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.

Imagery and strength of craving for eating, drinking and playing sport

The elaborated intrusion (EI) theory of desire (Kavanagh, Andrade, & May, 2005) attributes the motivational force of cravings to cognitive elaboration, including imagery, of apparently spontaneous thoughts that intrude into awareness. We report a questionnaire study in which respondents rated a craving for food or drink. Questionnaire items derived from EI theory formed a single factor alongside factors for anticipated reward/relief, resistance, and opportunity. In a multiple regression predicting strength of craving, the first three factors accounted for 36% of the variance. Opportunity did not enter the model. In a second study, the difference between individuals' strong and weak cravings to take part in a sporting activity was shown to be related to visual, auditory, and general imagery, and to anticipated reward or relief from engaging in the activity. Implications for treatment of craving-related disorders are discussed in the light of these results and of other research indicating that interference with imagery can reduce the strength of craving.

Eye-movements and visual imagery : a working memory approach to the treatment of post-traumatic stress disorder

It has been claimed that the symptoms of post-traumatic stress disorder (PTSD) can be ameliorated by eye-movement desensitization-reprocessing therapy (EMD-R), a procedure that involves the individual making saccadic eye-movements while imagining the traumatic event. We hypothesized that these eye-movements reduce the vividness of distressing images by disrupting the function of the visuospatial sketchpad (VSSP) of working memory, and that by doing so they reduce the intensity of the emotion associated with the image. This hypothesis was tested by asking non-PTSD participants to form images of neutral and negative pictures under dual task conditions. Their images were less vivid with concurrent eye-movements and with a concurrent spatial tapping task that did not involve eye-movements. In the first three experiments, these secondary tasks did not consistently affect participants' emotional responses to the images. However, Expt 4 used personal recollections as stimuli for the imagery task, and demonstrated a significant reduction in emotional response under the same dual task conditions. These results suggest that, if EMD-R works, it does so by reducing the vividness and emotiveness of traumatic images via the VSSP of working memory. Other visuospatial tasks may also be of therapeutic value.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Incorporating uncertainty in environmental models informed by imagery

In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Countering negative country of origin effects using imagery processing

A favorable product country of origin (e.g., an automobile made in Germany) is often considered an asset by marketers. Yet a challenge in today's competitive environment is how marketers of products from less favorably regarded countries can counter negative country of origin perceptions. Three studies investigate how mental imagery can be used to reduce the effects of negative country of origin stereotypes. Study 1 reveals that participants exposed to country of origin information exhibit automatic stereotype activation. Study 2 shows that self-focused counterstereotypical mental imagery (relative to other-focused mental imagery) significantly inhibits the automatic activation of negative country of origin stereotypes. Study 3 shows that this lessening of automatic negative associations persists when measured one day later. The results offer important implications for marketing theory and practice.