Abstract: In this study, an automated building and tree detection
method is proposed using DSM data and true orthophoto image. A
multiscale matched filtering is used on DSM data. Therefore, first
watershed transform is applied. Then, Otsu’s thresholding method
is used as an adaptive threshold to segment each watershed region.
Detected objects are masked with NDVI to separate buildings and
trees. The proposed method is able to detect buildings and trees
without entering any elevation threshold. We tested our method on
ISPRS semantic labeling dataset and obtained promising results.
Abstract: In this paper, we present a neural approach for
unsupervised natural color-texture image segmentation, which is
based on both Kohonen maps and mathematical morphology, using
a combination of the texture and the image color information of the
image, namely, the fractal features based on fractal dimension are
selected to present the information texture, and the color features
presented in RGB color space. These features are then used to train
the network Kohonen, which will be represented by the underlying
probability density function, the segmentation of this map is made
by morphological watershed transformation. The performance of our
color-texture segmentation approach is compared first, to color-based
methods or texture-based methods only, and then to k-means method.
Abstract: This text studies glass bottle intelligent inspector
based machine vision instead of manual inspection. The system
structure is illustrated in detail in this paper. The text presents the
method based on watershed transform methods to segment the
possible defective regions and extract features of bottle wall by rules.
Then wavelet transform are used to exact features of bottle finish
from images. After extracting features, the fuzzy support vector
machine ensemble is putted forward as classifier. For ensuring that
the fuzzy support vector machines have good classification ability,
the GA based ensemble method is used to combining the several
fuzzy support vector machines. The experiments demonstrate that
using this inspector to inspect glass bottles, the accuracy rate may
reach above 97.5%.