建築における三次元空間の二次元表現

A Study on the Connection between Nature and Architectural Space in Le Corbusier’s Venice Hospital Project

New sequential exact Euclidean distance transform algorithms based on convex analysis

A distance transformation (DT) takes a binary image as input and generates a distance map image in which the value of each pixel is its distance to a given set of object pixels in the binary image. In this research, DT's for multi class data (MCDTs) are developed which generate both a distance map and a class map containing for each pixel the class of the closest object. Results indicate that the MCDT based on the Fast Exact Euclidean Distance (FEED) method is a factor 2 tot 4 faster than MCDTs based on exact or semi-exact euclidean distance (ED) transformations, and is only a factor 2 to 4 slower than the MCDT based on the crude city-block approximation of the ED. In the second part of this research, the MCDTs were adapted such that they could be used for the fast generation of distance and class maps for video sequences. The frames of the sequences contain a number of fixed objects and a moving object, where each object has a separate label. Results show that the FEED based version is a factor 2 to 3.5 faster than the fastest of all the other video-MCDTs which is based on the chamfer 3,4 distance measure. FEED is even a factor 3.5 to 10 faster than another fast exact ED transformation. With video, multi class FEED it will be possible to measure distances from a moving object to various identified stationary objects with nearly the frame rate of a webcam. This will be very useful when the risk exists that objects move outside surveillance limits.

Scholars have long stressed the problem of ornament and expression when considering Viennese modernism. By the first decade of the 20th century, however, the avant-garde had shifted its focus from the surface to the interior. Adolf Loos (1870–1933), together with Josef Frank (1885–1967) and Oskar Strnad (1879–1935), led this generation of architects to interpret modernism through culture and lifestyle. They were interested in the experience of architectural space: how it could be navigated, inhabited, and designed to reflect the modern way of life while also offering respite from it. The New Space traces the theoretical conversation about space carried out in the writings and built works of Loos, Frank, and Strnad over four decades. The three ultimately explored what Le Corbusier would later—independently—term the architectural promenade. Lavishly illustrated with new photography and architectural plans, this important book enhances our understanding of the development of modernism and of architectural theory and practice.

This essay explores an understanding of architectural space that neither rejects out of hand nor is fully premised in prevailing assumptions about it as constitutionally independent of time, movement, experience, and the world. In so doing, various sources are examined that provide some insight on possible ways of expanding our notions of space and its involvement with vision, tactility, the body, and the body's capacities for action. Of special interest in this regard is Walter Benjamin's proposition of a “distractive art.” Under this rubric, connections between film and architecture, their inherent characteristics, and their means of appropriation are pursued. As a corollary, a few important qualitative characteristics of experience at Le Corbusier's Carpenter Center are discussed.

Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon "small" neighborhoods still outperforms it e.g. in terms of computation time. In this paper we determine the best possible maximum relative error of chamfer distances under various boundary conditions. In each case some best approximating sequences are explicitly given. Further, because of possible practical interest, we give all best approximating sequences in case of small (i.e. 5x5 and 7x7) neighborhoods.

A fast and exact Euclidean distance transformation using decomposed grayscale morphological operators is presented. Applied on a binary image, a distance transformation assigns each object pixel a value that corresponds to the shortest distance between the object pixel and the background pixels. It is shown that the large structuring element required for the Euclidean distance transformation can be easily decomposed into 3/spl times/3 windows. This is possible because the square of the Euclidean distance matrix changes uniformly both in the vertical and horizontal directions. A simple extension for a 3D Euclidean distance transformation is discussed. A fast distance transform for serial computers is also presented. Acting like thinning algorithms, the version for serial computers focuses operations only on the potential changing pixels and propagates from the boundary of objects, significantly reducing execution time. Nonsquare pixels can also be used in this algorithm. An example application, shape filtering using arbitrary sized circular dilation and erosion, is discussed. Rotation-invariant basic morphological operations can be done using this example application. >

In recent work, a graphical modeling construct called “topological patterns” has been shown to enable concise representation and direct analysis of repetitive dataflow graph sub-structures in the context of design methods and tools for digital signal processing systems [1]. In this paper, we present a formal design method for specifying topological patterns and deriving parameterized schedules from such patterns based on a novel schedule model called the scalable schedule tree. The approach represents an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for code generation. We demonstrate our methods for topological pattern representation, scalable schedule tree derivation, and associated dataflow graph code generation using a case study for image processing.

