Segmentation and Classification of Multimodal Imagery

Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming version of the above algorithm that requires a lower computational memory. The results illustrate that our proposed methodology compares favorably well, on a qualitative and quantitative level, in segmentation quality and computational efficiency with the latest state of the art techniques. We also developed a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also introduce a composite architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges, and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results

Experience Dimensions of Religious Festivals: Religion and Spirituality at Paryaya, Udupi, India

Festivals and events have been found to be an important motivation for travel, and are a significant component in a destination offering. All religious festivals, irrespective of religion and tradition, aim to develop spirituality. Experience is the core of festivals and events, and the experience is multifaceted. This research aims to analyse the impact of various experience dimensions of religious festivals on participants’ overall festival experience and behavioural intention at a biennial festival called ‘Paryaya’ held at Udupi, India, using the concept of the experience economy. The researchers have adopted a quantitative research approach for the study. The result obtained through Structural Equation Modelling reveal that education, esthetics, escapist, communitas, spirituality, and authenticity dimensions of experiences significantly contribute to tourists’ overall festival experience. The overall festival experience of travellers acts as a statistically significant predictor of their behavioural intention at Paryaya. Moreover, the research concludes that visitors to a religious festival can be considered spiritual tourists, and their experience closely reflects the characteristics of spiritual tourism

Overcoming the Challenges of COVID-19 by Hospitality Educational Administrators: A Grounded Theory Approach

The coronavirus pandemic has affected all walks of life across the globe. Higher education institutions confronted multiple challenges and disruptions in teaching and learning. However, the challenges hospitality education administrators need to resolve are distinct compared to other traditional higher education programs. This study aimed to understand the experiences and responses of hospitality educational administrators under crisis. The findings of the study are expected to assist hospitality education institutions to be prepared and respond better to any crisis in the future. To understand the challenges faced and strategies adopted by hospitality educational administrators, we interviewed 23 hospitality administrators across India. We have adopted a grounded theory approach to describe the challenges and strategies the hospitality educational administrators adopted. The analysis of data through the grounded theory approach yielded five main themes: antecedents that influenced the hospitality educational administrators’ response to the crisis, approaches toward strategies, strategies adopted to manage the crisis, perspectives of the consequences of strategies adopted, and intervening conditions that influenced the administrators’ choice of strategies. The result indicates that hospitality educational administrators need to be proactive. They have to create a crisis management system, adopt technology in teaching and learning, and engage with all stakeholders to manage the crisis. This study has multiple implications for hospitality educational administrators, policymakers, and researchers in educational administration

The Choice Between EBooks and Printed Books: A Study Among Hospitality and Tourism Educators and Learners

The emergence of electronic books (ebook) changed the position of printed books in the learning space. This study examines the perceptions and preferences of hospitality and tourism educators and learners. Similarities and dissimilarities in the preference, perception and its link with gender, and scope of the degree course what the respondents teach and learn are analyzed. Data was collected from both students and teachers of hospitality, tourism and culinary degree program at a private university located in Karnataka, India. A structured questionnaire was used to collect data from the respondents. The result of the study shows that printed books are preferred mostly for their easy usage and reading. Both student and teachers of hospitality and tourism feel pleasure in reading printed books. However, both hospitality and tourism educators have more intention to use e-books in the future. The findings obtained will help the educational service providers, publishers and librarians in their decisions related to investment and management of library resource very effectively which facilitate teaching and learning process