Skeleton matching with applications in severe weather detection

  title={Skeleton matching with applications in severe weather detection},
  author={Mohammad Mahdi Kamani and Farshid Farhat and Stephen Wistar and James Zijun Wang},
  journal={Appl. Soft Comput.},

Detecting Comma-Shaped Clouds for Severe Weather Forecasting Using Shape and Motion

This work proposes a machine learning and pattern recognition-based approach to detect “comma-shaped” clouds in satellite images, which are specific cloud distribution patterns strongly associated with cyclone formulation.

Polygonal Meshes of Highly Noisy Images based on a New Symmetric Thinning Algorithm with Theoretical Guarantees

A new symmetric thinning algorithms to extract from such highly noisy images 1-pixel wide skeletons with theoretical guarantees that establish the state-of-the-art in extracting optimal meshes fromhighly noisy images.

Targeted Meta-Learning for Critical Incident Detection in Weather Data

A novel framework named as targeted meta-learning is applied, which employs a small, well-crafted target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner and can overcome the bias issue, common to weather-related datasets, in a bow echo detection case study.

Enhanced Signal Recovery via Sparsity Inducing Image Priors

This work focuses on developing novel sparse signal representation algorithms to obtain more robust systems and on the design of tractable algorithms that can recover signals under aforementioned sparse models.

CAPTAIN: Comprehensive Composition Assistance for Photo Taking

A comprehensive fork-join framework, named CAPTAIN (Composition Assistance for Photo Taking), to guide a photographer with a variety of photography ideas, which consists of a few components: integrated object detection, photo genre classification, artistic pose clustering, and personalized aesthetics-aware image retrieval.


  • Hamed NikbakhtK. Papakonstantinou
  • Computer Science
    Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)
  • 2019
This work introduces a gradient-based Hamiltonian Markov Chain Monte Carlo framework, termed Approximate Sampling Target with Post-processing Adjustment (ASTPA), to construct a relevant target distribution by weighting the high-dimensional random variable space through a one-dimensional likelihood model, using the limit-state function.

Efficient fair principal component analysis

An adaptive first-order algorithm to learn a subspace that preserves fairness, while slightly compromising the reconstruction loss is proposed, which can be efficiently generalized to multiple group sensitive features and effectively reduce the unfairness decisions in downstream tasks such as classification.

Targeted Data-driven Regularization for Out-of-Distribution Generalization

The proposed framework, named as targeted data-driven regularization (TDR), is model- and dataset-agnostic, and employs a target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner.

An Artificial Intelligence Approach to Regulating Systemic Risk

It is found that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.

Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing

This paper proposes Pareto efficient Fairness (PEF) as a suitable fairness notion for supervised learning, that can ensure the optimal trade-off between overall loss and other fairness criteria, and empirically demonstrates the effectiveness of the PEF solution and the extracted Pare to frontier on real-world datasets.



Shape matching using skeleton context for automated bow echo detection

An automatic framework to detect bow echo patterns in radar images with high accuracy by introducing novel skeletonization and shape matching approaches is proposed.

Locating visual storm signatures from satellite images

An algorithm that analyzes satellite images from the vast historical archives to predict severe storms is proposed, which extracts and fits local cloud motions from image sequences to model the storm-related cloud patches.

Severe Thunderstorm Detection by Visual Learning Using Satellite Images

A computer algorithm is proposed that analyzes satellite images from historical archives to locate visual signatures of severe thunderstorms for short-term predictions and extracts and fits local cloud motion from image sequences to model the storm-related cloud patches.

Contrast Restoration of Weather Degraded Images

A physics-based model is presented that describes the appearances of scenes in uniform bad weather conditions and a fast algorithm to restore scene contrast, which is effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols.

The Bow Echo: Observations, Numerical Simulations, and Severe Weather Detection Methods

Abstract Bowing convective line segments (bow echoes) are often associated with swaths of damaging downburst winds and are sometimes accompanied by tornadoes that may reach violent (F4) intensity.

Path Similarity Skeleton Graph Matching

A novel graph matching algorithm is proposed and applies it to shape recognition based on object silhouettes by comparing the geodesic paths between skeleton endpoints by motivated by the fact that visually similar skeleton graphs may have completely different topological structures.

Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution

It is proven that the proposed approach never produces spurious branches, which are common when using the known skeleton pruning methods, and all skeleton points are centers of maximal disks.

Skeleton based shape matching and retrieval

The method encodes the geometric and topological information in the form of a skeletal graph and uses graph matching techniques to match the skeletons and to compare them and also describes a visualization tool to aid in the selection and specification of the matched objects.

Skeleton pruning as trade-off between skeleton simplicity and reconstruction error

This work proposes a simple algorithm to approximate the maximum of the Bayesian posterior probability which defines an order for iteratively removing the end branches to obtain the pruned skeleton and presents experimental results obtained without any parameter tuning.