Using Images to create a Hierarchical Grid Spatial Index

This paper presents a hybrid approach to spatial indexing of two dimensional data. It sheds new light on the age old problem by thinking of the traditional algorithms as working with images. Inspiration is drawn from an analogous situation that is found in machine and human vision. Image processing techniques are used to assist in the spatial indexing of the data. A fixed grid approach is used and bins with too many records are sub-divided hierarchically. Search queries are pre-computed for bins that do not contain any data records. This has the effect of dividing the search space up into non rectangular regions which are based on the spatial properties of the data. The bucketing quad tree can be considered as an image with a resolution of two by two for each layer. The results show that this method performs better than the quad tree if there are more divisions per layer. This confirms our suspicions that the algorithm works better if it gets to look at the data with higher resolution images. An elegant class structure is developed where the implementation of concrete spatial indexes for a particular data type merely relies on rendering the data onto an image.Comment: In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Taiwan, 2006, pp. 1974-197

Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Math Search for the Masses: Multimodal Search Interfaces and Appearance-Based Retrieval

We summarize math search engines and search interfaces produced by the Document and Pattern Recognition Lab in recent years, and in particular the min math search interface and the Tangent search engine. Source code for both systems are publicly available. "The Masses" refers to our emphasis on creating systems for mathematical non-experts, who may be looking to define unfamiliar notation, or browse documents based on the visual appearance of formulae rather than their mathematical semantics.Comment: Paper for Invited Talk at 2015 Conference on Intelligent Computer Mathematics (July, Washington DC

Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.Comment: CVPR Workshops Proceedings 202

RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding

We present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and Games (I3D '19