Mohammed Faisal | Searcharxiv

arXiv2023

Exploring User Perceptions of Virtual Reality Scene Design in Metaverse Learning Environments

Metaverse learning environments allow for a seamless and intuitive transition between activities compared to Virtual Reality (VR) learning environments, due to their interconnected design. The design of VR scenes is important for creating effective learning experiences in the Metaverse. However, there is limited research on the impact of different design elements on user's learning experiences in VR scenes. To address this, a study was conducted with 16 participants who interacted with two VR scenes, each with varying design elements such as style, color, texture, object, and background, while watching a short tutorial. Participant rankings of the scenes for learning were obtained using a seven-point Likert scale, and the Mann-Whitney U test was used to validate differences in preference between the scenes. The results showed a significant difference in preference between the scenes. Further analysis using the NASA TLX questionnaire was conducted to examine the impact of this difference on cognitive load, and participant feedback was also considered. The study emphasizes the importance of careful VR scene design to improve the user's learning experience.

cs.HC↗

arXiv2022

Texture Extraction Methods Based Ensembling Framework for Improved Classification

Texture-based classification solutions have proven their significance in many domains, from industrial inspections to health-related applications. New methods have been developed based on texture feature learning and CNN-based architectures to address computer vision use cases for images with rich texture-based features. In recent years, architectures solving texture-based classification problems and demonstrating state-of-the-art results have emerged. Yet, one limitation of these approaches is that they cannot claim to be suitable for all types of image texture patterns. Each technique has an advantage for a specific texture type only. To address this shortcoming, we propose a framework that combines more than one texture-based techniques together, uniquely, with a CNN backbone to extract the most relevant texture features. This enables the model to be trained in a self-selective manner and produce improved results over current published benchmarks -- with almost same number of model parameters. Our proposed framework works well on most texture types simultaneously and allows flexibility for additional texture-based methods to be accommodated to achieve better results than existing architectures. In this work, firstly, we present an analysis on the relative importance of existing techniques when used alone and in combination with other TE methods on benchmark datasets. Secondly, we show that Global Average Pooling which represents the spatial information -- is of less significance in comparison to the TE method(s) applied in the network while training for texture-based classification tasks. Finally, we present state-of-the-art results for several texture-based benchmark datasets by combining three existing texture-based techniques using our proposed framework.

cs.CV↗