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The aim of this study is to reveal the opinions of the architect in the design of Konya Science Center, to analyze the relationship established by the structure with the city, its scale with surrounding texture and human, the access to structure, its form, material, technology and its spatial presentation to determine whether it is an image structure or not. For this purpose, science centers’ design purpose and their importance for the city are explained at first, examples across the world are given. Observations and interviews are made, photographs of the structure are taken and any kind of written and drawn resources are utilized in order to determine whether the selected structure is in accordance with the definition “image structure”. As a result, it is determined with this study that Konya Science Center is “an image structure” for the city in parallel with the developing trends in design and construction technology fields in general terms after its analysis in architectural terms.

We consider operations that change the size of images, either shrinks or blow-ups. Image processing offers numerous possibilities, put at everyone’s disposal with such computer programs as Adobe Photoshop. We consider a different class of operations, aimed at immediate visual awareness, rather than pixel arrays. We demonstrate cases of blow-ups that do not sacrifice apparent resolution. This apparent information gain is due to “amodal occlusion.”, Industrial Design, Industrial Design Engineering

We consider operations that change the size of images, either shrinks or blow-ups. Image processing offers numerous possibilities, put at everyone’s disposal with such computer programs as Adobe Photoshop. We consider a different class of operations, aimed at immediate visual awareness, rather than pixel arrays. We demonstrate cases of blow-ups that do not sacrifice apparent resolution. This apparent information gain is due to “amodal occlusion.”, Industrial Design, Industrial Design Engineering

Author Posting. © University of Chicago, 2011. This article is posted here by permission of University of Chicago for personal use, not for redistribution. The definitive version was published in American Naturalist 177 (2011): 681-690, doi:10.1086/659626., It might seem obvious that a camouflaged animal must generally match its background whereas to be conspicuous an organism must differ from the background. However, the image parameters (or statistics) that evaluate the conspicuousness of patterns and textures are seldom well defined, and animal coloration patterns are rarely compared quantitatively with their respective backgrounds. Here we examine this issue in the Australian giant cuttlefish Sepia apama. We confine our analysis to the best-known and simplest image statistic, the correlation in intensity between neighboring pixels. Sepia apama can rapidly change their body patterns from assumed conspicuous signaling to assumed camouflage, thus providing an excellent and unique opportunity to investigate how such patterns differ in a single visual habitat. We describe the intensity variance and spatial frequency power spectra of these differing body patterns and compare these patterns with the backgrounds against which they are viewed. The measured image statistics of camouflaged animals closely resemble their backgrounds, while signaling animals differ significantly from their backgrounds. Our findings may provide the basis for a set of general rules for crypsis and signals. Furthermore, our methods may be widely applicable to the quantitative study of animal coloration., S.Z. was supported by a Case award from the Biotechnology and Biological Sciences Research Council and QinetiQ and is currently supported by Office of Naval Research (ONR) grant N00014-09-1-1053. R.T.H. received partial support from ONR grant N0001406-1- 0202.

Author Posting. © University of Chicago, 2011. This article is posted here by permission of University of Chicago for personal use, not for redistribution. The definitive version was published in American Naturalist 177 (2011): 681-690, doi:10.1086/659626., It might seem obvious that a camouflaged animal must generally match its background whereas to be conspicuous an organism must differ from the background. However, the image parameters (or statistics) that evaluate the conspicuousness of patterns and textures are seldom well defined, and animal coloration patterns are rarely compared quantitatively with their respective backgrounds. Here we examine this issue in the Australian giant cuttlefish Sepia apama. We confine our analysis to the best-known and simplest image statistic, the correlation in intensity between neighboring pixels. Sepia apama can rapidly change their body patterns from assumed conspicuous signaling to assumed camouflage, thus providing an excellent and unique opportunity to investigate how such patterns differ in a single visual habitat. We describe the intensity variance and spatial frequency power spectra of these differing body patterns and compare these patterns with the backgrounds against which they are viewed. The measured image statistics of camouflaged animals closely resemble their backgrounds, while signaling animals differ significantly from their backgrounds. Our findings may provide the basis for a set of general rules for crypsis and signals. Furthermore, our methods may be widely applicable to the quantitative study of animal coloration., S.Z. was supported by a Case award from the Biotechnology and Biological Sciences Research Council and QinetiQ and is currently supported by Office of Naval Research (ONR) grant N00014-09-1-1053. R.T.H. received partial support from ONR grant N0001406-1- 0202.

