Decentred optical axes and aberrations along principal visual field meridians

Suggestions that peripheral imagery may affect the development of refractive error have led to interest in the variation in refraction and aberration across the visual field. It is shown that, if the optical system of the eye is rotationally symmetric about an optical axis which does not coincide with the visual axis, measurements of refraction and aberration made along the horizontal and vertical meridians of the visual field will show asymmetry about the visual axis. The departures from symmetry are modelled for second-order aberrations, refractive components and third-order coma. These theoretical results are compared with practical measurements from the literature. The experimental data support the concept that departures from symmetry about the visual axis in the measurements of crossed-cylinder astigmatism J45 and J180 are largely explicable in terms of a decentred optical axis. Measurements of the mean sphere M suggest, however, that the retinal curvature must differ in the horizontal and vertical meridians.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

Automatic tracking, super-resolution and recognition of human faces from surveillance video

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

Omnidirectional visual odometry for a planetary rover

Position estimation for planetary rovers has been typically limited to odometry based on proprioceptive measurements such as the integration of distance traveled and measurement of heading change. Here we present and compare two methods of online visual odometry suited for planetary rovers. Both methods use omnidirectional imagery to estimate motion of the rover. One method is based on robust estimation of optical flow and subsequent integration of the flow. The second method is a full structure-from-motion solution. To make the comparison meaningful we use the same set of raw corresponding visual features for each method. The dataset is an sequence of 2000 images taken during a field experiment in the Atacama desert, for which high resolution GPS ground truth is available.

The Remnant

An Interactive Installation with holographic 3D projections, satellite imagery, surround sound and intuitive body driven interactivity. Remnant (v.1) was commissioned by the 2010 TreeLine ecoArt event - an initiative of the Sunshine Coast Council and presented at a remnant block of subtropical rainforest called ‘Mary Cairncross Scenic Reserve’ - located 100kms north of Brisbane near the township of Maleny. V2 was later commissioned for KickArts Gallery, Cairns, re-presenting the work in a new open format which allowed audiences to both experience the original power of the work and to also understand the construction of the work's powerful illusory, visual spaces. This art-science project focused upon the idea of remnant landscapes - isolated blocks of forest (or other vegetation types) typically set within a patchwork quilt of surrounding farmed land. Participants peer into a mysterious, long tunnel of imagery whilst navigating entirely through gentle head movements - allowing them to both 'steer' in three dimensions and also 'alight', as a butterfly might, upon a sector of landscape - which in turn reveals an underlying 'landscape of mind'. The work challenges audiences to re-imagine our conceptions of country in ways that will lead us to better reconnect and sustain today’s heavily divided landscapes. The research field involved developing new digital image projection methods, alternate embodied interaction and engagement strategies for eco-political media arts practice. The context was the creation of improved embodied and improvisational experiences for participants, further informed by ‘eco-philosophical’ and sustainment theories. By engaging with deep conceptions of connectivity between apparently disparate elements, the work considered novel strategies for fostering new desires, for understanding and re-thinking the requisite physical and ecological links between ‘things’ that have been historically shattered. The methodology was primarily practice-led and in concert with underlying theories. The work’s knowledge contribution was to question how new media interactive experience and embodied interaction might prompt participants to reflect upon appropriate resources and knowledges required to generate this substantive desire for new approaches to sustainment. This accentuated through the power of learning implied by the works' strongly visual and kinaesthetic interface (i.e. the tunnel of imagery and the head and torso operated navigation). The work was commissioned by the 2010 TreeLine ecoArt event - an initiative of the Sunshine Coast Council and the second version was commissioned by Kickarts Gallery, Cairns, specifically funded by a national optometrist chain. It was also funded in development by Arts Queensland and reviewed in Realtime.

Multi-modal object tracking using dynamic performance metrics

Intelligent surveillance systems typically use a single visual spectrum modality for their input. These systems work well in controlled conditions, but often fail when lighting is poor, or environmental effects such as shadows, dust or smoke are present. Thermal spectrum imagery is not as susceptible to environmental effects, however thermal imaging sensors are more sensitive to noise and they are only gray scale, making distinguishing between objects difficult. Several approaches to combining the visual and thermal modalities have been proposed, however they are limited by assuming that both modalities are perfuming equally well. When one modality fails, existing approaches are unable to detect the drop in performance and disregard the under performing modality. In this paper, a novel middle fusion approach for combining visual and thermal spectrum images for object tracking is proposed. Motion and object detection is performed on each modality and the object detection results for each modality are fused base on the current performance of each modality. Modality performance is determined by comparing the number of objects tracked by the system with the number detected by each mode, with a small allowance made for objects entering and exiting the scene. The tracking performance of the proposed fusion scheme is compared with performance of the visual and thermal modes individually, and a baseline middle fusion scheme. Improvement in tracking performance using the proposed fusion approach is demonstrated. The proposed approach is also shown to be able to detect the failure of an individual modality and disregard its results, ensuring performance is not degraded in such situations.

Visual arts, technology and education : how can teaching and learning in high school visual arts classrooms be enriched by the use of computer technology?

Whilst a variety of studies has appeared over the last decade addressing the gap between the potential promised by computers and the reality experienced in the classroom by teachers and students, few have specifically addressed the situation as it pertains to the visual arts classroom. The aim of this study was to explore the reality of the classroom use of computers for three visual arts highschool teachers and determine how computer technology might enrich visual arts teaching and learning. An action research approach was employed to enable the researcher to understand the situation from the teachers' points of view while contributing to their professional practice. The wider social context surrounding this study is characterised by an increase in visual communications brought about by rapid advances in computer technology. The powerful combination of visual imagery and computer technology is illustrated by continuing developments in the print, film and television industries. In particular, the recent growth of interactive multimedia epitomises this combination and is significant to this study as it represents a new form of publishing of great interest to educators and artists alike. In this social context, visual arts education has a significant role to play. By cultivating a critical awareness of the implications of technology use and promoting a creative approach to the application of computer technology within the visual arts, visual arts education is in a position to provide an essential service to students who will leave high school to participate in a visual information age as both consumers and producers.

Imagination

Imagination is a capacity of internal visualization, concept creation and manipulation not directly dependent upon sensation. Imagination is associated with a range of phenomena: mental imagery, fancy, inventiveness, insight, counterfactual reasoning, pretence, simulation and conceivability.

The impact of users’ cognitive style on their navigational behaviors in web searching

User-Web interactions have emerged as an important area of research in the field of information science. In this study, we investigate the effects of users’ cognitive styles on their Web navigational styles and information processing strategies. We report results from the analyses of 594 minutes recorded Web search sessions of 18 participants engaged in 54 scenario-based search tasks. We use questionnaires, cognitive style test, Web session logs and think-aloud as the data collection instruments. We classify users’ cognitive styles as verbalisers and imagers based on Riding’s (1991) Cognitive Style Analysis test. Two classifications of navigational styles and three categories of information processing strategies are identified. Our study findings show that there exist relationships between users’ cognitive style, and their navigational styles and information processing strategies. Verbal users seem to display sporadic navigational styles, and adopt a scanning strategy to understand the content of the search result page, while imagery users follow a structured navigational style and reading approach. We develop a matrix and a model that depicts the relationships between users’ cognitive styles, and their navigational style and information processing strategies. We discuss how the findings from this study could help search engine designers to provide an adaptive navigation support to users.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Boosting your product's country of origin

This article offers managerial implications for improving consumer perceptions of a product’s country of origin.