8,779 research outputs found
An Iterative Spanning Forest Framework for Superpixel Segmentation
Superpixel segmentation has become an important research problem in image
processing. In this paper, we propose an Iterative Spanning Forest (ISF)
framework, based on sequences of Image Foresting Transforms, where one can
choose i) a seed sampling strategy, ii) a connectivity function, iii) an
adjacency relation, and iv) a seed pixel recomputation procedure to generate
improved sets of connected superpixels (supervoxels in 3D) per iteration. The
superpixels in ISF structurally correspond to spanning trees rooted at those
seeds. We present five ISF methods to illustrate different choices of its
components. These methods are compared with approaches from the
state-of-the-art in effectiveness and efficiency. The experiments involve 2D
and 3D datasets with distinct characteristics, and a high level application,
named sky image segmentation. The theoretical properties of ISF are
demonstrated in the supplementary material and the results show that some of
its methods are competitive with or superior to the best baselines in
effectiveness and efficiency
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Image segmentation refers to the process to divide an image into
nonoverlapping meaningful regions according to human perception, which has
become a classic topic since the early ages of computer vision. A lot of
research has been conducted and has resulted in many applications. However,
while many segmentation algorithms exist, yet there are only a few sparse and
outdated summarizations available, an overview of the recent achievements and
issues is lacking. We aim to provide a comprehensive review of the recent
progress in this field. Covering 180 publications, we give an overview of broad
areas of segmentation topics including not only the classic bottom-up
approaches, but also the recent development in superpixel, interactive methods,
object proposals, semantic image parsing and image cosegmentation. In addition,
we also review the existing influential datasets and evaluation metrics.
Finally, we suggest some design flavors and research directions for future
research in image segmentation.Comment: submitted to Elsevier Journal of Visual Communications and Image
Representatio
Machine Learning Techniques and Applications For Ground-based Image Analysis
Ground-based whole sky cameras have opened up new opportunities for
monitoring the earth's atmosphere. These cameras are an important complement to
satellite images by providing geoscientists with cheaper, faster, and more
localized data. The images captured by whole sky imagers can have high spatial
and temporal resolution, which is an important pre-requisite for applications
such as solar energy modeling, cloud attenuation analysis, local weather
prediction, etc.
Extracting valuable information from the huge amount of image data by
detecting and analyzing the various entities in these images is challenging.
However, powerful machine learning techniques have become available to aid with
the image analysis. This article provides a detailed walk-through of recent
developments in these techniques and their applications in ground-based
imaging. We aim to bridge the gap between computer vision and remote sensing
with the help of illustrative examples. We demonstrate the advantages of using
machine learning techniques in ground-based image analysis via three primary
applications -- segmentation, classification, and denoising
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Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
This is the publisher’s final pdf. The published article is copyrighted by the Acoustical Society of America and can be found at: http://asadl.org/jasa/.Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
The exponentially increasing use of moving platforms for video capture
introduces the urgent need to develop the general background subtraction
algorithms with the capability to deal with the moving background. In this
paper, we propose a multilayer-based framework for online background
subtraction for videos captured by moving cameras. Unlike the previous
treatments of the problem, the proposed method is not restricted to binary
segmentation of background and foreground, but formulates it as a multi-label
segmentation problem by modeling multiple foreground objects in different
layers when they appear simultaneously in the scene. We assign an independent
processing layer to each foreground object, as well as the background, where
both motion and appearance models are estimated, and a probability map is
inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut
on Markov Random Field is employed to perform pixel-wise labeling. Extensive
evaluation results show that the proposed method outperforms state-of-the-art
methods on challenging video sequences.Comment: Accepted by ICCV'1
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
Most traditional video summarization methods are designed to generate
effective summaries for single-view videos, and thus they cannot fully exploit
the complicated intra and inter-view correlations in summarizing multi-view
videos in a camera network. In this paper, with the aim of summarizing
multi-view videos, we introduce a novel unsupervised framework via joint
embedding and sparse representative selection. The objective function is
two-fold. The first is to capture the multi-view correlations via an embedding,
which helps in extracting a diverse set of representatives. The second is to
use a `2;1- norm to model the sparsity while selecting representative shots for
the summary. We propose to jointly optimize both of the objectives, such that
embedding can not only characterize the correlations, but also indicate the
requirements of sparse representative selection. We present an efficient
alternating algorithm based on half-quadratic minimization to solve the
proposed non-smooth and non-convex objective with convergence analysis. A key
advantage of the proposed approach with respect to the state-of-the-art is that
it can summarize multi-view videos without assuming any prior
correspondences/alignment between them, e.g., uncalibrated camera networks.
