3,462 research outputs found
Leaf recognition for accurate plant classification.
Doctor of Philosophy in Computer Science, University of KwaZulu-Natal, Durban 2017.Plants are the most important living organisms on our planet because they are
sources of energy and protect our planet against global warming. Botanists were
the first scientist to design techniques for plant species recognition using leaves. Although
many techniques for plant recognition using leaf images have been proposed
in the literature, the precision and the quality of feature descriptors for shape, texture,
and color remain the major challenges. This thesis investigates the precision
of geometric shape features extraction and improved the determination of the Minimum
Bounding Rectangle (MBR). The comparison of the proposed improved MBR
determination method to Chaudhuri's method is performed using Mean Absolute
Error (MAE) generated by each method on each edge point of the MBR. On the
top left point of the determined MBR, Chaudhuri's method has the MAE value of
26.37 and the proposed method has the MAE value of 8.14.
This thesis also investigates the use of the Convexity Measure of Polygons for the
characterization of the degree of convexity of a given leaf shape. Promising results
are obtained when using the Convexity Measure of Polygons combined with other
geometric features to characterize leave images, and a classification rate of 92% was
obtained with a Multilayer Perceptron Neural Network classifier. After observing
the limitations of the Convexity Measure of Polygons, a new shape feature called
Convexity Moments of Polygons is presented in this thesis. This new feature has
the invariant properties of the Convexity Measure of Polygons, but is more precise
because it uses more than one value to characterize the degree of convexity of a
given shape. Promising results are obtained when using the Convexity Moments
of Polygons combined with other geometric features to characterize the leaf images
and a classification rate of 95% was obtained with the Multilayer Perceptron Neural
Network classifier.
Leaf boundaries carry valuable information that can be used to distinguish between
plant species. In this thesis, a new boundary-based shape characterization
method called Sinuosity Coefficients is proposed. This method has been used in
many fields of science like Geography to describe rivers meandering. The Sinuosity
Coefficients is scale and translation invariant. Promising results are obtained when
using Sinuosity Coefficients combined with other geometric features to characterize
the leaf images, a classification rate of 80% was obtained with the Multilayer
Perceptron Neural Network classifier.
Finally, this thesis implements a model for plant classification using leaf images,
where an input leaf image is described using the Convexity Moments, the Sinuosity
Coefficients and the geometric features to generate a feature vector for the recognition
of plant species using a Radial Basis Neural Network. With the model designed
and implemented the overall classification rate of 97% was obtained
A measure of non-convexity in the plane and the Minkowski sum
In this paper a measure of non-convexity for a simple polygonal region in the
plane is introduced. It is proved that for "not far from convex" regions this
measure does not decrease under the Minkowski sum operation, and guarantees
that the Minkowski sum has no "holes".Comment: 5 figures; Discrete and Computational Geometry, 201
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method
Polygons as optimal shapes with convexity constraint
In this paper, we focus on the following general shape optimization problem:
\min\{J(\Om), \Om convex, \Om\in\mathcal S_{ad}\}, where is a set of 2-dimensional admissible shapes and
is a shape functional. Using a specific
parameterization of the set of convex domains, we derive some extremality
conditions (first and second order) for this kind of problem. Moreover, we use
these optimality conditions to prove that, for a large class of functionals
(satisfying a concavity like property), any solution to this shape optimization
problem is a polygon
Moduli spaces of toric manifolds
We construct a distance on the moduli space of symplectic toric manifolds of
dimension four. Then we study some basic topological properties of this space,
in particular, path-connectedness, compactness, and completeness. The
construction of the distance is related to the Duistermaat-Heckman measure and
the Hausdorff metric. While the moduli space, its topology and metric, may be
constructed in any dimension, the tools we use in the proofs are
four-dimensional, and hence so is our main result.Comment: To appear in Geometriae Dedicata, minor changes to previous version,
19 pages, 6 figure
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