A spatial statistic to determine appropriate field sampling size for linking image and field data

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

Protocol, image, and discourse in political leadership competition:the case of Prime Minister Lionel Jospin, 1997-2002

This article examines the relationship between Prime Minister Jospin and President Chirac in the period 1997 to 2002. It is concerned in particular with symbolism, discourse and protocol, and how these have mediated the political competition between Chirac and Jospin. We develop a framework of analysis with several main strands. We consider the effects of the institutions of the Fifth Republic upon the political conduct of Prime Minister and President. We observe the perceived character traits of the individuals concerned, as well as the character traits expected of the offices of President and Prime Minister. We investigate the influence of the past upon the behaviour of Chirac and Jospin in the present, both in terms of notions of regime crisis which configured the institutions in the first place, and in relation to the image of previous holders of the offices (especially Charles de Gaulle and Franois Mitterrand).

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Perceptions of family environment, body image and self-esteem of eating disordered and non-eating disordered women

This study explored the perceptions of family environment, body image and self esteem of women who suffer from anorexia nervosa, bulimia nervosa, and depression. Using a nonequivalent control group design, one hundred and fifty women with anorexia nervosa (n = 50), bulimia nervosa (n = 50), and depression (n = 50) were given the Family Environment Scale (FES) and the Eating Disorders Inventory-2 (EDI-2). The objectives of this study were to: (1) study how women with anorexia nervosa and bulimia nervosa perceive their family environment as measured by the FES; (2) compare and contrast perceptions of family environment of women with anorexia nervosa and bulimia nervosa with the control group; (3) compare and contrast perceived levels of self esteem and body image as measured by the EDI-2 of women with anorexia nervosa and bulimia nervosa with the control group; and (4) examine the perceived family environments of eating disordered and non-eating disordered women with regard to body image and self esteem. Results suggested, women who suffered from anorexia nervosa or bulimia nervosa scored significantly lower (p $<$.021) on the Expressiveness, Intellectual-Cultural Orientation, and Active-Recreational subscales of the FES. The results also indicated that women who suffered from bulimia nervosa scored significantly higher (p $<$.015) than women who suffered from anorexia nervosa on the Conflict and Independence subscales of the FES. The results of studying these three populations reflected that women who suffered from anorexia nervosa scored significantly different (p $<$.000) than women who suffered from bulimia nervosa on many of the subscales of the EDI-2. The findings of the study confirmed that women who suffered from anorexia nervosa or bulimia nervosa scored significantly different (p $<$.000) on the subscales of the EDI-2 compared to women who suffered from depression. It was also confirmed that a relationship does exist between perceptions of body image and self esteem and perceptions of family environment amongst women with anorexia nervosa and bulimia nervosa as compared to depressed women. The findings of the study indicated that women who suffered from anorexia nervosa tended to: be less expressive and independent; handle conflict less openly; have a greater drive for thinness; have greater body dissatisfaction; be more perfectionistic; and struggle more intensely with fears around maturity and social insecurity than did women who suffered from bulimia nervosa or depression. In addition, the findings of the study also suggested that women who suffered from bulimia nervosa tended to: be raised in homes where openly expressed anger is permitted amongst family members; have a lesser drive for thinness; have less body dissatisfaction; be less perfectionistic; and not struggle as intensely with fears around maturity and social insecurity as do women who suffered from anorexia nervosa, but more than women who suffer from depression. Treatment implications that may assist community college professors and counselors in meeting the special needs of this special group of women were also discussed. (Abstract shortened by UMI.) ^

Habitat map of Danajon Bank, Philippines, derived from a high-spatial-resolution multi-spectral satellite image and georeferenced point intercept transect and spot-check survey field data, using an object-based image classification method

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.

Circumarctic wetland dataset based on ENVISAT ASAR Global Monitoring Mode with links to geotiff image and NetCDF files (2005-01 to 2011-12)

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1-7%.

Contributions to HEVC Prediction for Medical Image Compression

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compression where either near-lossless or lossless coding is required. In this dissertation, two different approaches to improve lossless coding of volumetric medical images, such as Magnetic Resonance and Computed Tomography, were studied and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a first approach, the use of geometric transformations to perform inter-slice prediction was investigated. For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction, that exploits inter-slice redundancy was proposed to extend the current HEVC lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when compared with DICOM recommended encoders, and 13.7% when compared with standard HEVC.

A Framework to Capture and Share Knowledge Using Storytelling and Video Sharing in Global Product Development

In global engineering enterprises, information and knowledge sharing are critical factors that can determine a project’s success. This statement is widely acknowledged in published literature. However, according to some academics, tacit knowledge is derived from a person’s lifetime of experience, practice, perception and learning, which makes it hard to capture and document in order to be shared. This project investigates if social media tools can be used to improve and enable tacit knowledge sharing within a global engineering enterprise. This paper first provides a brief background of the subject area, followed by an explanation of the industrial investigation, from which the proposed knowledge framework to improve tacit knowledge sharing is presented. This project’s main focus is on the improvement of collaboration and knowledge sharing amongst product development engineers in order to improve the whole product development cycle.

A First Step Towards Synthesising Rubrics and Video for the Formative Assessment of Complex Skills

Abstract. The performance objectives used for the formative assessment of com- plex skills are generally set through text-based analytic rubrics[1]. Moreover, video modeling examples are a widely applied method of observational learning, providing students with context-rich modeling examples of complex skills that act as an analogy for problem solving [1]. The purpose of this theoretical paper is to synthesize the components of video modeling and rubrics to support the formative assessment of complex skills. Based on theory, we argue that application of the developed Video Enhanced Rubrics (VER) fosters learners’ development of mental models, quality of provided feedback by various actors and finally, the learners mastery of complex skills.

Robust indoor speaker recognition in a network of audio and video sensors

Situational awareness is achieved naturally by the human senses of sight and hearing in combination. Automatic scene understanding aims at replicating this human ability using microphones and cameras in cooperation. In this paper, audio and video signals are fused and integrated at different levels of semantic abstractions. We detect and track a speaker who is relatively unconstrained, i.e., free to move indoors within an area larger than the comparable reported work, which is usually limited to round table meetings. The system is relatively simple: consisting of just 4 microphone pairs and a single camera. Results show that the overall multimodal tracker is more reliable than single modality systems, tolerating large occlusions and cross-talk. System evaluation is performed on both single and multi-modality tracking. The performance improvement given by the audio–video integration and fusion is quantified in terms of tracking precision and accuracy as well as speaker diarisation error rate and precision–recall (recognition). Improvements vs. the closest works are evaluated: 56% sound source localisation computational cost over an audio only system, 8% speaker diarisation error rate over an audio only speaker recognition unit and 36% on the precision–recall metric over an audio–video dominant speaker recognition method.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

The impact of event sponsorship on children’s brand image and purchase intentions: The mediator effects of product involvement and brand familiarity

Field lab in marketing: Children consumer behaviour