Author Posting. © University of Chicago, 2011. This article is posted here by permission of University of Chicago for personal use, not for redistribution. The definitive version was published in American Naturalist 177 (2011): 681-690, doi:10.1086/659626., It might seem obvious that a camouflaged animal must generally match its background whereas to be conspicuous an organism must differ from the background. However, the image parameters (or statistics) that evaluate the conspicuousness of patterns and textures are seldom well defined, and animal coloration patterns are rarely compared quantitatively with their respective backgrounds. Here we examine this issue in the Australian giant cuttlefish Sepia apama. We confine our analysis to the best-known and simplest image statistic, the correlation in intensity between neighboring pixels. Sepia apama can rapidly change their body patterns from assumed conspicuous signaling to assumed camouflage, thus providing an excellent and unique opportunity to investigate how such patterns differ in a single visual habitat. We describe the intensity variance and spatial frequency power spectra of these differing body patterns and compare these patterns with the backgrounds against which they are viewed. The measured image statistics of camouflaged animals closely resemble their backgrounds, while signaling animals differ significantly from their backgrounds. Our findings may provide the basis for a set of general rules for crypsis and signals. Furthermore, our methods may be widely applicable to the quantitative study of animal coloration., S.Z. was supported by a Case award from the Biotechnology and Biological Sciences Research Council and QinetiQ and is currently supported by Office of Naval Research (ONR) grant N00014-09-1-1053. R.T.H. received partial support from ONR grant N0001406-1- 0202.

The statistical structure of natural images has sparked interest in regards to both visual processing of such images and the aesthetic responses they elicit. We bring together these two lines of research under the simple proposition that early visual processing, more specifically visual sensitivity, may mediate visual preferences. In our first set of experiments, we measured both visual preference and visual sensitivity in the same observers, using patterns varying in the slope of their amplitude spectrum. Our results found similar functions for both variables, supporting our hypothesis. In a second set of experiments, we investigated the same question with regards to simple spatial frequency variations, and found even more convincing results. We conclude that early visual processing is implicated in visual preference, supporting the recently emerging sensory-based approaches to understanding visual preference.

This article shows how a (linear) scale-space representation can be defined for discrete signals of arbitrary dimension. The treatment is based upon the assumptions that (i) the scale-space representation should be defined by convolving the original signal with a one-parameter family of symmetric smoothing kernels possessing a semi-group property, and (ii) local extrema must not be enhanced when the scale parameter is increased continuously. It is shown that given these requirements the scale-space representation must satisfy the differential equation \partial_t L = A L for some linear and shift invariant operator A satisfying locality, positivity, zero sum, and symmetry conditions. Examples in one, two, and three dimensions illustrate that this corresponds to natural semi-discretizations of the continuous (second-order) diffusion equation using different discrete approximations of the Laplacean operator. In a special case the multi-dimensional representation is given by convolution with the one-dimensional discrete analogue of the Gaussian kernel along each dimension., QC 20130423

This article shows how a (linear) scale-space representation can be defined for discrete signals of arbitrary dimension. The treatment is based upon the assumptions that (i) the scale-space representation should be defined by convolving the original signal with a one-parameter family of symmetric smoothing kernels possessing a semi-group property, and (ii) local extrema must not be enhanced when the scale parameter is increased continuously. It is shown that given these requirements the scale-space representation must satisfy the differential equation \partial_t L = A L for some linear and shift invariant operator A satisfying locality, positivity, zero sum, and symmetry conditions. Examples in one, two, and three dimensions illustrate that this corresponds to natural semi-discretizations of the continuous (second-order) diffusion equation using different discrete approximations of the Laplacean operator. In a special case the multi-dimensional representation is given by convolution with the one-dimensional discrete analogue of the Gaussian kernel along each dimension., QC 20130423

