-based label
fusion methods | cs.CV | The objective of this study is to develop a patch-based labeling method that
cooperates with a label fusion using non-rigid registrations. We present a
novel patch-based label fusion method, whose selected patches and their weights
are calculated from a combination of similarity measures between patches using
intensity... | computer science |
25,933 | Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction | cs.CV | Several methods have been proposed for large-scale 3D reconstruction from
large, unorganized image collections. A large reconstruction problem is
typically divided into multiple components which are reconstructed
independently using structure from motion (SFM) and later merged together.
Incremental SFM methods are most... | computer science |
25,934 | Neutro-Connectedness Cut | cs.CV | Interactive image segmentation is a challenging task and receives increasing
attention recently; however, two major drawbacks exist in interactive
segmentation approaches. First, the segmentation performance of ROI-based
methods is sensitive to the initial ROI: different ROIs may produce results
with great difference. ... | computer science |
25,935 | Quantized Convolutional Neural Networks for Mobile Devices | cs.CV | Recently, convolutional neural networks (CNN) have demonstrated impressive
performance in various computer vision tasks. However, high performance
hardware is typically indispensable for the application of CNN models due to
the high computation complexity, which prohibits their further extensions. In
this paper, we pro... | computer science |
25,936 | Harnessing the Deep Net Object Models for Enhancing Human Action
Recognition | cs.CV | In this study, the influence of objects is investigated in the scenario of
human action recognition with large number of classes. We hypothesize that the
objects the humans are interacting will have good say in determining the action
being performed. Especially, if the objects are non-moving, such as objects
appearing ... | computer science |
25,937 | Spatial Phase-Sweep: Increasing temporal resolution of transient imaging
using a light source array | cs.CV | Transient imaging or light-in-flight techniques capture the propagation of an
ultra-short pulse of light through a scene, which in effect captures the
optical impulse response of the scene. Recently, it has been shown that we can
capture transient images using commercially available Time-of-Flight (ToF)
systems such as... | computer science |
25,938 | Analysis of Vessel Connectivities in Retinal Images by Cortically
Inspired Spectral Clustering | cs.CV | Retinal images provide early signs of diabetic retinopathy, glaucoma, and
hypertension. These signs can be investigated based on microaneurysms or
smaller vessels. The diagnostic biomarkers are the change of vessel widths and
angles especially at junctions, which are investigated using the vessel
segmentation or tracki... | computer science |
25,939 | Local and global gestalt laws: A neurally based spectral approach | cs.CV | A mathematical model of figure-ground articulation is presented, taking into
account both local and global gestalt laws. The model is compatible with the
functional architecture of the primary visual cortex (V1). Particularly the
local gestalt law of good continuity is described by means of suitable
connectivity kernel... | computer science |
25,940 | Sparse Coding with Fast Image Alignment via Large Displacement Optical
Flow | cs.CV | Sparse representation-based classifiers have shown outstanding accuracy and
robustness in image classification tasks even with the presence of intense
noise and occlusion. However, it has been discovered that the performance
degrades significantly either when test image is not aligned with the
dictionary atoms or the d... | computer science |
25,941 | Instance-Level Segmentation for Autonomous Driving with Deep Densely
Connected MRFs | cs.CV | Our aim is to provide a pixel-wise instance-level labeling of a monocular
image in the context of autonomous driving. We build on recent work [Zhang et
al., ICCV15] that trained a convolutional neural net to predict instance
labeling in local image patches, extracted exhaustively in a stride from an
image. A simple Mar... | computer science |
25,942 | Car Segmentation and Pose Estimation using 3D Object Models | cs.CV | Image segmentation and 3D pose estimation are two key cogs in any algorithm
for scene understanding. However, state-of-the-art CRF-based models for image
segmentation rely mostly on 2D object models to construct top-down high-order
potentials. In this paper, we propose new top-down potentials for image
segmentation and... | computer science |
25,943 | Transformed Residual Quantization for Approximate Nearest Neighbor
Search | cs.CV | The success of product quantization (PQ) for fast nearest neighbor search
depends on the exponentially reduced complexities of both storage and
computation with respect to the codebook size. Recent efforts have been focused
on employing sophisticated optimization strategies, or seeking more effective
models. Residual q... | computer science |
