Conor P. Mc Elhinney

LiDAR data management pipeline; from spatial database population to web-application visualization

While the existence of very large and scalable Database Management Systems (DBMSs) is well recognized, it is the usage and extension of these technologies to managing spatial data that has seen increasing amounts of research work in recent years. A focused area of this research work involves the handling of very high resolution Light Detection and Ranging (LiDAR) data. While LiDAR has many real world applications, it is usually the purview of organizations interested in capturing and monitoring our environment where it has become pervasive. In many of these cases, it has now become the de facto minimum standard expected when a need to acquire very detailed 3D spatial data is required. However, significant challenges exist when working with these data sources, from data storage to feature extraction through to data segmentation all presenting challenges relating to the very large volumes of data that exist. In this paper, we present the complete LiDAR data pipeline as managed in our spatial database framework. This involves three distinct sections, populating the database, building a spatial hierarchy that describes the available data sources, and spatially segmenting data based on user requirements which generates a visualization of these data in a WebGL enabled web-application viewer. All work presented is in an experimental results context where we show how this approach is runtime efficient given the very large volumes of LiDAR data that are being managed.

Independent component analysis applied to digital holograms of three-dimensional objects

We have successfully applied Independent Component Analysis to the removal of background speckle noise from digital holograms. Additive noise removal techniques do not perform well on speckle, which is better characterized as multiplicative noise. In addition, speckle contains 3D information and so cannot be removed completely. We use a blind source separation approach to the reduction of speckle noise in digital holograms. Independent Component Analysis computes a linear transformation of a multi-dimensional distribution that minimizes the statistical dependence between the components. It can be seen as an extension of principal component analysis where the transformed bases do not need to be orthonormal. Although a linear technique, we show how Independent Component Analysis can be applied to the reduction of background speckle in digital holograms. We have captured our digital holograms of three-dimensional objects using phase-shift digital interferometry. In addition, the technique can be extended and applied to segmentation and pattern recognition problems on digital holograms of three-dimensional objects. Results are provided using simulated and optical data.

Blockwise discrete Fourier transform analysis of digital hologram data of three-dimensional objects

We report on the results of a study into the characteristics of the blockwise discrete Fourier transform (DFT) coefficients of digital hologram data, with the aim of efficiently compressing the data. We captured digital holograms (whole Fresnel fields) of three-dimensional (3D) objects using phase-shift interferometry. The complex-valued fields were decomposed into nonoverlapping blocks of 8x8 pixels and transformed with the DFT. The inter-block distributions of the 64 Fourier coefficients were analyzed to determine the relative importance of each coefficient. Through techniques of selectively removing coefficients, or groups of coefficients, we were able to trace the relative importance of coefficients throughout a hologram, and over multiple holograms. We used rms error in the reconstructed image to quantify importance in the DFT domain. We have found that the positions of the most important coefficients are common throughout four of the five digital holograms in our test suite. These results will aid us in our aim of creating a general-purpose DFT quantization table that could be universally applied to digital hologram data of 3D objects as part of a JPEG-style compressor.

Superposition of digital holograms

In this paper we address the superposition of digital holograms from two independent perspectives. The first is concerned with the subject of superresolution, i.e. increasing the resolution of a digital holographic system beyond its limit. The limiting factor regarding resolution in a digital holographic system is the pixel size, which is equal to the smallest resolvable unit. By careful superposition of different digital holograms captured of the same three‐dimensional object, we attempt to increase the resolution of the reconstructed image and equivalently we attempt to increase the range of angles of reconstruction. We use the Wigner distribution function to qualify the method. The second form of digital hologram superposition is concerned with the construction of synthetic three‐dimensional scenes. By adding digital holograms of different objects, at the same or at different distances, we may create a synthetic three‐dimensional scene in which both objects are present. We may allow for the fact that o...

Holographic image processing of three-dimensional objects

We report on recent advances made in the area of holographic image processing of three-dimensional (3D) objects. In particular, we look at developments made in the areas of encryption, compression, noise removal, and 3D shape extraction. Results are provided using simulated objects and real-world 3D objects captured using phase- shift digital holography.