This article shows how a (linear) scale-space representation can be defined for discrete signals of arbitrary dimension. The treatment is based upon the assumptions that (i) the scale-space representation should be defined by convolving the original signal with a one-parameter family of symmetric smoothing kernels possessing a semi-group property, and (ii) local extrema must not be enhanced when the scale parameter is increased continuously. It is shown that given these requirements the scale-space representation must satisfy the differential equation \partial_t L = A L for some linear and shift invariant operator A satisfying locality, positivity, zero sum, and symmetry conditions. Examples in one, two, and three dimensions illustrate that this corresponds to natural semi-discretizations of the continuous (second-order) diffusion equation using different discrete approximations of the Laplacean operator. In a special case the multi-dimensional representation is given by convolution with the one-dimensional discrete analogue of the Gaussian kernel along each dimension., QC 20130423

This article shows how a (linear) scale-space representation can be defined for discrete signals of arbitrary dimension. The treatment is based upon the assumptions that (i) the scale-space representation should be defined by convolving the original signal with a one-parameter family of symmetric smoothing kernels possessing a semi-group property, and (ii) local extrema must not be enhanced when the scale parameter is increased continuously. It is shown that given these requirements the scale-space representation must satisfy the differential equation \partial_t L = A L for some linear and shift invariant operator A satisfying locality, positivity, zero sum, and symmetry conditions. Examples in one, two, and three dimensions illustrate that this corresponds to natural semi-discretizations of the continuous (second-order) diffusion equation using different discrete approximations of the Laplacean operator. In a special case the multi-dimensional representation is given by convolution with the one-dimensional discrete analogue of the Gaussian kernel along each dimension., QC 20130423

This article shows how a (linear) scale-space representation can be defined for discrete signals of arbitrary dimension. The treatment is based upon the assumptions that (i) the scale-space representation should be defined by convolving the original signal with a one-parameter family of symmetric smoothing kernels possessing a semi-group property, and (ii) local extrema must not be enhanced when the scale parameter is increased continuously. It is shown that given these requirements the scale-space representation must satisfy the differential equation \partial_t L = A L for some linear and shift invariant operator A satisfying locality, positivity, zero sum, and symmetry conditions. Examples in one, two, and three dimensions illustrate that this corresponds to natural semi-discretizations of the continuous (second-order) diffusion equation using different discrete approximations of the Laplacean operator. In a special case the multi-dimensional representation is given by convolution with the one-dimensional discrete analogue of the Gaussian kernel along each dimension., QC 20130423

This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects: The formulation of a scale-space theory for discrete signals. Previously, the scale-space concept has been expressed for continuous signals only. We propose that the canonical way to construct a scale-space for discrete signals is by convolution with a kernel called the discrete analogue of the Gaussian kernel, or equivalently by solving a semi-discretized version of the diffusion equation. Both the one-dimensional and two-dimensional cases are covered. An extensive analysis of discrete smoothing kernels is carried out for one-dimensional signals and the discrete scale-space properties of the most common discretizations to the continuous theory are analysed. A representation, called the scale-space primal sketch, which gives a formal description of the hierarchical relations between structures at different levels of scale. It is aimed at making information in the scale-space representation explicit. We give a theory for its construction and an algorithm for computing it. A theory for extracting significant image structures and determining the scales of these structures from this representation in a solely bottom-up data-driven way. Examples demonstrating how such qualitative information extracted from the scale-space primal sketch can be used for guiding and simplifying other early visual processes. Applications are given to edge detection, histogram analysis and classification based on local features. Among other possible applications one can mention perceptual grouping, texture analysis, stereo matching, model matching and motion. A detailed theoretical analysis of the evolution properties of critical points and blobs in scale-space, comprising drift velocity estimates under scale-space smoothing, a classification of the possible ty, QC 20120119