In recent work, a graphical modeling construct called "topological patterns" has been shown to enable concise representation and direct analysis of repetitive dataflow graph sub-structures in the context of design methods and tools for digital signal processing systems (Sane et al. 2010). In this paper, we present a formal design method for specifying topological patterns and deriving parameterized schedules from such patterns based on a novel schedule model called the scalable schedule tree. The approach represents an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for code generation. Through application case studies involving image processing and wireless communications, we demonstrate our methods for topological pattern representation, scalable schedule tree derivation, and associated dataflow graph code generation.

A frequently arising problem in computational geometry is when a physical structure, such as an ad-hoc wireless sensor network or a protein backbone, can measure local information about its geometry (e.g., distances, angles, and/or orientations), and the goal is to reconstruct the global geometry from this partial information. More precisely, we are given a graph, the approximate lengths of the edges, and possibly extra information, and our goal is to assign coordinates to the vertices that satisfy the given constraints up to a constant factor away from the best possible. We obtain the first subexponential-time (quasipolynomial-time) algorithm for this problem given a complete graph of Euclidean distances with additive error and no extra information. For general graphs, the analogous problem is NP-hard even with exact distances. Thus, for general graphs, we consider natural types of extra information that make the problem more tractable, including approximate angles between edges, the order type of vertices, a model of coordinate noise, or knowledge about the range of distance measurements. Our quasipolynomial-time algorithm for no extra information can also beviewed as a polynomial-time algorithm given an extremum oracle as extra information. We give several approximation algorithms and contrasting hardness results for these scenarios.

In 2003, Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) published a paper describing an algorithm that computes the exact distance transform in linear time (with respect to image size) for the rectangular binary images in the k-dimensional space ? k and distance measured with respect to L p -metric for 1?p??, which includes Euclidean distance L 2. In this paper we discuss this algorithm from theoretical and practical points of view. On the practical side, we concentrate on its Euclidean distance version, discuss the possible ways of implementing it as signed distance transform, and experimentally compare implemented algorithms. We also describe the parallelization of these algorithms and discuss the computational time savings associated with them. All these implementations will be made available as a part of the CAVASS software system developed and maintained in our group (Grevera et al. in J. Digit. Imaging 20:101---118, 2007). On the theoretical side, we prove that our version of the signed distance transform algorithm, GBDT, returns the exact value of the distance from the geometrically defined object boundary. We provide a complete proof (which was not given of Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) that all these algorithms work correctly for L p -metric with 1<p<?. We also point out that the precise form of the algorithm from Maurer et al. (IEEE Trans. Pattern Anal. Mach. Intell. 25:265---270, 2003) is not well defined for L 1 and L ? metrics. In addition, we show that the algorithm can be used to find, in linear time, the exact value of the diameter of an object, that is, the largest possible distance between any two of its elements.

Sketch-based 3D model retrieval is an important way to retrieve 3D models in mechanical part library. There are huge modal differences between sketches and 3D models. Aiming at the information redundancy of multiple views projected by the 3D model, the weak representation ability of the features extracted from the part views, in order to improve the effect of sketch-based 3D model retrieval, a sketch-based 3D mechanical part Library retrieval method is proposed to fuse the local and global dual-scale features of the part model. Firstly, a projection view representation method of 3D model based on image entropy is proposed to reduce the redundancy among multiple views. Then, a mechanical sketch model for edge contour extraction of part view is proposed, and the contour sketch of the part is extracted in depth to approximate the similarity between the view and the sketch. Finally, a visual word bag model and an improved multi-view convolutional neural network model is constructed to extract the local and global features of the contour sketch respectively. By fusing the dual-scale features, Euclidean distance is used to match the 3D model of the part based on the sketch. The experimental results on ESB datasets show that the proposed method is significantly improved in a series of evaluation indicators such as accuracy and recall rate compared with the existing 6 common methods, which verifies the feasibility and effectiveness of the proposed method.

Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision.

Currently, various industries around the world are advocating innovation, and the design of architectural spaces is no exception. However, the innovation of traditional architectural space design methods is not enough. In view of the problems of the traditional architectural space design methods, such as weak quantification, weak logic, and low level of science and technology, this paper used the particle swarm optimization algorithm in the data algorithm to study the architectural space design methods. This paper introduced the principles of architectural space design, studied some existing design methods and problems, and used particle swarm optimization algorithm to make some innovations in architectural space design methods. Experts and scholars were invited to score to verify the superiority of particle swarm optimization algorithm for architectural space design. The residents’ satisfaction rate with the building space design method using particle swarm optimization algorithm was more than 91.65%, and the average satisfaction rate of 20 residents was 93.2%. At the same time, the space experts who used the particle swarm optimization algorithm to design the building space design method scored more than 8.34 points. Innovative design of architectural space requires methods. Only by applying the right approach can the ideal design effect be achieved as quickly as possible to ensure the quality of the design.

Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity