MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources.

Gaps between assistive technologies and dementia care

A growing number of people are now entering the elderly age category in Japan; this raises the likelihood of more persons with dementia, as the probability of becoming cognitively impaired increases with age. There is an increasing need for caregivers who are well trained and experienced and who can pay special attention to the needs of people with dementia. Technology can play an important role in helping such people and their caregivers. A lack of mutual understanding between caregivers and researchers regarding the appropriate uses of assistive technologies is another problem. We have described the relationship between information and communication technology (ICT), especially assistive technologies, and social issues as a first step towards developing a technology roadmap. © 2012 IEEE.

Biobjective optimisation of preliminary aircraft trajectories

The protection of the environment against pollutants produced by aviation is of great concern in the 21st century. Among the multiplicity of proposed solutions, modifying flight profiles for existing aircraft is a promising approach. The aim is to deliver and understand the trade-off between environmental impact and operating costs. This work will illustrate the optimisation process of aircraft trajectories by minimising fuel consumption and flight time for the climb phase of an aircraft that belongs to A320 category. To achieve this purpose a new variant of a multi-objective Tabu Search optimiser was evolved and integrated within a computational framework, called GATAC, that simulates flight profiles based on altitude and speed. © 2013 Springer-Verlag.

Bayesian updating of a unified soil compression model

Some amount of differential settlement occurs even in the most uniform soil deposit, but it is extremely difficult to estimate because of the natural heterogeneity of the soil. The compression response of the soil and its variability must be characterised in order to estimate the probability of the differential settlement exceeding a certain threshold value. The work presented in this paper introduces a probabilistic framework to address this issue in a rigorous manner, while preserving the format of a typical geotechnical settlement analysis. In order to avoid dealing with different approaches for each category of soil, a simplified unified compression model is used to characterise the nonlinear compression behavior of soils of varying gradation through a single constitutive law. The Bayesian updating rule is used to incorporate information from three different laboratory datasets in the computation of the statistics (estimates of the means and covariance matrix) of the compression model parameters, as well as of the uncertainty inherent in the model.

Multi scale shape index for 3D object recognition

We present Multi Scale Shape Index (MSSI), a novel feature for 3D object recognition. Inspired by the scale space filtering theory and Shape Index measure proposed by Koenderink & Van Doorn [6], this feature associates different forms of shape, such as umbilics, saddle regions, parabolic regions to a real valued index. This association is useful for representing an object based on its constituent shape forms. We derive closed form scale space equations which computes a characteristic scale at each 3D point in a point cloud without an explicit mesh structure. This characteristic scale is then used to estimate the Shape Index. We quantitatively evaluate the robustness and repeatability of the MSSI feature for varying object scales and changing point cloud density. We also quantify the performance of MSSI for object category recognition on a publicly available dataset. © 2013 Springer-Verlag.

Generating Options for Active Risk Control (GO-ARC): introducing a novel technique.

BACKGROUND: After investing significant amounts of time and money in conducting formal risk assessments, such as root cause analysis (RCA) or failure mode and effects analysis (FMEA), healthcare workers are left to their own devices in generating high-quality risk control options. They often experience difficulty in doing so, and tend toward an overreliance on administrative controls (the weakest category in the hierarchy of risk controls). This has important implications for patient safety and the cost effectiveness of risk management operations. This paper describes a before and after pilot study of the Generating Options for Active Risk Control (GO-ARC) technique, a novel tool to improve the quality of the risk control options generation process. OUTCOME MEASURES: The quantity, quality (using the three-tiered hierarchy of risk controls), variety, and novelty of risk controls generated. RESULTS: Use of the GO-ARC technique was associated with improvement on all measures. CONCLUSIONS: While this pilot study has some notable limitations, it appears that the GO-ARC technique improved the risk control options generation process. Further research is needed to confirm this finding. It is also important to note that improved risk control options are a necessary, but not sufficient, step toward the implementation of more robust risk controls.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

Sensing through body dynamics

It has been shown that sensory morphology and sensory-motor coordination enhance the capabilities of sensing in robotic systems. The tasks of categorization and category learning, for example, can be significantly simplified by exploiting the morphological constraints, sensory-motor couplings and the interaction with the environment. This paper argues that, in the context of sensory-motor control, it is essential to consider body dynamics derived from morphological properties and the interaction with the environment in order to gain additional insight into the underlying mechanisms of sensory-motor coordination, and more generally the nature of perception. A locomotion model of a four-legged robot is used for the case studies in both simulation and real world. The locomotion model demonstrates how attractor states derived from body dynamics influence the sensory information, which can then be used for the recognition of stable behavioral patterns and of physical properties in the environment. A comprehensive analysis of behavior and sensory information leads to a deeper understanding of the underlying mechanisms by which body dynamics can be exploited for category learning of autonomous robotic systems. © 2006 Elsevier Ltd. All rights reserved.