Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Optimising container process at multimodal container terminal with automatic straddle carriers

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Automatic Detection of Marine Mammal from Aerial Imagery

The following technical report describes the approach and algorithm used to detect marine mammals from aerial imagery taken from manned/unmanned platform. The aim is to automate the process of counting the population of dugongs and other mammals. We have developed and algorithm that automatically presents to a user a number of possible candidates of these mammals. We tested the algorithm in two distinct datasets taken from different altitudes. Analysis and discussion is presented in regards with the complexity of the input datasets, the detection performance.

LCADesign : an integrated approach to automatic eco-efficiency assessment of commercial buildings

Buildings consume resources and energy, contribute to pollution of our air, water and soil, impact the health and well-being of populations and constitute an important part of the built environment in which we live. The ability to assess their design with a view to reducing that impact automatically from their 3D CAD representations enables building design professionals to make informed decisions on the environmental impact of building structures. Contemporary 3D object-oriented CAD files contain a wealth of building information. LCADesign has been designed as a fully integrated approach for automated eco-efficiency assessment of commercial buildings direct from 3D CAD. LCADesign accesses the 3D CAD detail through Industry Foundation Classes (IFCs) - the international standard file format for defining architectural and constructional CAD graphic data as 3D real-world objects - to permit construction professionals to interrogate these intelligent drawing objects for analysis of the performance of a design. The automated take-off provides quantities of all building components whose specific production processes, logistics and raw material inputs, where necessary, are identified to calculate a complete list of quantities for all products such as concrete, steel, timber, plastic etc and combines this information with the life cycle inventory database, to estimate key internationally recognised environmental indicators such as CML, EPS and Eco-indicator 99. This paper outlines the key modules of LCADesign and their role in delivering an automated eco-efficiency assessment for commercial buildings.