Engineering topics

Ghanem, Sally

Publications and source records attributed to Ghanem, Sally.

3 records

DOE OSTI2023

Latent code-based fusion: A Volterra neural network approach

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in lieu of generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a robust classification performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Scaling Subspace-Driven Approaches Using Information Fusion

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

Ghanem, Sally↗

DOE OSTI2022

Robust Wheel Detection for Vehicle Re-Identification

Vehicle re-identification is a demanding and challenging task in automated surveillance systems. The goal of vehicle re-identification is to associate images of the same vehicle to identify re-occurrences of the same vehicle. Robust re-identification of individual vehicles requires reliable and discriminative features extracted from specific parts of the vehicle. In this work, we construct an efficient and robust wheel detector that precisely locates and selects vehicular wheels from vehicle images. The associated hubcap geometry can hence be utilized to extract fundamental signatures from vehicle images and exploit them for vehicle re-identification. Wheels pattern information can yield additional information about vehicles in questions. To that end, we utilized a vehicle imagery dataset that has thousands of side-view vehicle collected under different illumination conditions and elevation angles. The collected dataset was used for training and testing the wheel detector. Experiments show that our approach could detect vehicular wheels accurately for 99.41% of the vehicles in the dataset.

47 OTHER INSTRUMENTATION↗