Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Learning object dynamics for smooth tracking of moving lip contours

A new technique is proposed for learning the dynamic characteristics of a deformable object, applied in particular to the problem of lip-tracking. Experimental results are given which demonstrate that the use of dynamic models allows the system to track more robustly under adverse conditions and to correct spurious, poorly tracked frames

Enlightening, balancing and transcending entrepreneurial orientation : the 4th annual EO3 research PDW

Entrepreneurial Orientation (EO) has a 30 year history as one of the most used concepts in entrepreneurship research. “Recent attention in formal sessions at the Academy of Management conference programs confirm Entrepreneurial Orientation as a primary construct with a majority of Entrepreneurship Division sponsored sessions devoted to studies using EO related measures”, as reported by the 2010 division program chair, Per Davidson (Roberts, 2010: 9). However, questions continue to be raised concerning over-dependence on parts of one strategic scale, possible inappropriate or under-theorized adaptations, and the lack of theoretical development on application and performance variance in emergent, organizational, and socioeconomic settings. One recent area of investigation in analysis, methods, theory and application concerns an “EO gestalt”, focusing on the family of EO-related measures and theory, rather than on one or more dimensions, in order to explore the theory and process of the Entrepreneurial Orientation phenomenon. The goals of the 4th Annual EO3 PDW are to enlighten researchers on the development of Entrepreneurial Orientation theory and related scales, balance the use of Entrepreneurial Orientation current knowledge with new research frontiers suggested by EO3 scholars’ questions, and transcend boundaries in the discoveries undertaken in the shared interdisciplinary and cross-cultural research agenda currently developing for Entrepreneurial Orientation concepts. Going into its forth year, the EO3 PDW has been pivotal in formalizing discussion, pushing research forward, and gaining insights from experienced and cutting edge scholars, as it provides a point of reference for coalescing research questions and findings surrounding this important concept.

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

On the behaviour of higher-order spectral features for object recognition in the presence of various types of noise

This paper presents results on the robustness of higher-order spectral features to Gaussian, Rayleigh, and uniform distributed noise. Based on cluster plots and accuracy results for various signal to noise conditions, the higher-order spectral features are shown to be better than moment invariant features.

Aerial object following using visual fuzzy servoing

This article presents a visual servoing system to follow a 3D moving object by a Micro Unmanned Aerial Vehicle (MUAV). The presented control strategy is based only on the visual information given by an adaptive tracking method based on the colour information. A visual fuzzy system has been developed for servoing the camera situated on a rotary wing MAUV, that also considers its own dynamics. This system is focused on continuously following of an aerial moving target object, maintaining it with a fixed safe distance and centred on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigation

Orientation passport : using gamification to engage university students

Adding game elements to an application to motivate use and enhance the user experience is a growing trend known as gamification. This study explores the use of game achievements when applied to a mobile application designed to help new students at university. This paper describes the foundations of a design framework used to integrate game elements to Orientation Passport, a personalised orientation event application for smart phones. Orientation Passport utilises game achievements to present orientation information in an engaging way and to encourage use of the application. The system is explained in terms of the design framework, and the findings of a pilot study involving 26 new students are presented. This study contributes the foundations of a design framework for general gamified achievement design. It also suggests that added game elements can be enjoyable but can potentially encourage undesirable use by some, and aren't as enjoyable if not enforced properly by the technology. Consideration is also needed when enforcing stricter game rules as usability can be affected.

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

Charting the system : the integrated master schedule as a multi-level and poly-temporal boundary object in complex projects

This paper investigates the use of visual artifacts to represent a complex adaptive system (CAS). The integrated master schedule (IMS) is one of those visuals widely used in complex projects for scheduling, budgeting, and project management. In this paper, we discuss how the IMS outperforms the traditional timelines and acts as a ‘multi-level and poly-temporal boundary object’ that visually represents the CAS. We report the findings of a case study project on the way the IMS mapped interactions, interdependencies, constraints and fractal patterns in a complex project. Finally, we discuss how the IMS was utilised as a complex boundary object by eliciting commitment and development of shared mental models, and facilitating negotiation through the layers of multiple interpretations from stakeholders.