25,944 | Multi-Instance Visual-Semantic Embedding | cs.CV | Visual-semantic embedding models have been recently proposed and shown to be
effective for image classification and zero-shot learning, by mapping images
into a continuous semantic label space. Although several approaches have been
proposed for single-label embedding tasks, handling images with multiple labels
(which i... | computer science |
25,945 | Seeing through the Human Reporting Bias: Visual Classifiers from Noisy
Human-Centric Labels | cs.CV | When human annotators are given a choice about what to label in an image,
they apply their own subjective judgments on what to ignore and what to
mention. We refer to these noisy "human-centric" annotations as exhibiting
human reporting bias. Examples of such annotations include image tags and
keywords found on photo s... | computer science |
25,946 | Deep Learning with S-shaped Rectified Linear Activation Units | cs.CV | Rectified linear activation units are important components for
state-of-the-art deep convolutional networks. In this paper, we propose a novel
S-shaped rectified linear activation unit (SReLU) to learn both convex and
non-convex functions, imitating the multiple function forms given by the two
fundamental laws, namely ... | computer science |
25,947 | Cost-based Feature Transfer for Vehicle Occupant Classification | cs.CV | Knowledge of human presence and interaction in a vehicle is of growing
interest to vehicle manufacturers for design and safety purposes. We present a
framework to perform the tasks of occupant detection and occupant
classification for automatic child locks and airbag suppression. It operates
for all passenger seats, us... | computer science |
25,948 | Do Less and Achieve More: Training CNNs for Action Recognition Utilizing
Action Images from the Web | cs.CV | Recently, attempts have been made to collect millions of videos to train CNN
models for action recognition in videos. However, curating such large-scale
video datasets requires immense human labor, and training CNNs on millions of
videos demands huge computational resources. In contrast, collecting action
images from t... | computer science |
25,949 | Mid-level Representation for Visual Recognition | cs.CV | Visual Recognition is one of the fundamental challenges in AI, where the goal
is to understand the semantics of visual data. Employing mid-level
representation, in particular, shifted the paradigm in visual recognition. The
mid-level image/video representation involves discovering and training a set of
mid-level visual... | computer science |
25,950 | Convolutional Architecture Exploration for Action Recognition and Image
Classification | cs.CV | Convolutional Architecture for Fast Feature Encoding (CAFFE) [11] is a
software package for the training, classifying, and feature extraction of
images. The UCF Sports Action dataset is a widely used machine learning dataset
that has 200 videos taken in 720x480 resolution of 9 different sporting
activities: diving, gol... | computer science |
25,951 | Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd | cs.CV | Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually d... | computer science |
25,952 | Adaptive Object Detection Using Adjacency and Zoom Prediction | cs.CV | State-of-the-art object detection systems rely on an accurate set of region
proposals. Several recent methods use a neural network architecture to
hypothesize promising object locations. While these approaches are
computationally efficient, they rely on fixed image regions as anchors for
predictions. In this paper we p... | computer science |
25,953 | Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from
Non-linear Measurements | cs.CV | This work addresses the problem of estimating proton density and T1 maps from
two partially sampled K-space scans such that the total acquisition time
remains approximately the same as a single scan. Existing multi parametric non
linear curve fitting techniques require a large number (8 or more) of echoes to
estimate t... | computer science |
25,954 | G-CNN: an Iterative Grid Based Object Detector | cs.CV | We introduce G-CNN, an object detection technique based on CNNs which works
without proposal algorithms. G-CNN starts with a multi-scale grid of fixed
bounding boxes. We train a regressor to move and scale elements of the grid
towards objects iteratively. G-CNN models the problem of object detection as
finding a path f... | computer science |
25,955 | Truncated Max-of-Convex Models | cs.CV | Truncated convex models (TCM) are a special case of pairwise random fields
that have been widely used in computer vision. However, by restricting the
order of the potentials to be at most two, they fail to capture useful image
statistics. We propose a natural generalization of TCM to high-order random
fields, which we ... | computer science |
25,956 | Learning Transferrable Knowledge for Semantic Segmentation with Deep
Convolutional Neural Network | cs.CV | We propose a novel weakly-supervised semantic segmentation algorithm based on
Deep Convolutional Neural Network (DCNN). Contrary to existing
