13,492 research outputs found
Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images
Efficient and easy segmentation of images and volumes is of great practical
importance. Segmentation problems that motivate our approach originate from
microscopy imaging commonly used in materials science, medicine, and biology.
We formulate image segmentation as a probabilistic pixel classification
problem, and we apply segmentation as a step towards characterising image
content. Our method allows the user to define structures of interest by
interactively marking a subset of pixels. Thanks to the real-time feedback, the
user can place new markings strategically, depending on the current outcome.
The final pixel classification may be obtained from a very modest user input.
An important ingredient of our method is a graph that encodes image content.
This graph is built in an unsupervised manner during initialisation and is
based on clustering of image features. Since we combine a limited amount of
user-labelled data with the clustering information obtained from the unlabelled
parts of the image, our method fits in the general framework of semi-supervised
learning. We demonstrate how this can be a very efficient approach to
segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe
Underwater Fish Detection with Weak Multi-Domain Supervision
Given a sufficiently large training dataset, it is relatively easy to train a
modern convolution neural network (CNN) as a required image classifier.
However, for the task of fish classification and/or fish detection, if a CNN
was trained to detect or classify particular fish species in particular
background habitats, the same CNN exhibits much lower accuracy when applied to
new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN
needs to be continuously fine-tuned to improve its classification accuracy to
handle new project-specific fish species or habitats. In this work we present a
labelling-efficient method of training a CNN-based fish-detector (the Xception
CNN was used as the base) on relatively small numbers (4,000) of project-domain
underwater fish/no-fish images from 20 different habitats. Additionally, 17,000
of known negative (that is, missing fish) general-domain (VOC2012) above-water
images were used. Two publicly available fish-domain datasets supplied
additional 27,000 of above-water and underwater positive/fish images. By using
this multi-domain collection of images, the trained Xception-based binary
(fish/not-fish) classifier achieved 0.17% false-positives and 0.61%
false-negatives on the project's 20,000 negative and 16,000 positive holdout
test images, respectively. The area under the ROC curve (AUC) was 99.94%.Comment: Published in the 2019 International Joint Conference on Neural
Networks (IJCNN-2019), Budapest, Hungary, July 14-19, 2019,
https://www.ijcnn.org/ , https://ieeexplore.ieee.org/document/885190
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Segmentation of axon and myelin from microscopy images of the nervous system
provides useful quantitative information about the tissue microstructure, such
as axon density and myelin thickness. This could be used for instance to
document cell morphometry across species, or to validate novel non-invasive
quantitative magnetic resonance imaging techniques. Most currently-available
segmentation algorithms are based on standard image processing and usually
require multiple processing steps and/or parameter tuning by the user to adapt
to different modalities. Moreover, only few methods are publicly available. We
introduce AxonDeepSeg, an open-source software that performs axon and myelin
segmentation of microscopic images using deep learning. AxonDeepSeg features:
(i) a convolutional neural network architecture; (ii) an easy training
procedure to generate new models based on manually-labelled data and (iii) two
ready-to-use models trained from scanning electron microscopy (SEM) and
transmission electron microscopy (TEM). Results show high pixel-wise accuracy
across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and
84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed
and morphological metrics are extracted and compared against the literature.
AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure
Autonomous monitoring of cliff nesting seabirds using computer vision
In this paper we describe a proposed system for automatic visual monitoring of seabird populations. Image sequences of cliff face nesting sites are captured using time-lapse digital photography. We are developing image processing software which is designed to automatically interpret these images, determine the number of birds present, and monitor activity. We focus primarily on the the development of low-level image processing techniques to support this goal. We first describe our existing work in video processing, and show how it is suitable for this problem domain. Image samples from a particular nest site are presented, and used to describe the associated challenges. We conclude by showing how we intend to develop our work to construct a distributed system capable of simultaneously monitoring a number of sites in the same locality
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and
Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201
Whole-brain vasculature reconstruction at the single capillary level
The distinct organization of the brainâs vascular network ensures that it is adequately supplied with oxygen and nutrients. However, despite this fundamental role, a detailed reconstruction of the brain-wide vasculature at the capillary level remains elusive, due to insufficient image quality using the best available techniques. Here, we demonstrate a novel approach that improves vascular demarcation by combining CLARITY with a vascular staining approach that can fill the entire blood vessel lumen and imaging with light-sheet fluorescence microscopy. This method significantly improves image contrast, particularly in depth, thereby allowing reliable application of automatic segmentation algorithms, which play an increasingly important role in high-throughput imaging of the terabyte-sized datasets now routinely produced. Furthermore, our novel method is compatible with endogenous fluorescence, thus allowing simultaneous investigations of vasculature and genetically targeted neurons. We believe our new method will be valuable for future brain-wide investigations of the capillary network
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
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