Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low- frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.

A recent surge in the application of Virtual Reality in education has made VR Learning Environments (VRLEs) prevalent in fields ranging from aviation, medicine, and skill training to teaching factual and conceptual content. In spite of multiple 3D affordances provided by VR, learning content placement in VRLEs has been mostly limited to a static placement in the environment. We conduct two studies to investigate the effect of different spatial representations of learning content in virtual environments on learning outcomes and user experience. In the first study, we studied the effects of placing content at four different places - world-anchored (TV screen placed in the environment), user-anchored (panel anchored to the wrist or head-mounted display of the user) and object-anchored (panel anchored to the object associated with current content) - in the VR environment with forty-two participants in the context of learning how to operate a laser cutting machine through an immersive tutorial. In the follow-up study, twenty-two participants from this study were given the option to choose from these four placements to understand their preferences. The effects of placements were examined on learning outcome measures - knowledge gain, knowledge transfer, cognitive load, user experience, and user preferences. We found that participants preferred user-anchored (controller condition) and object-anchored placement. While knowledge gain, knowledge transfer, and cognitive load were not found to be significantly different between the four conditions, the object-anchored placement scored significantly better than the TV screen and head-mounted display conditions on the user experience scales of attractiveness, stimulation, and novelty.

Automatic recognition of text, such as a batch code printed on a box placed on a moving conveyor belt, is still a challenging problem. This paper proposes an end-to-end character recognition technique while addressing the major challenges encountered in a real environment, such as motion blur in the acquired images, slanted or oriented characters, creased batch codes due to wear and tear of boxes, variations in label formats, and variations in printing styles. The major contribution of this work lies in development of three sequential modules: text localization using Connectionist Text Proposal Network(CTPN), character detection and character recognition using a modified version of the capsule network (CapsNet). In contrast to CapsNet, where only a standard single convolution is used, the proposed method uses a series of feature blocks, making it a deep CapsNet which is later proven to generate more comprehensive and better separable feature vectors over its counterpart. The feature generation module is further enhanced by setting a smaller kernel size than CapsNet. The proposed system is validated on a real-world box / packet dataset generated in a retail manufacturing industry. The proposed recognition network architecture is also validated on a standard public dataset (ICDAR 2013). The comparative results are presented with statistical analysis in the experimental results section.