Images of desire : cognitive models of craving

Cognitive modelling of phenomena in clinical practice allows the operationalisation of otherwise diffuse descriptive terms such as craving or flashbacks. This supports the empirical investigation of the clinical phenomena and the development of targeted treatment interventions. This paper focuses on the cognitive processes underpinning craving, which is recognised as a motivating experience in substance dependence. We use a high-level cognitive architecture, Interacting Cognitive Subsystems (ICS), to compare two theories of craving: Tiffany's theory, centred on the control of automated action schemata, and our own Elaborated Intrusion theory of craving. Data from a questionnaire study of the subjective aspects of everyday desires experienced by a large non-clinical population are presented. Both the data and the high-level modelling support the central claim of the Elaborated Intrusion theory that imagery is a key element of craving, providing the subjective experience and mediating much of the associated disruption of concurrent cognition.

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.

Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.

Design of a Web interface for anatomical images

Interactive documents for use with the World Wide Web have been developed for viewing multi-dimensional radiographic and visual images of human anatomy, derived from the Visible Human Project. Emphasis has been placed on user-controlled features and selections. The purpose was to develop an interface which was independent of host operating system and browser software which would allow viewing of information by multiple users. The interfaces were implemented using HyperText Markup Language (HTML) forms, C programming language and Perl scripting language. Images were pre-processed using ANALYZE and stored on a Web server in CompuServe GIF format. Viewing options were included in the document design, such as interactive thresholding and two-dimensional slice direction. The interface is an example of what may be achieved using the World Wide Web. Key applications envisaged for such software include education, research and accessing of information through internal databases and simultaneous sharing of images by remote computers by health personnel for diagnostic purposes.

Unsupervised alignment of thousands of images

The task addressed in this thesis is the automatic alignment of an ensemble of misaligned images in an unsupervised manner. This application is especially useful in computer vision applications where annotations of the shape of an object of interest present in a collection of images is required. Performing this task manually is a slow, tedious, expensive and error prone process which hinders the progress of research laboratories and businesses. Most recently, the unsupervised removal of geometric variation present in a collection of images has been referred to as congealing based on the seminal work of Learned-Miller [21]. The only assumption made in congealing is that the parametric nature of the misalignment is known a priori (e.g. translation, similarity, a�ne, etc) and that the object of interest is guaranteed to be present in each image. The capability to congeal an ensemble of misaligned images stemming from the same object class has numerous applications in object recognition, detection and tracking. This thesis concerns itself with the construction of a congealing algorithm titled, least-squares congealing, which is inspired by the well known image to image alignment algorithm developed by Lucas and Kanade [24]. The algorithm is shown to have superior performance characteristics when compared to previously established methods: canonical congealing by Learned-Miller [21] and stochastic congealing by Z�ollei [39].

Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation

This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition.

Contrast enhancement of EPID images via difference imaging : a feasibility study

In this study, the feasibility of difference imaging for improving the contrast of electronic portal imaging device (EPID) images is investigated. The difference imaging technique consists of the acquisition of two EPID images (with and without the placement of an additional layer of attenuating medium on the surface of the EPID)and the subtraction of one of these images from the other. The resulting difference image shows improved contrast, compared to a standard EPID image, since it is generated by lower-energy photons. Results of this study show that, ¯rstly, this method can produce images exhibiting greater contrast than is seen in standard megavoltage EPID images and that, secondly, the optimal thickness of attenuating material for producing a maximum contrast enhancement may vary with phantom thickness and composition. Further studies of the possibilities and limitations of the di®erence imaging technique, and the physics behind it, are therefore recommended.

