2,364 research outputs found
Kernel Cuts: MRF meets Kernel & Spectral Clustering
We propose a new segmentation model combining common regularization energies,
e.g. Markov Random Field (MRF) potentials, and standard pairwise clustering
criteria like Normalized Cut (NC), average association (AA), etc. These
clustering and regularization models are widely used in machine learning and
computer vision, but they were not combined before due to significant
differences in the corresponding optimization, e.g. spectral relaxation and
combinatorial max-flow techniques. On the one hand, we show that many common
applications using MRF segmentation energies can benefit from a high-order NC
term, e.g. enforcing balanced clustering of arbitrary high-dimensional image
features combining color, texture, location, depth, motion, etc. On the other
hand, standard clustering applications can benefit from an inclusion of common
pairwise or higher-order MRF constraints, e.g. edge alignment, bin-consistency,
label cost, etc. To address joint energies like NC+MRF, we propose efficient
Kernel Cut algorithms based on bound optimization. While focusing on graph cut
and move-making techniques, our new unary (linear) kernel and spectral bound
formulations for common pairwise clustering criteria allow to integrate them
with any regularization functionals with existing discrete or continuous
solvers.Comment: The main ideas of this work are published in our conference papers:
"Normalized cut meets MRF" [70] (ECCV 2016) and "Secrets of Grabcut and
kernel K-means" [41] (ICCV 2015
Hierarchical Piecewise-Constant Super-regions
Recent applications in computer vision have come to heavily rely on
superpixel over-segmentation as a pre-processing step for higher level vision
tasks, such as object recognition, image labelling or image segmentation. Here
we present a new superpixel algorithm called Hierarchical Piecewise-Constant
Super-regions (HPCS), which not only obtains superpixels comparable to the
state-of-the-art, but can also be applied hierarchically to form what we call
n-th order super-regions. In essence, a Markov Random Field (MRF)-based
anisotropic denoising formulation over the quantized feature space is adopted
to form piecewise-constant image regions, which are then combined with a
graph-based split & merge post-processing step to form superpixels. The graph
and quantized feature based formulation of the problem allows us to generalize
it hierarchically to preserve boundary adherence with fewer superpixels.
Experimental results show that, despite the simplicity of our framework, it is
able to provide high quality superpixels, and to hierarchically apply them to
form layers of over-segmentation, each with a decreasing number of superpixels,
while maintaining the same desired properties (such as adherence to strong
image edges). The algorithm is also memory efficient and has a low
computational cost
A Simple Unsupervised Color Image Segmentation Method based on MRF-MAP
Color image segmentation is an important topic in the image processing field.
MRF-MAP is often adopted in the unsupervised segmentation methods, but their
performance are far behind recent interactive segmentation tools supervised by
user inputs. Furthermore, the existing related unsupervised methods also suffer
from the low efficiency, and high risk of being trapped in the local optima,
because MRF-MAP is currently solved by iterative frameworks with inaccurate
initial color distribution models. To address these problems, the letter
designs an efficient method to calculate the energy functions approximately in
the non-iteration style, and proposes a new binary segmentation algorithm based
on the slightly tuned Lanczos eigensolver. The experiments demonstrate that the
new algorithm achieves competitive performance compared with two state-of-art
segmentation methods.Comment: Submitted to IEEE SP
GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation
In this project, we first study the Gaussian-based hidden Markov random field
(HMRF) model and its expectation-maximization (EM) algorithm. Then we
generalize it to Gaussian mixture model-based hidden Markov random field. The
algorithm is implemented in MATLAB. We also apply this algorithm to color image
segmentation problems and 3D volume segmentation problems
A regularization-based approach for unsupervised image segmentation
We propose a novel unsupervised image segmentation algorithm, which aims to
segment an image into several coherent parts. It requires no user input, no
supervised learning phase and assumes an unknown number of segments. It
achieves this by first over-segmenting the image into several hundred
superpixels. These are iteratively joined on the basis of a discriminative
classifier trained on color and texture information obtained from each
superpixel. The output of the classifier is regularized by a Markov random
field that lends more influence to neighbouring superpixels that are more
similar. In each iteration, similar superpixels fall under the same label,
until only a few coherent regions remain in the image. The algorithm was tested
on a standard evaluation data set, where it performs on par with
