Department Publications

As Virtual Reality (VR) technology continues to advance and widespread through various industries, the potential of immersive technologies in the banking sector has also begun to be acknowledged more commonly. VR banking, a 3D bank environment developed in an immersive world to provide customers with bank services, has the potential to substantially enhance the customer experience. However, a clear insight into an end-to-end pipeline for developing a banking system in VR is yet to be presented. This paper presents a novel VR banking system that facilitates users with personalized full-body avatars for representation within a VR bank, a speech-to-text integration for providing user information for bank services, and real-time dual avatar interaction and communication with a bank employee. Through a subjective survey, we evaluate the performance of the proposed facilitates and the overall VR bank …

Maritime surveillance is vital to mitigate illegal activities such as drug smuggling, illegal fishing, and human trafficking. Vision-based maritime surveillance is challenging mainly due to visibility issues at night, which results in failures in re-identifying vessels and detecting suspicious activities. In this paper, we introduce a thermal, vision-based approach for maritime surveillance with object tracking, vessel re-identification, and suspicious activity detection capabilities. For vessel re-identification, we propose a novel viewpoint-independent algorithm which compares features of the sides of the vessel separately (separate side-spaces) leveraging shape information in the absence of color features. We propose techniques to adapt tracking and activity detection algorithms for the thermal domain and train them using a thermal dataset we created. This dataset will be the first publicly available benchmark dataset for thermal maritime surveillance. Our system is capable of re-identifying vessels with an 81.8% Top1 score and identifying suspicious activities with a 72.4\% frame mAP score; a new benchmark for each task in the thermal domain.

3D reconstruction of the liver for volumetry is important for qualitative analysis and disease diagnosis. Liver volumetry using Ultrasound (US) scans, although advantageous due to less acquisition time and safety, is challenging due to the inherent noisiness in US scans, blurry boundaries, and partial liver visibility. We address these challenges by using the segmentation masks of a few incomplete sagittal-plane US scans of the liver in conjunction with a Statistical Shape Model (SSM) built using a set of CT scans of the liver. We compute the shape parameters needed to warp this canonical SSM to fit the US scans through a parametric regression network. The resulting 3D liver reconstruction is accurate and leads to automatic liver volume calculation. We evaluate the accuracy of the estimated liver volumes with respect to CT segmentation volumes using RMSE. Our volume computation is statistically much closer to the …

Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labeled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach …

Denoising of four-dimensional (4-D) light fields (LFs) is a fundamental preprocessing step before employing LFs for downstream tasks. Most of LF denoising methods have higher computational complexities despite excellent performance. Denoising of LFs using 4-D linear filters is a low-complexity approach that can achieve near real-time or real-time processing. We propose an ultra low-complexity LF denoising methods using 4-D infinite-extent impulse response (IIR) hyperfan filters in this paper. Our approach exploits the sparsity of an LF in the 4-D frequency domain, and employ a set of one-dimensional and two-dimensional first-order IIR filters to approximate the 4-D hyperfan-shaped passband. On average, the proposed 4-D IIR hyperfan filter needs only 2.56 multiplications and 21.06 additions to process a LF sample leading to more than 92% and 64% savings in the number of multiplications and additions to …