Rigorous experiments on several multi-view datasets demonstrate that our
approach clearly outperforms the state-of-the-art methods.Comment: IEEE Trans. on Multimedia, 2017 (In Press
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
The throughput of electron microscopes has increased significantly in recent
years, enabling detailed analysis of cell morphology and ultrastructure.
Analysis of neural circuits at single-synapse resolution remains the flagship
target of this technique, but applications to cell and developmental biology
are also starting to emerge at scale. The amount of data acquired in such
studies makes manual instance segmentation, a fundamental step in many analysis
pipelines, impossible. While automatic segmentation approaches have improved
significantly thanks to the adoption of convolutional neural networks, their
accuracy still lags behind human annotations and requires additional manual
proof-reading. A major hindrance to further improvements is the limited field
of view of the segmentation networks preventing them from exploiting the
expected cell morphology or other prior biological knowledge which humans use
to inform their segmentation decisions. In this contribution, we show how such
domain-specific information can be leveraged by expressing it as long-range
interactions in a graph partitioning problem known as the lifted multicut
problem. Using this formulation, we demonstrate significant improvement in
segmentation accuracy for three challenging EM segmentation problems from
neuroscience and cell biology
Water Detection through Spatio-Temporal Invariant Descriptors
In this work, we aim to segment and detect water in videos. Water detection
is beneficial for appllications such as video search, outdoor surveillance, and
systems such as unmanned ground vehicles and unmanned aerial vehicles. The
specific problem, however, is less discussed compared to general texture
recognition. Here, we analyze several motion properties of water. First, we
describe a video pre-processing step, to increase invariance against water
reflections and water colours. Second, we investigate the temporal and spatial
properties of water and derive corresponding local descriptors. The descriptors
are used to locally classify the presence of water and a binary water detection
mask is generated through spatio-temporal Markov Random Field regularization of
the local classifications. Third, we introduce the Video Water Database,
containing several hours of water and non-water videos, to validate our
algorithm. Experimental evaluation on the Video Water Database and the DynTex
database indicates the effectiveness of the proposed algorithm, outperforming
multiple algorithms for dynamic texture recognition and material recognition by
ca. 5% and 15% respectively
Factor Analysis in Fault Diagnostics Using Random Forest
Factor analysis or sometimes referred to as variable analysis has been
extensively used in classification problems for identifying specific factors
that are significant to particular classes. This type of analysis has been
widely used in application such as customer segmentation, medical research,
network traffic, image, and video classification. Today, factor analysis is
prominently being used in fault diagnosis of machines to identify the
significant factors and to study the root cause of a specific machine fault.
The advantage of performing factor analysis in machine maintenance is to
perform prescriptive analysis (helps answer what actions to take?) and
preemptive analysis (helps answer how to eliminate the failure mode?). In this
paper, a real case of an industrial rotating machine was considered where
vibration and ambient temperature data was collected for monitoring the health
of the machine. Gaussian mixture model-based clustering was used to cluster the
data into significant groups, and spectrum analysis was used to diagnose each
cluster to a specific state of the machine. The significant features that
attribute to a particular mode of the machine were identified by using the
random forest classification model. The significant features for specific modes
of the machine were used to conclude that the clusters generated are distinct
and have a unique set of significant features
Algorithms for screening of Cervical Cancer: A chronological review
There are various algorithms and methodologies used for automated screening
of cervical cancer by segmenting and classifying cervical cancer cells into
different categories. This study presents a critical review of different
research papers published that integrated AI methods in screening cervical
cancer via different approaches analyzed in terms of typical metrics like
dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the
reader with an insight of Machine Learning algorithms like SVM (Support Vector
Machines), GLCM (Gray Level Co-occurrence Matrix), k-NN (k-Nearest Neighbours),
MARS (Multivariate Adaptive Regression Splines), CNNs (Convolutional Neural
Networks), spatial fuzzy clustering algorithms, PNNs (Probabilistic Neural
Networks), Genetic Algorithm, RFT (Random Forest Trees), C5.0, CART
(Classification and Regression Trees) and Hierarchical clustering algorithm for
feature extraction, cell segmentation and classification. This paper also
covers the publicly available datasets related to cervical cancer. It presents
a holistic review on the computational methods that have evolved over the
period of time, in chronological order in detection of malignant cells.Comment: This critical review of various machine learning algorithms for
Cervical Cancer Screening was completed at National Institute of
Biologicals(NIB), India by B.Tech final year Computer Science students at
JSSATE, Noida, India under the supervision of Director at NIB Dr. Surinder
Singh and Jr. Scientist Sh. P.S. Chandranan
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