354 research outputs found

    The Use of Aesthetics in a Comprehensive Art Curriculum

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    This study focuses on using aesthetics in the art education curriculum. It also suggests a variety of approaches through which art educators may implement aesthetics in the classroom. Discussions of aesthetics were found in writings of Plato and Aristotle and continue to this day. Philosophers have defined aesthetics as a theory of the beautiful. Educators took this idea a step further in developing curricula and methods of educating that include aesthetics. It has been said in art education literature that aesthetics gives those who practice it a more complete understanding of art. To show the extent of benefits that aesthetics can have in art education, examples of aesthetic experiences are reviewed and discussed. The aesthetics as a philosophy of art has developed into methods used in education. These methods will be discussed. Using the knowledge that aesthetics reveals will demonstrate the importance of art through comparative analysis and historical variation. Aesthetics provide important knowledge about art that can give a classroom teacher motivational dialogue and stimulating ideas in teaching art. Helping students to understand the connection between art and aesthetics allows students to know more about and better understand the importance of each

    Sound mosaics: a graphical user interface for sound synthesis based on audio-visual associations.

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    This thesis presents the design of a Graphical User Interface (GUI) for computer-based sound synthesis to support users in the externalisation of their musical ideas when interacting with the System in order to create and manipulate sound. The approach taken consisted of three research stages. The first stage was the formulation of a novel visualisation framework to display perceptual dimensions of sound in Visual terms. This framework was based on the findings of existing related studies and a series of empirical investigations of the associations between auditory and visual precepts that we performed for the first time in the area of computer-based sound synthesis. The results of our empirical investigations suggested associations between the colour dimensions of brightness and saturation with the auditory dimensions of pitch and loudness respectively, as well as associations between the multidimensional precepts of visual texture and timbre. The second stage of the research involved the design and implementation of Sound Mosaics, a prototype GUI for sound synthesis based on direct manipulation of visual representations that make use of the visualisation framework developed in the first stage. We followed an iterative design approach that involved the design and evaluation of an initial Sound Mosaics prototype. The insights gained during this first iteration assisted us in revising various aspects of the original design and visualisation framework that led to a revised implementation of Sound Mosaics. The final stage of this research involved an evaluation study of the revised Sound Mosaics prototype that comprised two controlled experiments. First, a comparison experiment with the widely used frequency-domain representations of sound indicated that visual representations created with Sound Mosaics were more comprehensible and intuitive. Comprehensibility was measured as the level of accuracy in a series of sound image association tasks, while intuitiveness was related to subjects' response times and perceived levels of confidence. Second, we conducted a formative evaluation of Sound Mosaics, in which it was exposed to a number of users with and without musical background. Three usability factors were measured: effectiveness, efficiency, and subjective satisfaction. Sound Mosaics was demonstrated to perform satisfactorily in ail three factors for music subjects, although non-music subjects yielded less satisfactory results that can be primarily attributed to the subjects' unfamiliarity with the task of sound synthesis. Overall, our research has set the necessary groundwork for empirically derived and validated associations between auditory and visual dimensions that can be used in the design of cognitively useful GUIs for computer-based sound synthesis and related area

    Textural Difference Enhancement based on Image Component Analysis

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    In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

    Textural Difference Enhancement based on Image Component Analysis

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    In this thesis, we propose a novel image enhancement method to magnify the textural differences in the images with respect to human visual characteristics. The method is intended to be a preprocessing step to improve the performance of the texture-based image segmentation algorithms. We propose to calculate the six Tamura's texture features (coarseness, contrast, directionality, line-likeness, regularity and roughness) in novel measurements. Each feature follows its original understanding of the certain texture characteristic, but is measured by some local low-level features, e.g., direction of the local edges, dynamic range of the local pixel intensities, kurtosis and skewness of the local image histogram. A discriminant texture feature selection method based on principal component analysis (PCA) is then proposed to find the most representative characteristics in describing textual differences in the image. We decompose the image into pairwise components representing the texture characteristics strongly and weakly, respectively. A set of wavelet-based soft thresholding methods are proposed as the dictionaries of morphological component analysis (MCA) to sparsely highlight the characteristics strongly and weakly from the image. The wavelet-based thresholding methods are proposed in pair, therefore each of the resulted pairwise components can exhibit one certain characteristic either strongly or weakly. We propose various wavelet-based manipulation methods to enhance the components separately. For each component representing a certain texture characteristic, a non-linear function is proposed to manipulate the wavelet coefficients of the component so that the component is enhanced with the corresponding characteristic accentuated independently while having little effect on other characteristics. Furthermore, the above three methods are combined into a uniform framework of image enhancement. Firstly, the texture characteristics differentiating different textures in the image are found. Secondly, the image is decomposed into components exhibiting these texture characteristics respectively. Thirdly, each component is manipulated to accentuate the corresponding texture characteristics exhibited there. After re-combining these manipulated components, the image is enhanced with the textural differences magnified with respect to the selected texture characteristics. The proposed textural differences enhancement method is used prior to both grayscale and colour image segmentation algorithms. The convincing results of improving the performance of different segmentation algorithms prove the potential of the proposed textural difference enhancement method