Digital processing of diffusion-tensor images of avascular tissues

Diffusion is the process that leads to the mixing of substances as a result of spontaneous and random thermal motion of individual atoms and molecules. It was first detected by the English botanist Robert Brown in 1827, and the phenomenon became known as ‘Brownian motion’. More specifically, the motion observed by Brown was translational diffusion – thermal motion resulting in random variations of the position of a molecule. This type of motion was given a correct theoretical interpretation in 1905 by Albert Einstein, who derived the relationship between temperature, the viscosity of the medium, the size of the diffusing molecule, and its diffusion coefficient. It is translational diffusion that is indirectly observed in MR diffusion-tensor imaging (DTI). The relationship obtained by Einstein provides the physical basis for using translational diffusion to probe the microscopic environment surrounding the molecule.

Toward a more robust facial expression recognition in occluded images using randomly sampled Gabor based templates

Occlusion is a big challenge for facial expression recognition (FER) in real-world situations. Previous FER efforts to address occlusion suffer from loss of appearance features and are largely limited to a few occlusion types and single testing strategy. This paper presents a robust approach for FER in occluded images and addresses these issues. A set of Gabor based templates is extracted from images in the gallery using a Monte Carlo algorithm. These templates are converted into distance features using template matching. The resulting feature vectors are robust to occlusion. Occluded eyes and mouth regions and randomly places occlusion patches are used for testing. Two testing strategies analyze the effects of these occlusions on the overall recognition performance as well as each facial expression. Experimental results on the Cohn-Kanade database confirm the high robustness of our approach and provide useful insights about the effects of occlusion on FER. Performance is also compared with previous approaches.

Unpacking Japan's 21st century "National Conversation" : images of the future beyond the iron cage of the "Catch Up" model

