Design of Immersive Online Hotel Walkthrough System Using Image-Based (Concentric Mosaics) Rendering

Conventional hotel booking websites only represents their services in 2D photos to show their facilities. 2D photos are just static photos that cannot be move and rotate. Imagebased virtual walkthrough for the hospitality industry is a potential technology to attract more customers. In this project, a research will be carried out to create an Image-based rendering (IBR) virtual walkthrough and panoramic-based walkthrough by using only Macromedia Flash Professional 8, Photovista Panorama 3.0 and Reality Studio for the interaction of the images. The web-based of the image-based are using the Macromedia Dreamweaver Professional 8. The images will be displayed in Adobe Flash Player 8 or higher. In making image-based walkthrough, a concentric mosaic technique is used while image mosaicing technique is applied in panoramic-based walkthrough. A comparison of the both walkthrough is compared. The study is also focus on the comparison between number of pictures and smoothness of the walkthrough. There are advantages of using different techniques such as image-based walkthrough is a real time walkthrough since the user can walk around right, left, forward and backward whereas the panoramic-based cannot experience real time walkthrough because the user can only view 360 degrees from a fixed spot

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Detection and Mosaicing through Deep Learning Models for Low-Quality Retinal Images

Glaucoma is a severe eye disease that is asymptomatic in the initial stages and can lead to blindness, due to its degenerative characteristic. There isn’t any available cure for it, and it is the second most common cause of blindness in the world. Most of the people affected by it only discovers the disease when it is already too late. Regular visits to the ophthalmologist are the best way to prevent or contain it, with a precise diagnosis performed with professional equipment. From another perspective, for some individuals or populations, this task can be difficult to accomplish, due to several restrictions, such as low incoming resources, geographical adversities, and travelling restrictions (distance, lack of means of transportation, etc.). Also, logistically, due to its dimensions, relocating the professional equipment can be expensive, thus becoming not viable to bring them to remote areas. In the market, low-cost products like the D-Eye lens offer an alternative to meet this need. The D-Eye lens can be attached to a smartphone to capture fundus images, but it presents a major drawback in terms of lower-quality imaging when compared to professional equipment. This work presents and evaluates methods for eye reading with D-Eye recordings. This involves exposing the retina in two steps: object detection and summarization via object mosaicing. Deep learning methods, such as the YOLO family architecture, were used for retina registration as an object detector. The summarization methods presented and inferred in this work mosaiced the best retina images together to produce a more detailed resultant image. After selecting the best workflow from these methods, a final inference was performed and visually evaluated, the results were not rich enough to serve as a pre-screening medical assessment, determining that improvements in the actual algorithm and technology are needed to retrieve better imaging

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

ALMA Solar Ephemeris Generator

An online software tool for the easy preparation of ephemerides of the solar surface features is presented. It was developed as a helper tool for the preparation of observations of the Sun with the Atacama Large Millimeter/submillimeter Array (ALMA), but it can be used at other observatories as well. The tool features an easy to use point-and-click graphical user interface with the possibility to enter or adjust input parameters, while the result is a table of predicted positions in the celestial equatorial coordinate system, suitable for import into the ALMA Observing Tool software. The tool has been successfully used for the preparation and execution of solar observations with ALMA.Comment: Submitted to The Mining Geological Petroleum Engineering Bulletin (see https://www.scopus.com/sourceid/101730), 7 pages, 2 figure

