688 research outputs found
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
The performance of existing supervised neuron segmentation methods is highly
dependent on the number of accurate annotations, especially when applied to
large scale electron microscopy (EM) data. By extracting semantic information
from unlabeled data, self-supervised methods can improve the performance of
downstream tasks, among which the mask image model (MIM) has been widely used
due to its simplicity and effectiveness in recovering original information from
masked images. However, due to the high degree of structural locality in EM
images, as well as the existence of considerable noise, many voxels contain
little discriminative information, making MIM pretraining inefficient on the
neuron segmentation task. To overcome this challenge, we propose a
decision-based MIM that utilizes reinforcement learning (RL) to automatically
search for optimal image masking ratio and masking strategy. Due to the vast
exploration space, using single-agent RL for voxel prediction is impractical.
Therefore, we treat each input patch as an agent with a shared behavior policy,
allowing for multi-agent collaboration. Furthermore, this multi-agent model can
capture dependencies between voxels, which is beneficial for the downstream
segmentation task. Experiments conducted on representative EM datasets
demonstrate that our approach has a significant advantage over alternative
self-supervised methods on the task of neuron segmentation. Code is available
at \url{https://github.com/ydchen0806/dbMiM}.Comment: IJCAI 23 main track pape
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Interpretation of common-yet-challenging interaction scenarios can benefit
well-founded decisions for autonomous vehicles. Previous research achieved this
using their prior knowledge of specific scenarios with predefined models,
limiting their adaptive capabilities. This paper describes a Bayesian
nonparametric approach that leverages continuous (i.e., Gaussian processes) and
discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying
interaction patterns of the ego vehicle with other nearby vehicles. Our model
relaxes dependency on the number of surrounding vehicles by developing an
acceleration-sensitive velocity field based on Gaussian processes. The
experiment results demonstrate that the velocity field can represent the
spatial interactions between the ego vehicle and its surroundings. Then, a
discrete Bayesian nonparametric model, integrating Dirichlet processes and
hidden Markov models, is developed to learn the interaction patterns over the
temporal space by segmenting and clustering the sequential interaction data
into interpretable granular patterns automatically. We then evaluate our
approach in the highway lane-change scenarios using the highD dataset collected
from real-world settings. Results demonstrate that our proposed Bayesian
nonparametric approach provides an insight into the complicated lane-change
interactions of the ego vehicle with multiple surrounding traffic participants
based on the interpretable interaction patterns and their transition properties
in temporal relationships. Our proposed approach sheds light on efficiently
analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian
interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see
https://chengyuan-zhang.github.io/Multivehicle-Interaction
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the
time-frequency distribution of radio signals and analyzing their time-varying
patterns of communication states in radio monitoring and management. While it
excels when performing short-term signal analyses, it becomes inadaptable for
long-term signal analyses because it cannot adequately depict signal
time-varying patterns in a large time span on a space-limited screen. This
research thus presents an abstract signal time-frequency (ASTF) diagram to
address this problem. In the diagram design, a visual abstraction method is
proposed to visually encode signal communication state changes in time slices.
A time segmentation algorithm is proposed to divide a large time span into time
slices.Three new quantified metrics and a loss function are defined to ensure
the preservation of important time-varying information in the time
segmentation. An algorithm performance experiment and a user study are
conducted to evaluate the effectiveness of the diagram for long-term signal
analyses.Comment: 11 pages, 9 figure
A hybrid on-line ECG segmenting system for long-term monitoring
This paper introduces a new hybrid ECG beat segmenting system, which can be applied in the processing unit of single-channel, long-term ECG monitors for the on-line segmentation of the ECG signal. Numerous ECG segmentation techniques are already existing and applied, however sufficiently robust and reliable methods currently require more than one ECG signal channel and quite complex computations, which are practically not feasible in stand-alone, low-cost monitors. Our new system approach presents a time domain segmentation technique based on a priori physiological and morphological information of the ECG beat. The segmentation is carried out after classifying the ECG beat, using the linear approximation of the filtered ECG signal and considering the pathophysiological properties as well. The proposed algorithms require moderate computational power, allowing the practical realization in battery powered stand-alone long-term cardiac monitors or small-sized cardiac defibrillators. The prototype version of the system was implemented in Matlab. The test and evaluation of the system was carried out with the help of reference signal databases
Medical Image Segmentation with Deep Convolutional Neural Networks
Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published
Development of a New Local Adaptive Thresholding Method and Classification Algorithms for X-ray Machine Vision Inspection of Pecans
This study evaluated selected local adaptive thresholding methods for pecan defect segmentation and proposed a new method: Reverse Water Flow. Good pecan nuts and fabricated defective pecan nuts were used for comparison, in addition to images from published research articles. For detailed comparison, defective and good pecans, 100 each, were collect from a mechanical sorter operating at Pecan Research Farm, Oklahoma State University. To improve classification accuracy and reduce the decision time AdaBoost and support vector machine classifiers were applied and compared with Bayesian classifier. The data set was randomly divided into training and validation sets and 300 such runs were made. A new local adaptive thresholding method with a new hypothesis: reversing the water flow and a simpler thresholding criterion is proposed. The new hypothesis, reversing the simulated water flow, reduced the computational time by 40-60% as compared to the existing fastest Oh method. The proposed method could segment both larBiosystems and Agricultural Engineerin
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