Morphology and design: reconciling intellect, intuition, and ethics in the reflective practice of architecture

This paper starts by exploring models of knowledge in order to place architectural knowledgein relation to the forms of knowledge that have been developed by other academic disciplineswithin the universities. In the light of suggestions that the low esteem in which architectureis held within the universities may be due to its basis in practice and its apparent lack of acoherent body of knowledge, the proposition is made that morphology has a special place inadvancing architectural knowledge because it is able to make the link between design and itssocial consequences. Understanding this relationship is vital if architecture is to defend itsposition as an art that is of general social relevance as opposed to being the domain of thesocially privileged. Kolb?s learning cycle is introduced as a device to track the forms of knowledgethat are essential to the reflective practice of a genuinely social architecture and to relatethese to the insights into morphology and design that have been provided by space syntaxover the past two decades. ?Sheltered? housing for older people is taken as an example of howa morphological approach can offer an enlightened critique of design guidance that articulatesthe authentic experiences of the inhabitants. The creative interplay of intellect and intuition isconsidered in relation to how morphology can help to clarify strategic design choices early onin the design process. The importance of briefing and evaluation are also stressed as essentialingredients that will enable space syntax to turn Kolb?s learning cycle into a dynamic learningprocess. The paper concludes by proposing an ethical framework for design. This paper starts by exploring models of knowledge in order to place architectural knowledgein relation to the forms of knowledge that have been developed by other academic disciplineswithin the universities. In the light of suggestions that the low esteem in which architectureis held within the universities may be due to its basis in practice and its apparent lack of acoherent body of knowledge, the proposition is made that morphology has a special place inadvancing architectural knowledge because it is able to make the link between design and itssocial consequences. Understanding this relationship is vital if architecture is to defend itsposition as an art that is of general social relevance as opposed to being the domain of thesocially privileged. Kolb?s learning cycle is introduced as a device to track the forms of knowledgethat are essential to the reflective practice of a genuinely social architecture and to relatethese to the insights into morphology and design that have been provided by space syntaxover the past two decades. ?Sheltered? housing for older people is taken as an example of howa morphological approach can offer an enlightened critique of design guidance that articulatesthe authentic experiences of the inhabitants. The creative interplay of intellect and intuition isconsidered in relation to how morphology can help to clarify strategic design choices early onin the design process. The importance of briefing and evaluation are also stressed as essentialingredients that will enable space syntax to turn Kolb?s learning cycle into a dynamic learningprocess. The paper concludes by proposing an ethical framework for design

Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

Cracks on a painting is not a defect but an inimitable signature of an artwork which can be used for origin examination, aging monitoring, damage identification, and even forgery detection. This work presents the development of a new methodology and corresponding toolbox for the extraction and characterization of information from an image of a craquelure pattern. The proposed approach processes craquelure network as a graph. The graph representation captures the network structure via mutual organization of junctions and fractures. Furthermore, it is invariant to any geometrical distortions. At the same time, our tool extracts the properties of each node and edge individually, which allows to characterize the pattern statistically. We illustrate benefits from the graph representation and statistical features individually using novel Graph Neural Network and hand-crafted descriptors correspondingly. However, we also show that the best performance is achieved when both techniques are merged into one framework. We perform experiments on the dataset for paintings' origin classification and demonstrate that our approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop

Automated artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels

The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Anemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Anemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%

A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes