Image-Based Vehicle Verification Using Steerable Gaussian Filter

This paper presents a new feature descriptor for a vehicle verification system. The Steerable Gaussian Filter (SGF) is utilized to generate an image feature descriptor. The descriptor is constructed by concatenating the statistical parameters of the SGF filtered output. The Maximum Likelihood Estimation (MLE) estimates the statistical estimator using a heavy-tailed and bell-shaped distribution assumption such as Gaussian, Laplace, or Generalized Gaussian Distribution (GGD). A classifier assigns a class label of the vehicle hypothesis based on an image descriptor. As documented in the experimental results, the proposed feature descriptor achieves a promising result, and it outperforms the state-of-theart vehicle verification systems, making it a very competitive candidate in the practical applications

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field