weakly-supervised approaches, our algorithm exploits auxiliary segmentation
annotations available for different categories to guide segmentations on images
with only image-level c... | computer science |
25,957 | A Combined Deep-Learning and Deformable-Model Approach to Fully
Automatic Segmentation of the Left Ventricle in Cardiac MRI | cs.CV | Segmentation of the left ventricle (LV) from cardiac magnetic resonance
imaging (MRI) datasets is an essential step for calculation of clinical indices
such as ventricular volume and ejection fraction. In this work, we employ deep
learning algorithms combined with deformable models to develop and evaluate a
fully autom... | computer science |
25,958 | Assessment of texture measures susceptibility to noise in conventional
and contrast enhanced computed tomography lung tumour images | cs.CV | Noise is one of the major problems that hinder an effective texture analysis
of disease in medical images, which may cause variability in the reported
diagnosis. In this paper seven texture measurement methods (two wavelet, two
model and three statistical based) were applied to investigate their
susceptibility to subtl... | computer science |
25,959 | Texture measures combination for improved meningioma classification of
histopathological images | cs.CV | Providing an improved technique which can assist pathologists in correctly
classifying meningioma tumours with a significant accuracy is our main
objective. The proposed technique, which is based on optimum texture measure
combination, inspects the separability of the RGB colour channels and selects
the channel which b... | computer science |
25,960 | A Multiresolution Clinical Decision Support System Based on Fractal
Model Design for Classification of Histological Brain Tumours | cs.CV | Tissue texture is known to exhibit a heterogeneous or non-stationary nature,
therefore using a single resolution approach for optimum classification might
not suffice. A clinical decision support system that exploits the subband
textural fractal characteristics for best bases selection of meningioma brain
histopatholog... | computer science |
25,961 | Part-Stacked CNN for Fine-Grained Visual Categorization | cs.CV | In the context of fine-grained visual categorization, the ability to
interpret models as human-understandable visual manuals is sometimes as
important as achieving high classification accuracy. In this paper, we propose
a novel Part-Stacked CNN architecture that explicitly explains the fine-grained
recognition process ... | computer science |
25,962 | Data Driven Robust Image Guided Depth Map Restoration | cs.CV | Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight
(ToF) are usually contaminated by missing data, noises and suffer from being of
low resolution. In this paper, we present a robust method for high-quality
restoration of a degraded depth map with the guidance of the corresponding
color image.... | computer science |
25,963 | Improving Facial Analysis and Performance Driven Animation through
Disentangling Identity and Expression | cs.CV | We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and... | computer science |
25,964 | Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method
and Gaussian Mixture Model Method | cs.CV | It is meaningful to detect outliers in traffic data for traffic management.
However, this is a massive task for people from large-scale database to
distinguish outliers. In this paper, we present two methods: Kernel Smoothing
Na\"ive Bayes (NB) method and Gaussian Mixture Model (GMM) method to
automatically detect any ... | computer science |
25,965 | Graph entropies in texture segmentation of images | cs.CV | We study the applicability of a set of texture descriptors introduced in
recent work by the author to texture-based segmentation of images. The texture
descriptors under investigation result from applying graph indices from
quantitative graph theory to graphs encoding the local structure of images. The
underlying graph... | computer science |
25,966 | MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on
Edge-Guided Interpolation | cs.CV | In interpretation of remote sensing images, it is possible that some images
which are supplied by different sensors become understandable. For better
visual perception of these images, it is essential to operate series of
pre-processing and elementary corrections and then operate a series of main
processing steps for m... | computer science |
25,967 | A framework for robust object multi-detection with a vote aggregation
and a cascade filtering | cs.CV | This paper presents a framework designed for the multi-object detection
purposes and adjusted for the application of product search on the market
shelves. The framework uses a single feedback loop and a pattern resizing
mechanism to demonstrate the top effectiveness of the state-of-the-art local
features. A high detect... | computer science |
25,968 | Robust Scene Text Recognition Using Sparse Coding based Features | cs.CV | In this paper, we propose an effective scene text recognition method using
sparse coding based features, called Histograms of Sparse Codes (HSC) features.
