161 research outputs found
Detecting and localizing edges composed of steps, peaks and roofs
It is well known that the projection of depth or orientation
discontinuities in a physical scene results in image
intensity edges which are not ideal step edges but
are more typically a combination of steps, peak and
roof profiles. However most edge detection schemes
ignore the composite nature of these edges, resulting
in systematic errors in detection and localization. We
address the problem of detecting and localizing these
edges, while at the same time also solving the problem
of false responses in smoothly shaded regions with
constant gradient of the image brightness. We show
that a class of nonlinear filters, known as quadratic
filters, are appropriate for this task, while linear filters
are not. A series of performance criteria are derived
for characterizing the SNR, localization and multiple
responses of these filters in a manner analogous to
Canny's criteria for linear filters. A two-dimensional
version of the approach is developed which has the
property of being able to represent multiple edges at the
same location and determine the orientation of each
to any desired precision. This permits junctions to be
localized without rounding. Experimental results are
presented
Detecting and localizing edges composed of steps, peaks and roofs
Caption title.Includes bibliographical references (p. 17-18).Research supported by the U.S. Army Research Office. DAAL01-86-K-0171Pietro Perona and Jitendra Malik
Deformable kernels for early vision
Early vision algorithms often have a first stage of linear filtering
that 'extracts' from the image information at multiple
scales of resolution and multiple orientations. A common
difficulty in the design and implementation of such
schemes is that one feels compelled to discretize coarsely
the space of scales and orientations in order to reduce computation and storage costs. This discretization produces
anisotropies due to a loss of traslation-, rotation- scaling- invariance that makes early vision algorithms less precise and
more difficult to design. This need not be so: one can compute
and store efficiently the response of families of linear
filters defined on a continuum of orientations and scales. A
technique is presented that allows (1) to compute the best approximation of a given family using linear combinations of
a small number of 'basis' functions; (2) to describe all finite-dimensional families, i.e. the families of filters for which a
finite dimensional representation is possible with no error.
The technique is general and can be applied to generating
filters in arbitrary dimensions. Experimental results are presented that demonstrate the applicability of the technique to
generating multi-orientation multi-scale 20 edge-detection
kernels. The implementation issues are also discussed
Deformable kernels for early vision
Early vision algorithms often have a first stage of linear-filtering that `extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. A technique is presented that allows: 1) computing the best approximation of a given family using linear combinations of a small number of `basis' functions; and 2) describing all finite-dimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations. The relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multi-scale 2D edge-detection kernels. The implementation issues are also discussed
Finding Boundaries in Images
In computational vision, finding the boundaries of the regions
in a image which correspond to different surfaces in the scene is
usually approached as a problem of detecting brightness edges.
In this paper, we argue that this is a limited view. Boundaries
in images could be associated with differences on a number of visual
attributes-brightness, color, texture, stereoscopic disparity
and motion-all of which are utilized in human vision. Machine
vision systems should do the same. We argue that convolution of
the image with a bank of Gaussian derivative filters is a suitable
common first stage for this task. We also present some new results
on the problem of detecting and localizing brightness edges
composed of step, peak and roof profiles
2-D edge feature extraction to subpixel accuracy using the generalized energy approach
Precision edge feature extraction is a very important step in vision, Researchers mainly use step edges to model an edge at subpixel level. In this paper we describe a new technique for two dimensional edge feature extraction to subpixel accuracy using a general edge model. Using six basic edge types to model edges, the edge parameters at subpixel level are extracted by fitting a model to the image signal using least-.squared error fitting technique.<br /
Deformable kernels for early vision
Caption title.Includes bibliographical references (p. 22-24).Research supported by the U.S. Army Research Office. DAAL01-86-K-0171Pietro Perona
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