Image and Video Analytics for Document Processing and Event Recognition

The proliferation of handheld devices with cameras is among many changes in the past several decades which affected the document image analysis community by providing a far less constrained document imaging experience compared to traditional non-portable flatbed scanners. Although these devices provide more flexibility in capturing, the users now have to consider numerous environmental challenges including 1) a limited field-of-view keeping users from acquiring a high-quality images of large sources in a single frame, 2) Light reflections on glossy surfaces that result in saturated regions, and 3) Crumpled or non-planar documents that cannot be captured effectively from a single pose. Another change is the application of deep neural networks such as the deep convolutional neural networks (CNNs) for text analysis which is showing unprecedented performance over the classical approaches. Beginning with the success in character recognition, CNNs have shown their strength in many tasks in document analysis as well as computer vision. Researchers have explored potential applicability of CNNs for tasks such as text detection and segmentation, and have been quite successful. These networks, trained to perform single tasks, have recently evolved to handle multiple tasks. This introduces several important challenges including imposing multiple tasks on single architecture network and integrating multiple architectures with different tasks. In this dissertation, we make contributions in both of these areas. First, we propose a novel Graphcut-based document image mosaicking method which seeks to overcome the known limitations of the previous approaches. Our method does not require any prior knowledge of the content of the document images, making it more widely applicable and robust. Information regarding the geometrical disposition between the overlapping images is exploited to minimize the errors at the boundary regions. We incorporate a sharpness measure which induces cut generation in a way that results in the mosaic including the sharpest pixels. Our method is shown to outperform previous methods, both quantitatively and qualitatively. Second, we address the problem of removing highlight regions caused by the light sources reflecting off glossy surfaces in indoor environments. We devise an efficient method to detect and remove the highlights from the target scene by jointly estimating separate homographies for the target scene and the highlights. Our method is based on the observation that when given two images captured at different viewpoints, the displacement of the target scene is different from that of the highlight regions. We show the effectiveness of our method in removing the highlight reflections by comparing it with the related state-of-the-art methods. Unlike the previous methods, our method has the ability to handle saturated and relatively large highlights which completely obscure the content underneath. Third, we address the problem of selecting instances of a planar object in a video or set of images based on an evaluation of its "frontalness". We introduce the idea of "evaluating the frontalness" by computing how close the object's surface normal aligns with the optical axis of a camera. The unique and novel aspect of our method is that unlike previous planar object pose estimation methods, our method does not require a frontal reference image. The intuition is that a true frontal image can be used to reproduce other non-frontal images by perspective projection, while the non-frontal images have limited ability to do so. We show comparing 'frontal' and 'non-frontal' can be extended to compare 'more frontal' and 'less frontal' images. Based on this observation, our method estimates the relative frontalness of an image by exploiting the objective space error. We also propose the use of a K-invariant space to evaluate the frontalness even when the camera intrinsic parameters are unknown (e.g., images/videos from the web). Our method improves the accuracy over a baseline method. Lastly, we address the problem of integrating multiple deep neural networks (specifically CNNs) with different architectures and different tasks into a unified framework. To demonstrate the end-to-end integration of networks with different tasks and different architecture, we select event recognition and object detection. One of the novel aspects of our approach is that this is the first attempt to exploit the power of deep convolutional neural networks to directly integrate relevant object information into a unified network to improve event recognition performance. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition with the rigid and non-rigid object detection

Panoramic 360◦ videos in virtual reality using two lenses and a mobile phone

Cameras generally have a 60◦ field of view of and can capture only a portion of their surroundings. Panoramic cameras are used to capture the entire 360◦ view known as panoramic images. Virtual reality makes use of these panoramic images to provide a more immersive experience compared to seeing images on a 2D screen. Most of the panoramic cameras are expensive. It is important for the camera to be affordable in order for virtual reality to become a part of daily life. This is a comprehensive document about the successful implementation of the cheapest 360◦ video camera, using multiple lenses on a mobile phone. With the advent of technology nearly everyone has a mobile phone. Equipping these mobile phones with the technology to capture panoramic images using multiple lenses will convert them into the most economical panoramic camera

Design of Immersive Online Hotel Walkthrough System Using Image-Based (Concentric Mosaics) Rendering

Conventional hotel booking websites only represents their services in 2D photos to show their facilities. 2D photos are just static photos that cannot be move and rotate. Imagebased virtual walkthrough for the hospitality industry is a potential technology to attract more customers. In this project, a research will be carried out to create an Image-based rendering (IBR) virtual walkthrough and panoramic-based walkthrough by using only Macromedia Flash Professional 8, Photovista Panorama 3.0 and Reality Studio for the interaction of the images. The web-based of the image-based are using the Macromedia Dreamweaver Professional 8. The images will be displayed in Adobe Flash Player 8 or higher. In making image-based walkthrough, a concentric mosaic technique is used while image mosaicing technique is applied in panoramic-based walkthrough. A comparison of the both walkthrough is compared. The study is also focus on the comparison between number of pictures and smoothness of the walkthrough. There are advantages of using different techniques such as image-based walkthrough is a real time walkthrough since the user can walk around right, left, forward and backward whereas the panoramic-based cannot experience real time walkthrough because the user can only view 360 degrees from a fixed spot