For character detection, we use the HSC features instead of using the
Histograms of Oriented Gradients (HOG) features. The HSC features are extracted
by computing sp... | computer science |
25,969 | Combined statistical and model based texture features for improved image
classification | cs.CV | This paper aims to improve the accuracy of texture classification based on
extracting texture features using five different texture methods and
classifying the patterns using a naive Bayesian classifier. Three
statistical-based and two model-based methods are used to extract texture
features from eight different textur... | computer science |
25,970 | Actor-Action Semantic Segmentation with Grouping Process Models | cs.CV | Actor-action semantic segmentation made an important step toward advanced
video understanding problems: what action is happening; who is performing the
action; and where is the action in space-time. Current models for this problem
are local, based on layered CRFs, and are unable to capture long-ranging
interaction of v... | computer science |
25,971 | LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing | cs.CV | Supervoxel segmentation has strong potential to be incorporated into early
video analysis as superpixel segmentation has in image analysis. However, there
are many plausible supervoxel methods and little understanding as to when and
where each is most appropriate. Indeed, we are not aware of a single
comparative study ... | computer science |
25,972 | Exploiting Local Structures with the Kronecker Layer in Convolutional
Networks | cs.CV | In this paper, we propose and study a technique to reduce the number of
parameters and computation time in convolutional neural networks. We use
Kronecker product to exploit the local structures within convolution and
fully-connected layers, by replacing the large weight matrices by combinations
of multiple Kronecker p... | computer science |
25,973 | Learning Local Image Descriptors with Deep Siamese and Triplet
Convolutional Networks by Minimising Global Loss Functions | cs.CV | Recent innovations in training deep convolutional neural network (ConvNet)
models have motivated the design of new methods to automatically learn local
image descriptors. The latest deep ConvNets proposed for this task consist of a
siamese network that is trained by penalising misclassification of pairs of
local image ... | computer science |
25,974 | Event Specific Multimodal Pattern Mining with Image-Caption Pairs | cs.CV | In this paper we describe a novel framework and algorithms for discovering
image patch patterns from a large corpus of weakly supervised image-caption
pairs generated from news events. Current pattern mining techniques attempt to
find patterns that are representative and discriminative, we stipulate that our
discovered... | computer science |
25,975 | Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model
Perspective | cs.CV | Many patch-based image denoising algorithms can be formulated as applying a
smoothing filter to the noisy image. Expressed as matrices, the smoothing
filters must be row normalized so that each row sums to unity. Surprisingly, if
we apply a column normalization before the row normalization, the performance
of the smoot... | computer science |
25,976 | Discriminative Sparsity for Sonar ATR | cs.CV | Advancements in Sonar image capture have enabled researchers to apply
sophisticated object identification algorithms in order to locate targets of
interest in images such as mines. Despite progress in this field, modern sonar
automatic target recognition (ATR) approaches lack robustness to the amount of
noise one would... | computer science |
25,977 | Tensor Sparse and Low-Rank based Submodule Clustering Method for
Multi-way Data | cs.CV | A new submodule clustering method via sparse and low-rank representation for
multi-way data is proposed in this paper. Instead of reshaping multi-way data
into vectors, this method maintains their natural orders to preserve data
intrinsic structures, e.g., image data kept as matrices. To implement
clustering, the multi... | computer science |
25,978 | A Unified Framework for Compositional Fitting of Active Appearance
Models | cs.CV | Active Appearance Models (AAMs) are one of the most popular and
well-established techniques for modeling deformable objects in computer vision.
