By introducing the object cloud into topological space, the spatial relationships between fuzzy objects transform to cloud
relationships in cloud space. According to cloud theory, all the spatial objects can be represented by three types object
cloud: point-cloud, line-cloud and area-cloud. So the 9-intersection model of spatial topological relations proposed by
Egenhofer can be extended by using the new definition of object cloud. The relationship between object clouds is
flexible relationship. Different from the crisp relationship model, 9IM, the flexible relationship model by object cloud
can be simplified to 4-intersection cloud model(4ICM), including to equal, contain, intersect and disjoint. The cloud
operation and virtue cloud can be introduced to representing the fuzzy and uncertain topological relations. The method
makes spatial data model enable to model the spatial phenomena with fuzziness and uncertainties, and enriches the cloud theory.
A transition region extraction and image segmentation algorithm in cloud-space was proposed. Mapping image into onedimensional
cloud-space by a one-to-many model, an object can be transformed into an object-cloud which with some
digital characteristics. Two neighboring objects are corresponding to two intersecting object-clouds in cloud-space and
the intersected region is just the transition region. By logical operation between intersecting clouds, we can obtain the
boundary-cloud and its digital characteristics. The entropy and hyper-entropy of boundary-cloud can determine the
reasonable scope of transition region. Calculating the average gray level of transition region as the threshold, we could
extract the object accurately. Experiments testify that the algorithm is both efficient and effective.
A fuzzy edge detection algorithm based on object-cloud and maximum fuzzy entropy principle are proposed in this
paper. According to the uncertainty of the objects in the RS image, the spatial objects in RS image space can be mapped
to the cloud space by 1:M cloud model. Object-cloud will have the digital characteristics to describe the fuzziness and
randomicity of objects in RS image. According to the cloud operation, boundary-cloud and its digital characteristics can
be achieved and the membership matrix of transition region can be constructed. By maximum fuzzy entropy principle,
edge detection can be accomplished in the membership matrix of transition region.
This paper presents a new method applied to texture feature representation in RS image based on cloud model. Aiming at
the fuzziness and randomness of RS image, we introduce the cloud theory into RS image processing in a creative way.
The digital characteristics of clouds well integrate the fuzziness and randomness of linguistic terms in a unified way and
map the quantitative and qualitative concepts. We adopt texture multi-dimensions cloud to accomplish vagueness and
randomness handling of texture feature in RS image. The method has two steps: 1) Correlativity analyzing of texture
statistical parameters in Grey Level Co-occurrence Matrix (GLCM) and parameters fuzzification. GLCM can be used to
representing the texture feature in many aspects perfectly. According to the expressive force of texture statistical
parameters and by Correlativity analyzing of texture statistical parameters, we can abstract a few texture statistical
parameters that can best represent the texture feature. By the fuzziness algorithm, the texture statistical parameters can be
mapped to fuzzy cloud space. 2) Texture multi-dimensions cloud model constructing. Based on the abstracted texture
statistical parameters and fuzziness cloud space, texture multi-dimensions cloud model can be constructed in micro-windows
of image. According to the membership of texture statistical parameters, we can achieve the samples of cloud-drop.
By backward cloud generator, the digital characteristics of texture multi-dimensions cloud model can be achieved
and the Mathematical Expected Hyper Surface(MEHS) of multi-dimensions cloud of micro-windows can be constructed.
At last, the weighted sum of the 3 digital characteristics of micro-window cloud model was proposed and used in texture
representing in RS image. The method we develop is demonstrated by applying it to texture representing in many RS
images, various performance studies testify that the method is both efficient and effective. It enriches the cloud theory,
and proposes a new idea for image texture representing and analyzing, especially RS image.
The spatial region in RS image has positional and thematic values uncertainties. Based on the uncertainties and the cloud
theory, the paper studies the representation of spatial uncertain region in image and proposes a new method applied to
spatial uncertain region representation based on cloud model. In 2-dimensional universe of discourse, by the gray and gradient or other digital characters of image, we can construct object-cloud of spatial object. Uncertain spatial region can be represented by object-cloud. Edge of spatial object can be represented by half-cloud-ring. So spatial uncertain region can be represented based on cloud model properly. Experiments testify that the method is both efficient and effective. It enriches the cloud theory, and proposes a new idea for representation of fuzzy object and image comprehending and analyzing, especially remote sensing image.
In 1999, the first Hartmann-Shack wave-front sensor for the human eye aberration measurement in China was established. The H-S sensor was successfully improved and applied to the clinic diagnosis. In this paper, the principle and the method of measuring wave aberrations of the human eye are given. The accuracy of the Hartman-Shack sensor is measured and analyzed. The measurement results of the wave-front aberrations of the real eyes using the sensor are demonstrated.
In 1980, the first laboratory on Adaptive Optics in China was established in Institute of Optics and Electronics, Chinese Academy of Sciences. Several adaptive optical systems had been set up and applied in Inertial Confinement Fusion (ICF) and retinal high-resolution imaging. In 1985, the first adaptive optical system for ICF equipment was set up in the world. Another 45 element adaptive optical system was first built for correcting the static and dynamic wavefront aberrations existed in the large-aperture Nd: glass laser for inertial confinement fusion in 2001. Two set adaptive optical system with 19-element and 37-element deformable mirror had been developed for human retina imaging in 2000 and 2002 respectively. In this paper, the function and performance of these adaptive optical systems are described and the experiment results are presented.
Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. Clustering algorithms typically use the Euclidean distance. In the real world, there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. In this paper, we study the problem of clustering in the presence of obstacles and propose spatial clustering by Voronoi distance in Voronoi diagram (Thiessen polygon). Voronoi diagram has lateral spatial adjacency character. Based on it, we can express the spatial lateral adjacency relation conveniently and solve the problem derived from spatial clustering in the presence of obstacles. The method has three steps. First, building the Voronoi diagram in the presence of obstacles. Second, defining the Voronoi distance. Based on Voronoi diagram, we propose the Voronoi distance. Giving two spatial objects, Pi and Pj, The Voronoi distance is defined that the minimum object Voronoi regions number between Pi and Pj in the Voronoi diagram. Third, we propose Following-Obstacle-Algorithm (FOA). FOA includes three steps: the initializing step, the querying step and the pruning step. By FOA, we can get the Voronoi distance between any two objects. By Voronoi diagram and the FOA, the spatial clustering in the presence of obstacles can be accomplished conveniently, and more precisely. We conduct various performance studies to show that the method is both efficient and effective.
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