64,301 research outputs found
Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation
There is a growing interest in computer science, engineering, and mathematics
for modeling signals in terms of union of subspaces and manifolds. Subspace
segmentation and clustering of high dimensional data drawn from a union of
subspaces are especially important with many practical applications in computer
vision, image and signal processing, communications, and information theory.
This paper presents a clustering algorithm for high dimensional data that comes
from a union of lower dimensional subspaces of equal and known dimensions. Such
cases occur in many data clustering problems, such as motion segmentation and
face recognition. The algorithm is reliable in the presence of noise, and
applied to the Hopkins 155 Dataset, it generates the best results to date for
motion segmentation. The two motion, three motion, and overall segmentation
rates for the video sequences are 99.43%, 98.69%, and 99.24%, respectively
Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation with Efficient Image Annotation
Spannotation is an open source user-friendly tool developed for image
annotation for semantic segmentation specifically in autonomous navigation
tasks. This study provides an evaluation of Spannotation, demonstrating its
effectiveness in generating accurate segmentation masks for various
environments like agricultural crop rows, off-road terrains and urban roads.
Unlike other popular annotation tools that requires about 40 seconds to
annotate an image for semantic segmentation in a typical navigation task,
Spannotation achieves similar result in about 6.03 seconds. The tools utility
was validated through the utilization of its generated masks to train a U-Net
model which achieved a validation accuracy of 98.27% and mean Intersection Over
Union (mIOU) of 96.66%. The accessibility, simple annotation process and
no-cost features have all contributed to the adoption of Spannotation evident
from its download count of 2098 (as of February 25, 2024) since its launch.
Future enhancements of Spannotation aim to broaden its application to complex
navigation scenarios and incorporate additional automation functionalities.
Given its increasing popularity and promising potential, Spannotation stands as
a valuable resource in autonomous navigation and semantic segmentation. For
detailed information and access to Spannotation, readers are encouraged to
visit the project's GitHub repository at
https://github.com/sof-danny/spannotationComment: 8 pages, 6 figures, 1 table, 1 pseudo code (algorithm), 55 reference
Improved Approximation Algorithms for Segment Minimization in Intensity Modulated Radiation Therapy
he segment minimization problem consists of finding the smallest set of
integer matrices that sum to a given intensity matrix, such that each summand
has only one non-zero value, and the non-zeroes in each row are consecutive.
This has direct applications in intensity-modulated radiation therapy, an
effective form of cancer treatment. We develop three approximation algorithms
for matrices with arbitrarily many rows. Our first two algorithms improve the
approximation factor from the previous best of to (roughly) and , respectively, where is
the largest entry in the intensity matrix. We illustrate the limitations of the
specific approach used to obtain these two algorithms by proving a lower bound
of on the approximation
guarantee. Our third algorithm improves the approximation factor from to , where is (roughly) the largest
difference between consecutive elements of a row of the intensity matrix.
Finally, experimentation with these algorithms shows that they perform well
with respect to the optimum and outperform other approximation algorithms on
77% of the 122 test cases we consider, which include both real world and
synthetic data.Comment: 18 page
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
Learning Segmentation Masks with the Independence Prior
An instance with a bad mask might make a composite image that uses it look
fake. This encourages us to learn segmentation by generating realistic
composite images. To achieve this, we propose a novel framework that exploits a
new proposed prior called the independence prior based on Generative
Adversarial Networks (GANs). The generator produces an image with multiple
category-specific instance providers, a layout module and a composition module.
Firstly, each provider independently outputs a category-specific instance image
with a soft mask. Then the provided instances' poses are corrected by the
layout module. Lastly, the composition module combines these instances into a
final image. Training with adversarial loss and penalty for mask area, each
provider learns a mask that is as small as possible but enough to cover a
complete category-specific instance. Weakly supervised semantic segmentation
methods widely use grouping cues modeling the association between image parts,
which are either artificially designed or learned with costly segmentation
labels or only modeled on local pairs. Unlike them, our method automatically
models the dependence between any parts and learns instance segmentation. We
apply our framework in two cases: (1) Foreground segmentation on
category-specific images with box-level annotation. (2) Unsupervised learning
of instance appearances and masks with only one image of homogeneous object
cluster (HOC). We get appealing results in both tasks, which shows the
independence prior is useful for instance segmentation and it is possible to
unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
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