In this paper, we study the problem of fitting AAMs using Compositional
Gradient Descent (CGD) algorithms. We present a unified and complete view of
these algorithms and classi... | computer science |
25,979 | Susceptibility of texture measures to noise: an application to lung
tumor CT images | cs.CV | Five different texture methods are used to investigate their susceptibility
to subtle noise occurring in lung tumor Computed Tomography (CT) images caused
by acquisition and reconstruction deficiencies. Noise of Gaussian and Rayleigh
distributions with varying mean and variance was encountered in the analyzed CT
images... | computer science |
25,980 | A fractal dimension based optimal wavelet packet analysis technique for
classification of meningioma brain tumours | cs.CV | With the heterogeneous nature of tissue texture, using a single resolution
approach for optimum classification might not suffice. In contrast, a
multiresolution wavelet packet analysis can decompose the input signal into a
set of frequency subbands giving the opportunity to characterise the texture at
the appropriate f... | computer science |
25,981 | Supervised Texture Segmentation: A Comparative Study | cs.CV | This paper aims to compare between four different types of feature extraction
approaches in terms of texture segmentation. The feature extraction methods
that were used for segmentation are Gabor filters (GF), Gaussian Markov random
fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM). It was
shown th... | computer science |
25,982 | Image Resolution Enhancement by Using Interpolation Followed by
Iterative Back Projection | cs.CV | In this paper, we propose a new super resolution technique based on the
interpolation followed by registering them using iterative back projection
(IBP). Low resolution images are being interpolated and then the interpolated
images are being registered in order to generate a sharper high resolution
image. The proposed ... | computer science |
25,983 | Automatic Detection and Decoding of Photogrammetric Coded Targets | cs.CV | Close-range Photogrammetry is widely used in many industries because of the
cost effectiveness and efficiency of the technique. In this research, we
introduce an automated coded target detection method which can be used to
enhance the efficiency of the Photogrammetry. | computer science |
25,984 | Multi-task CNN Model for Attribute Prediction | cs.CV | This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to ... | computer science |
25,985 | Kernel Sparse Subspace Clustering on Symmetric Positive Definite
Manifolds | cs.CV | Sparse subspace clustering (SSC), as one of the most successful subspace
clustering methods, has achieved notable clustering accuracy in computer vision
tasks. However, SSC applies only to vector data in Euclidean space. As such,
there is still no satisfactory approach to solve subspace clustering by ${\it
self-express... | computer science |
25,986 | Matrix Variate RBM and Its Applications | cs.CV | Restricted Boltzmann Machine (RBM) is an importan- t generative model
modeling vectorial data. While applying an RBM in practice to images, the data
have to be vec- torized. This results in high-dimensional data and valu- able
spatial information has got lost in vectorization. In this paper, a
Matrix-Variate Restricted... | computer science |
25,987 | Robust Method of Vote Aggregation and Proposition Verification for
Invariant Local Features | cs.CV | This paper presents a method for analysis of the vote space created from the
local features extraction process in a multi-detection system. The method is
opposed to the classic clustering approach and gives a high level of control
over the clusters composition for further verification steps. Proposed method
comprises o... | computer science |
25,988 | Gamifying Video Object Segmentation | cs.CV | Video object segmentation can be considered as one of the most challenging
computer vision problems. Indeed, so far, no existing solution is able to
effectively deal with the peculiarities of real-world videos, especially in
cases of articulated motion and object occlusions; limitations that appear more
evident when we... | computer science |
25,989 | Crater Detection via Convolutional Neural Networks | cs.CV | Craters are among the most studied geomorphic features in the Solar System
because they yield important information about the past and present geological
processes and provide information about the relative ages of observed geologic
formations. We present a method for automatic crater detection using advanced
machine l... | computer science |
25,990 | Space-Time Representation of People Based on 3D Skeletal Data: A Review | cs.CV | Spatiotemporal human representation based on 3D visual perception data is a
rapidly growing research area. Based on the information sources, these
