12,931 research outputs found
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
Automatic Parameter Adaptation for Multi-object Tracking
Object tracking quality usually depends on video context (e.g. object
occlusion level, object density). In order to decrease this dependency, this
paper presents a learning approach to adapt the tracker parameters to the
context variations. In an offline phase, satisfactory tracking parameters are
learned for video context clusters. In the online control phase, once a context
change is detected, the tracking parameters are tuned using the learned values.
The experimental results show that the proposed approach outperforms the recent
trackers in state of the art. This paper brings two contributions: (1) a
classification method of video sequences to learn offline tracking parameters,
(2) a new method to tune online tracking parameters using tracking context.Comment: International Conference on Computer Vision Systems (ICVS) (2013
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip
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