    Fluid Dynamics of Watercolor Painting : Experiments and Modelling

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    In his classic study in 1908, A.M. Worthington gave a thorough account of splashes and their formation through visualization experiments. In more recent times, there has been renewed interest in this subject, and much of the underlying physics behind Worthington\u27s experiments has now been clarified. One specific set of such recent studies, which motivates this thesis, concerns the fluid dynamics behind Jackson Pollock\u27s drip paintings. The physical processes and the mathematical structures hidden in his works have received serious attention and have made the scientific pursuit of art a compelling area of exploration. Our current work explores the interaction of watercolors with watercolor paper. Specifically, we conduct experiments to analyze the settling patterns of droplets of watercolor paint on wet and frozen paper. Variations in paint viscosity, paper roughness, paper temperature, and the height of a released droplet are examined from time of impact, through its transient stages, until its final, dry state. Observable phenomena such as paint splashing, spreading, fingering, branching, rheological deposition, and fractal patterns are studied in detail and classified in terms of the control parameters. Using the one-dimensional (1-D) Saint-Venant differential equations, which are a simplification of the three-dimensional (3-D) Navier-Stokes equations from fluid dynamics, we created a computer-simulated, mathematical model of a droplet splash of watercolor paint onto a flat surface. The mathematical model is analyzed using a MATLAB code which considered changes in droplet height, radius, and velocity of dispersal over time. We also implemented a stochastic version of the Saint-Venant equations which captured the random fingering patterns of a droplet splash. Initial conditions for height, radius, and velocity of a radially spreading droplet were given at the onset of the simulation. Dynamic viscosity and fluid density were parameters incorporated into this system of differential equations, which could be easily adjusted in the MATLAB code for the paint type to be simulated. The stochastic nature of our model was designed to recreate the complex behavior of water splashes, the non-homogeneity of the watercolor paper, and the resulting patterns. We then computed the fractal dimension of each computer-generated droplet image to compare theoretical and experimental values. Analysis of the set of data consisting of over 10,000 trials was conducted to determine any significant statistical correlations among the spreading pattern, the number of fingers, viscosity, density and fractal dimension. Finally, we extended the system of differential equations based on the Saint-Venant equations to include the effects of temperature upon the paint-spreading pattern. In a similar manner, we compared the theoretical values of fractal dimensions generated by our MATLAB model to the experimental results for paint droplets on a frozen substrate

    The Re-Cognition of Being\u27s Infrastructure as Self-Completion

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    Wholeness as indivisible and the human being\u27s connectedness with it are the abiding themes of the Buddhist experience-rooted and process-oriented thinking that goes by the name of rDzogs-chen. From its basically holistic point of view, the human being is a sub-whole, similar to a variation on a musical theme. From another point of view, however, based on the confusion of a compacted (and hence de-compactable) totality with wholeness, the human being is seen as being a reality that is internally divided and feels uncertain about who/ what he really is. Together, the intolerable feelings of being divided and uncertain cause a yearning for wholeness and transcendence. Both wholeness and transcendence are realized in the face-to-face encounter with the experiencer\u27s real being and its recognition

    Development of a perception oriented texture-based image retrieval system for wallpapers.

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    Due to advances in computer technology, large image collections have been digitised and archived in computers. Image management systems are therefore developed to retrieve relevant images. Because of the limitations of text-based image retrieval systems, Content-Based Image Retrieval (CBIR) systems have been developed. A CBIR system usually extracts global or local contents of colour, shape and texture from an image to form a feature vector that is used to index the image. Plethora methods have been developed to extract these features, however, there is very little in the literature to study the closeness of each method to human perception. This research aims to develop a human perception oriented content-based image retrieval system for the Museum of Domestic Design & Architecture (MoDA) wallpaper images. Since texture has been widely regarded as the main feature for these images and applied in CBIR systems, psychophysical experiments were conducted to study the way human perceive texture and to evaluate five popular computational models for texture representations: Grey Level Co-occurrence Matrices (GLCM), Multi-Resolution Simultaneous Auto-Regressive (MRSAR) model, Fourier Transform (FT), Wavelet Transform (WT) and Gabor Transform (GT). By analyzing experimental results, it was found that people consider directionality and regularity to be more important in terms of texture than coarseness. Unexpectedly, none of the five models appeared to represent human perception of texture very well. It was therefore concluded that classification is needed before retrieval in order to improve retrieval performance and a new classification algorithm based on directionality and regularity for wallpaper images was developed. The experimental result showed that the evaluation algorithm worked effectively and the evaluation experiments confirmed the necessity of the classification step in the development of CBIR system for MoDA collections

    Textures, Patterns and Surfaces in Color Films

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