representations can be broadly categorized into two groups based on RGB-D
information or 3D skeleton data. Recently, skeleton-based human representations
have been intensive... | computer science |
25,991 | Low-rank Matrix Factorization under General Mixture Noise Distributions | cs.CV | Many computer vision problems can be posed as learning a low-dimensional
subspace from high dimensional data. The low rank matrix factorization (LRMF)
represents a commonly utilized subspace learning strategy. Most of the current
LRMF techniques are constructed on the optimization problems using L1-norm and
L2-norm los... | computer science |
25,992 | Memory Matters: Convolutional Recurrent Neural Network for Scene Text
Recognition | cs.CV | Text recognition in natural scene is a challenging problem due to the many
factors affecting text appearance. In this paper, we presents a method that
directly transcribes scene text images to text without needing of sophisticated
character segmentation. We leverage recent advances of deep neural networks to
model the ... | computer science |
25,993 | Image-based Vehicle Analysis using Deep Neural Network: A Systematic
Study | cs.CV | We address the vehicle detection and classification problems using Deep
Neural Networks (DNNs) approaches. Here we answer to questions that are
specific to our application including how to utilize DNN for vehicle detection,
what features are useful for vehicle classification, and how to extend a model
trained on a limi... | computer science |
25,994 | Automatic 3D object detection of Proteins in Fluorescent labeled
microscope images with spatial statistical analysis | cs.CV | Since manual object detection is very inaccurate and time consuming, some
automatic object detection tools have been developed in recent years. At the
moment, there is no image analysis software available which provides an
automatic, objective assessment of 3D foci which is generally applicable.
Complications arise fro... | computer science |
25,995 | Quality Adaptive Low-Rank Based JPEG Decoding with Applications | cs.CV | Small compression noises, despite being transparent to human eyes, can
adversely affect the results of many image restoration processes, if left
unaccounted for. Especially, compression noises are highly detrimental to
inverse operators of high-boosting (sharpening) nature, such as deblurring and
superresolution agains... | computer science |
25,996 | Stochastic Dykstra Algorithms for Metric Learning on Positive
Semi-Definite Cone | cs.CV | Recently, covariance descriptors have received much attention as powerful
representations of set of points. In this research, we present a new metric
learning algorithm for covariance descriptors based on the Dykstra algorithm,
in which the current solution is projected onto a half-space at each iteration,
and runs at ... | computer science |
25,997 | Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal
Component Analysis | cs.CV | The probabilistic principal component analysis (PPCA) is built upon a global
linear mapping, with which it is insufficient to model complex data variation.
This paper proposes a mixture of bilateral-projection probabilistic principal
component analysis model (mixB2DPPCA) on 2D data. With multi-components in the
mixture... | computer science |
25,998 | Block-Diagonal Sparse Representation by Learning a Linear Combination
Dictionary for Recognition | cs.CV | In a sparse representation based recognition scheme, it is critical to learn
a desired dictionary, aiming both good representational power and
discriminative performance. In this paper, we propose a new dictionary learning
model for recognition applications, in which three strategies are adopted to
achieve these two ob... | computer science |
25,999 | On Some Properties of Calibrated Trifocal Tensors | cs.CV | In two-view geometry, the essential matrix describes the relative position
and orientation of two calibrated images. In three views, a similar role is
assigned to the calibrated trifocal tensor. It is a particular case of the
(uncalibrated) trifocal tensor and thus it inherits all its properties but, due
to the smaller... | computer science |
26,000 | Spontaneous Facial Micro-Expression Recognition using Discriminative
Spatiotemporal Local Binary Pattern with an Improved Integral Projection | cs.CV | Recently, there are increasing interests in inferring mirco-expression from
facial image sequences. Due to subtle facial movement of micro-expressions,
feature extraction has become an important and critical issue for spontaneous
facial micro-expression recognition. Recent works usually used spatiotemporal
local binary... | computer science |
26,001 | Discriminatively Trained Latent Ordinal Model for Video Classification | cs.CV | We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The prop... | computer science |
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