CUDA based Level Set Method for 3D Reconstruction of Fishes from Large Acoustic Data

Acoustic images present views of underwater dynamics, even in high depths. With multi-beam echo sounders (SONARs), it is possible to capture series of 2D high resolution acoustic images. 3D reconstruction of the water column and subsequent estimation of fish abundance and fish species identification is highly desirable for planning sustainable fisheries. Main hurdles in analysing acoustic images are the presence of speckle noise and the vast amount of acoustic data. This paper presents a level set formulation for simultaneous fish reconstruction and noise suppression from raw acoustic images. Despite the presence of speckle noise blobs, actual fish intensity values can be distinguished by extremely high values, varying exponentially from the background. Edge detection generally gives excessive false edges that are not reliable. Our approach to reconstruction is based on level set evolution using Mumford-Shah segmentation functional that does not depend on edges in an image. We use the implicit function in conjunction with the image to robustly estimate a threshold for suppressing noise in the image by solving a second differential equation. We provide details of our estimation of suppressing threshold and show its convergence as the evolution proceeds. We also present a GPU based streaming computation of the method using NVIDIA’s CUDA framework to handle large volume data-sets. Our implementation is optimised for memory usage to handle large volumes

Polygon Feature Extraction from Satellite Imagery Based on Colour Image Segmentation and Medial Axis

Areal features are of great importance in applications like shore line mapping, boundary delineation and change detection. This research work is an attempt to automate the process of extracting feature boundaries from satellite imagery. This process is intended to eventually replace manual digitization by computer assisted boundary detection and conversion to a vector layer in a Geographic Information System. Another potential application is to be able to use the extracted linear features in image matching algorithms. In multi-spectral satellite imagery, various features can be distinguished based on their colour. There has been a good amount of work already done as far as boundary detection and skeletonization is concerned, but this research work is different from the previous ones in the way that it uses the Delaunay graph and the Voronoi tessellation to extract boundary and skeletons that are guaranteed to be topologically equivalent to the segmented objects. The features thus extracted as object border can be stored as vector maps in a Geographic Information System after labelling and editing. Here we present a complete methodology of the skeletonization process from satellite imagery using a colour image segmentation algorithm with examples of road networks and hydrographic networks.

An Analysis of Physiological and Psychological Responses in Virtual Reality and Flat Screen Gaming

Recent research has focused on the effectiveness of Virtual Reality (VR) in games as a more immersive method of interaction. However, there is a lack of robust analysis of the physiological effects between VR and flatscreen (FS) gaming. This paper introduces the first systematic comparison and analysis of emotional and physiological responses to commercially available games in VR and FS environments. To elicit these responses, we first selected four games through a pilot study of 6 participants to cover all four quadrants of the valence-arousal space. Using these games, we recorded the physiological activity, including Blood Volume Pulse and Electrodermal Activity, and self-reported emotions of 33 participants in a user study. Our data analysis revealed that VR gaming elicited more pronounced emotions, higher arousal, increased cognitive load and stress, and lower dominance than FS gaming. The Virtual Reality and Flat Screen (VRFS) dataset, containing over 15 hours of multimodal data comparing FS and VR gaming across different games, is also made publicly available for research purposes. Our analysis provides valuable insights for further investigations into the physiological and emotional effects of VR and FS gaming.Comment: This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Fast and robust construction of 3D architectural models from 2D plans

In this work we present a simple and robust method to create 3D building models from a set of architectural plans. Such plans are created for human readability and thus pose some problem in automatic creation of a 3D model. We suggest a semi-automated approach for plan cleaning and provide an algorithm for alignment and stacking of the plans followed by generation of 3D building model. We show results of our method on floor plans that generate complex 3D models in near real-time

Feature Extraction and Simplification from colour images based on Colour Image Segmentation and Skeletonization using the Quad-Edge data structure

Region features in colour images are of interest in applications such as mapping, climatology, change detection, medicine, etc. This research work is an attempt to automate the process of extracting feature boundaries from colour images. This process is an attempt to eventually replace manual digitization process by computer assisted boundary detection and conversion to a vector layer in a spatial database. In colour images, various features can be distinguished based on their colour. The features thus extracted as object border can be stored as vector maps in a spatial database after labelling and editing. Here, we present a complete methodology of the boundary extraction and skeletonization process from colour imagery using a colour image segmentation algorithm, a crust extraction algorithm and our new skeleton extraction algorithm. We also present a prototype application for completely automated or semi-automated processing of (satellite) imagery and scanned maps with an application to coastline extraction. Other applications include extraction of fields, clear cuts, clouds, as well as heating or pollution monitoring and dense forest mapping among others

Multi-domain, higher order level set scheme for 3D image segmentation on the GPU

Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only C(0) continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method to evaluate level set surfaces that are C(2) continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming solver is efficient in memory usage