state-of-the-art algorithms in term of precision and greatly outperforms the
state of the art by reducing the oversegmentation of the object of interest
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
Find my mug: Efficient object search with a mobile robot using semantic segmentation
In this paper, we propose an efficient semantic segmentation framework for
indoor scenes, tailored to the application on a mobile robot. Semantic
segmentation can help robots to gain a reasonable understanding of their
environment, but to reach this goal, the algorithms not only need to be
accurate, but also fast and robust. Therefore, we developed an optimized 3D
point cloud processing framework based on a Randomized Decision Forest,
achieving competitive results at sufficiently high frame rates. We evaluate the
capabilities of our method on the popular NYU depth dataset and our own data
and demonstrate its feasibility by deploying it on a mobile service robot, for
which we could optimize an object search procedure using our results.Comment: Part of the OAGM 2014 proceedings (arXiv:1404.3538
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation
Image segmentation is the process of partitioning the image into significant
regions easier to analyze. Nowadays, segmentation has become a necessity in
many practical medical imaging methods as locating tumors and diseases. Hidden
Markov Random Field model is one of several techniques used in image
segmentation. It provides an elegant way to model the segmentation process.
This modeling leads to the minimization of an objective function. Conjugate
Gradient algorithm (CG) is one of the best known optimization techniques. This
paper proposes the use of the Conjugate Gradient algorithm (CG) for image
segmentation, based on the Hidden Markov Random Field. Since derivatives are
not available for this expression, finite differences are used in the CG
algorithm to approximate the first derivative. The approach is evaluated using
a number of publicly available images, where ground truth is known. The Dice
Coefficient is used as an objective criterion to measure the quality of
segmentation. The results show that the proposed CG approach compares favorably
with other variants of Hidden Markov Random Field segmentation algorithms
Fast image-based obstacle detection from unmanned surface vehicles
Obstacle detection plays an important role in unmanned surface vehicles
(USV). The USVs operate in highly diverse environments in which an obstacle may
be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline,
which presents a significant challenge to continuous detection from images
taken onboard. This paper addresses the problem of online detection by
constrained unsupervised segmentation. To this end, a new graphical model is
proposed that affords a fast and continuous obstacle image-map estimation from
a single video stream captured onboard a USV. The model accounts for the
semantic structure of marine environment as observed from USV by imposing weak
structural constraints. A Markov random field framework is adopted and a highly
efficient algorithm for simultaneous optimization of model parameters and
segmentation mask estimation is derived. Our approach does not require
computationally intensive extraction of texture features and comfortably runs
in real-time. The algorithm is tested on a new, challenging, dataset for
segmentation and obstacle detection in marine environments, which is the
largest annotated dataset of its kind. Results on this dataset show that our
model outperforms the related approaches, while requiring a fraction of
computational effort.Comment: This is an extended version of the ACCV2014 paper [Kristan et al.,
2014] submitted to a journal. [Kristan et al., 2014] M. Kristan, J. Pers, V.
Sulic, S. Kovacic, A graphical model for rapid obstacle image-map estimation
from unmanned surface vehicles, in Proc. Asian Conf. Computer Vision, 201
On Regularized Losses for Weakly-supervised CNN Segmentation
Minimization of regularized losses is a principled approach to weak
supervision well-established in deep learning, in general. However, it is
largely overlooked in semantic segmentation currently dominated by methods
mimicking full supervision via "fake" fully-labeled training masks (proposals)
generated from available partial input. To obtain such full masks the typical
methods explicitly use standard regularization techniques for "shallow"
segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such
standard regularizers directly into the loss functions over partial input. This
approach simplifies weakly-supervised training by avoiding extra MRF/CRF
inference steps or layers explicitly generating full masks, while improving
both the quality and efficiency of training. This paper proposes and
experimentally compares different losses integrating MRF/CRF regularization
terms. We juxtapose our regularized losses with earlier proposal-generation
methods using explicit regularization steps or layers. Our approach achieves
state-of-the-art accuracy in semantic segmentation with near full-supervision
quality
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