Seeing Tree Structure from Vibration

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://tree.csail.mit.edu

An Elastic Interaction-Based Loss Function for Medical Image Segmentation

Deep learning techniques have shown their success in medical image segmentation since they are easy to manipulate and robust to various types of datasets. The commonly used loss functions in the deep segmentation task are pixel-wise loss functions. This results in a bottleneck for these models to achieve high precision for complicated structures in biomedical images. For example, the predicted small blood vessels in retinal images are often disconnected or even missed under the supervision of the pixel-wise losses. This paper addresses this problem by introducing a long-range elastic interaction-based training strategy. In this strategy, convolutional neural network (CNN) learns the target region under the guidance of the elastic interaction energy between the boundary of the predicted region and that of the actual object. Under the supervision of the proposed loss, the boundary of the predicted region is attracted strongly by the object boundary and tends to stay connected. Experimental results show that our method is able to achieve considerable improvements compared to commonly used pixel-wise loss functions (cross entropy and dice Loss) and other recent loss functions on three retinal vessel segmentation datasets, DRIVE, STARE and CHASEDB1

The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on high-resolution fundus images is required

Modeling, simulation and forecasting of wind power plants using agent-based approach

© 2020 Elsevier Ltd National economy and growth rely heavily on electricity but rapid urbanization, expeditious industrialization and increased domestic use due to population growth are among the reasons for the severe energy crisis in developing countries. The extended demand-supply gaps, depleting reservoirs of fossil fuel, and the environmental hazards altogether ignite the need for wider adoption of renewable energy resources for electricity generation. A functional assessment of the engineering design for this transition is a prerequisite before proceeding to on-ground implementation due to its high impact on system sustainability. To this end, we propose an agent-based modeling and simulation framework for the rapid prototyping of wind power plants. The proposed approach abstracts active components of wind power plants using agents and implements their dynamic behavior through agent interactions. The proposed model helps in composing different model components, design valuation, and forecasting energy generation in a cost-effective and productive manner. The proposed model is demonstrated by conceptualizing the design of the Foundation Wind Energy plant, located at Sindh, Pakistan, and the development of its agent-based model. The obtained short-term and long-term electricity generation profiles are validated with the actual data. We further compared the forecasts with the time series analysis performed on the actual data, using five different time-series forecasting models. The proposed simulation model and time series analysis model fit well on the actual data with a root mean square deviation of approximately 9 MW. The proposed framework will assist the policymakers in estimating the extent of electrical energy produced at given conditions using the wind potential available at the corridors of any country. It will further aid in the realistic analysis of the future dynamics of electricity demand and supply, hence help in effective energy planning

Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies

A maximum entropy deep reinforcement learning neural tracker

Tracking of anatomical structures has multiple applications in the field of biomedical imaging, including screening, diagnosing and monitoring the evolution of pathologies. Semi-automated tracking of elongated structures has been previously formulated as a problem suitable for deep reinforcement learning (DRL), but it remains a challenge. We introduce a maximum entropy continuous-action DRL neural tracker capable of training from scratch in a complex environment in the presence of high noise levels, Gaussian blurring and detractors. The trained model is evaluated on two-photon microscopy images of mouse cortex. At the expense of slightly worse robustness compared to a previously applied DRL tracker, we reach significantly higher accuracy, approaching the performance of the standard hand-engineered algorithm used for neuron tracing. The higher sample efficiency of our maximum entropy DRL tracker indicates its potential of being applied directly to small biomedical datasets