7,709 research outputs found
A rigorous definition of axial lines: ridges on isovist fields
We suggest that 'axial lines' defined by (Hillier and Hanson, 1984) as lines
of uninterrupted movement within urban streetscapes or buildings, appear as
ridges in isovist fields (Benedikt, 1979). These are formed from the maximum
diametric lengths of the individual isovists, sometimes called viewsheds, that
make up these fields (Batty and Rana, 2004). We present an image processing
technique for the identification of lines from ridges, discuss current
strengths and weaknesses of the method, and show how it can be implemented
easily and effectively.Comment: 18 pages, 5 figure
Automatic Detection of Calibration Grids in Time-of-Flight Images
It is convenient to calibrate time-of-flight cameras by established methods,
using images of a chequerboard pattern. The low resolution of the amplitude
image, however, makes it difficult to detect the board reliably. Heuristic
detection methods, based on connected image-components, perform very poorly on
this data. An alternative, geometrically-principled method is introduced here,
based on the Hough transform. The projection of a chequerboard is represented
by two pencils of lines, which are identified as oriented clusters in the
gradient-data of the image. A projective Hough transform is applied to each of
the two clusters, in axis-aligned coordinates. The range of each transform is
properly bounded, because the corresponding gradient vectors are approximately
parallel. Each of the two transforms contains a series of collinear peaks; one
for every line in the given pencil. This pattern is easily detected, by
sweeping a dual line through the transform. The proposed Hough-based method is
compared to the standard OpenCV detection routine, by application to several
hundred time-of-flight images. It is shown that the new method detects
significantly more calibration boards, over a greater variety of poses, without
any overall loss of accuracy. This conclusion is based on an analysis of both
geometric and photometric error.Comment: 11 pages, 11 figures, 1 tabl
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering
We propose a new pattern recognition method that is able to reconstruct the
3D structure of the active part of a fault network using the spatial location
of earthquakes. The method is a generalization of the so-called dynamic
clustering method, that originally partitions a set of datapoints into
clusters, using a global minimization criterion over the spatial inertia of
those clusters. The new method improves on it by taking into account the full
spatial inertia tensor of each cluster, in order to partition the dataset into
fault-like, anisotropic clusters. Given a catalog of seismic events, the output
is the optimal set of plane segments that fits the spatial structure of the
data. Each plane segment is fully characterized by its location, size and
orientation. The main tunable parameter is the accuracy of the earthquake
localizations, which fixes the resolution, i.e. the residual variance of the
fit. The resolution determines the number of fault segments needed to describe
the earthquake catalog, the better the resolution, the finer the structure of
the reconstructed fault segments. The algorithm reconstructs successfully the
fault segments of synthetic earthquake catalogs. Applied to the real catalog
constituted of a subset of the aftershocks sequence of the 28th June 1992
Landers earthquake in Southern California, the reconstructed plane segments
fully agree with faults already known on geological maps, or with blind faults
that appear quite obvious on longer-term catalogs. Future improvements of the
method are discussed, as well as its potential use in the multi-scale study of
the inner structure of fault zones
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The Connected Component Algorithm on The NON-VON Supercomputer
The NON-VON Supercomputer is a highly parallel tree-structured computer that is being Implemented at Columbia University. In this paper, we demonstrate that tree architectures with their favorable characteristics for VLSI Implementation, and fast global broadcast, lend themselves easily and naturally to the representation and manipulation of Images represented by hierarchical data structures A description of NON-VON architecture IS presented With an emphasis on the special architectural features that will be used m our Image understanding algorithms. We adopt a variation of the quad tree data structure, called the binary Image tree, to represent images in the NON-VON tree We show how Images are loaded in the NON-VON tree, and present the algorithm for budding the binary Image trees. An efficient Implementation of the connected component labeling algorithm on NON-VON is then presented Simulation results are discussed, and we show the fast execution time of the algorithm on NON-VON. Other algorithms are also developed, such as hlstogrammlng, Hough transform, Set operations and Image correlation, and we can conclude that NON-VON can be used to Implement efficiently several :important Image understanding task
Towards binocular active vision in a robot head system
This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the âsearchlight metaphorâ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a âstepping stoneâ visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade
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