How does the image of the future operate upon history, and upon national and individual identities? To what extent are possible futures colonized by the image? What are the un-said futurecratic discourses that underlie the image of the future? Such questions inspired the examination of Japan’s futures images in this thesis. The theoretical point of departure for this examination is Polak’s (1973) seminal research into the theory of the ‘image of the future’ and seven contemporary Japanese texts which offer various alternative images for Japan’s futures, selected as representative of a ‘national conversation’ about the futures of that nation. These seven images of the future are: 1. Report of the Prime Minister’s Commission on Japan’s Goals in the 21st Century—The Frontier Within: Individual Empowerment and Better Governance in the New Millennium, compiled by a committee headed by Japan’s preeminent Jungian psychologist Kawai Hayao (1928-2007); 2. Slow Is Beautiful—a publication by Tsuji Shinichi, in which he re-images Japan as a culture represented by the metaphor of the sloth, concerned with slow and quality-oriented livingry as a preferred image of the future to Japan’s current post-bubble cult of speed and economic efficiency; 3. MuRatopia is an image of the future in the form of a microcosmic prototype community and on-going project based on the historically significant island of Awaji, and established by Japanese economist and futures thinker Yamaguchi Kaoru; 4. F.U.C.K, I Love Japan, by author Tanja Yujiro provides this seven text image of the future line-up with a youth oriented sub-culture perspective on that nation’s futures; 5. IMAGINATION / CREATION—a compilation of round table discussions about Japan’s futures seen from the point of view of Japan’s creative vanguard; 6. Visionary People in a Visionless Country: 21 Earth Connecting Human Stories is a collection of twenty one essays compiled by Denmark born Tokyo resident Peter David Pedersen; and, 7. EXODUS to the Land of Hope, authored by Murakami Ryu, one of Japan’s most prolific and influential writers, this novel suggests a future scenario portraying a massive exodus of Japan’s youth, who, literate with state-of-the-art information and communication technologies (ICTs) move en masse to Japan’s northern island of Hokkaido to launch a cyber-revolution from the peripheries. The thesis employs a Futures Triangle Analysis (FTA) as the macro organizing framework and as such examines both pushes of the present and weights from the past before moving to focus on the pulls to the future represented by the seven texts mentioned above. Inayatullah’s (1999) Causal Layered Analysis (CLA) is the analytical framework used in examining the texts. Poststructuralist concepts derived primarily from the work of Michel Foucault are a particular (but not exclusive) reference point for the analytical approach it encompasses. The research questions which reflect the triangulated analytic matrix are: 1. What are the pushes—in terms of current trends—that are affecting Japan’s futures? 2. What are the historical and cultural weights that influence Japan’s futures? 3. What are the emerging transformative Japanese images of the future discourses, as embodied in actual texts, and what potential do they offer for transformative change in Japan? Research questions one and two are discussed in Chapter five and research question three is discussed in Chapter six. The first two research questions should be considered preliminary. The weights outlined in Chapter five indicate that the forces working against change in Japan are formidable, structurally deep-rooted, wide-spread, and under-recognized as change-adverse. Findings and analyses of the push dimension reveal strong forces towards a potentially very different type of Japan. However it is the seven contemporary Japanese images of the future, from which there is hope for transformative potential, which form the analytical heart of the thesis. In analyzing these texts the thesis establishes the richness of Japan’s images of the future and, as such, demonstrates the robustness of Japan’s stance vis-à-vis the problem of a perceived map-less and model-less future for Japan. Frontier is a useful image of the future, whose hybrid textuality, consisting of government, business, academia, and creative minority perspectives, demonstrates the earnestness of Japan’s leaders in favour of the creation of innovative futures for that nation. Slow is powerful in its aim to reconceptualize Japan’s philosophies of temporality, and build a new kind of nation founded on the principles of a human-oriented and expanded vision of economy based around the core metaphor of slowness culture. However its viability in Japan, with its post-Meiji historical pushes to an increasingly speed-obsessed social construction of reality, could render it impotent. MuRatopia is compelling in its creative hybridity indicative of an advanced IT society, set in a modern day utopian space based upon principles of a high communicative social paradigm, and sustainability. IMAGINATION / CREATION is less the plan than the platform for a new discussion on Japan’s transformation from an econo-centric social framework to a new Creative Age. It accords with emerging discourses from the Creative Industries, which would re-conceive of Japan as a leading maker of meaning, rather than as the so-called guzu, a term referred to in the book meaning ‘laggard’. In total, Love Japan is still the most idiosyncratic of all the images of the future discussed. Its communication style, which appeals to Japan’s youth cohort, establishes it as a potentially formidable change agent in a competitive market of futures images. Visionary People is a compelling image for its revolutionary and subversive stance against Japan’s vision-less political leadership, showing that it is the people, not the futures-making elite or aristocracy who must take the lead and create a new vanguard for the nation. Finally, Murakami’s Exodus cannot be ruled out as a compelling image of the future. Sharing the appeal of Tanja’s Love Japan to an increasingly disenfranchised youth, Exodus portrays a near-term future that is achievable in the here and now, by Japan’s teenagers, using information and communications technologies (ICTs) to subvert leadership, and create utopianist communities based on alternative social principles. The principal contribution from this investigation in terms of theory belongs to that of developing the Japanese image of the future. In this respect, the literature reviews represent a significant compilation, specifically about Japanese futures thinking, the Japanese image of the future, and the Japanese utopia. Though not exhaustive, this compilation will hopefully serve as a useful starting point for future research, not only for the Japanese image of the future, but also for all image of the future research. Many of the sources are in Japanese and their English summations are an added reason to respect this achievement. Secondly, the seven images of the future analysed in Chapter six represent the first time that Japanese image of the future texts have been systematically organized and analysed. Their translation from Japanese to English can be claimed as a significant secondary contribution. What is more, they have been analysed according to current futures methodologies that reveal a layeredness, depth, and overall richness existing in Japanese futures images. Revealing this image-richness has been one of the most significant findings of this investigation, suggesting that there is fertile research to be found from this still under-explored field, whose implications go beyond domestic Japanese concerns, and may offer fertile material for futures thinkers and researchers, Japanologists, social planners, and policy makers.

Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images

In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs. It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain. Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate. Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.