This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects: The formulation of a scale-space theory for discrete signals. Previously, the scale-space concept has been expressed for continuous signals only. We propose that the canonical way to construct a scale-space for discrete signals is by convolution with a kernel called the discrete analogue of the Gaussian kernel, or equivalently by solving a semi-discretized version of the diffusion equation. Both the one-dimensional and two-dimensional cases are covered. An extensive analysis of discrete smoothing kernels is carried out for one-dimensional signals and the discrete scale-space properties of the most common discretizations to the continuous theory are analysed. A representation, called the scale-space primal sketch, which gives a formal description of the hierarchical relations between structures at different levels of scale. It is aimed at making information in the scale-space representation explicit. We give a theory for its construction and an algorithm for computing it. A theory for extracting significant image structures and determining the scales of these structures from this representation in a solely bottom-up data-driven way. Examples demonstrating how such qualitative information extracted from the scale-space primal sketch can be used for guiding and simplifying other early visual processes. Applications are given to edge detection, histogram analysis and classification based on local features. Among other possible applications one can mention perceptual grouping, texture analysis, stereo matching, model matching and motion. A detailed theoretical analysis of the evolution properties of critical points and blobs in scale-space, comprising drift velocity estimates under scale-space smoothing, a classification of the possible ty, QC 20120119

This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects: The formulation of a scale-space theory for discrete signals. Previously, the scale-space concept has been expressed for continuous signals only. We propose that the canonical way to construct a scale-space for discrete signals is by convolution with a kernel called the discrete analogue of the Gaussian kernel, or equivalently by solving a semi-discretized version of the diffusion equation. Both the one-dimensional and two-dimensional cases are covered. An extensive analysis of discrete smoothing kernels is carried out for one-dimensional signals and the discrete scale-space properties of the most common discretizations to the continuous theory are analysed. A representation, called the scale-space primal sketch, which gives a formal description of the hierarchical relations between structures at different levels of scale. It is aimed at making information in the scale-space representation explicit. We give a theory for its construction and an algorithm for computing it. A theory for extracting significant image structures and determining the scales of these structures from this representation in a solely bottom-up data-driven way. Examples demonstrating how such qualitative information extracted from the scale-space primal sketch can be used for guiding and simplifying other early visual processes. Applications are given to edge detection, histogram analysis and classification based on local features. Among other possible applications one can mention perceptual grouping, texture analysis, stereo matching, model matching and motion. A detailed theoretical analysis of the evolution properties of critical points and blobs in scale-space, comprising drift velocity estimates under scale-space smoothing, a classification of the possible ty, QC 20120119

This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects: The formulation of a scale-space theory for discrete signals. Previously, the scale-space concept has been expressed for continuous signals only. We propose that the canonical way to construct a scale-space for discrete signals is by convolution with a kernel called the discrete analogue of the Gaussian kernel, or equivalently by solving a semi-discretized version of the diffusion equation. Both the one-dimensional and two-dimensional cases are covered. An extensive analysis of discrete smoothing kernels is carried out for one-dimensional signals and the discrete scale-space properties of the most common discretizations to the continuous theory are analysed. A representation, called the scale-space primal sketch, which gives a formal description of the hierarchical relations between structures at different levels of scale. It is aimed at making information in the scale-space representation explicit. We give a theory for its construction and an algorithm for computing it. A theory for extracting significant image structures and determining the scales of these structures from this representation in a solely bottom-up data-driven way. Examples demonstrating how such qualitative information extracted from the scale-space primal sketch can be used for guiding and simplifying other early visual processes. Applications are given to edge detection, histogram analysis and classification based on local features. Among other possible applications one can mention perceptual grouping, texture analysis, stereo matching, model matching and motion. A detailed theoretical analysis of the evolution properties of critical points and blobs in scale-space, comprising drift velocity estimates under scale-space smoothing, a classification of the possible ty, QC 20120119