Snakes, or active contours, are used extensively in computer vision and image processing application, particularly to
locate object boundaries. GVF (Gradient Vector Flow) model has resolved two key problems of the traditional
deformable model. However, it still requires both the initial contour being close to the target and a large amount of
computation. And it is difficult to process the cupped target edge. This paper analysis the characteristics of deformable
model firstly, then proposed a new method based on B-spline lifting wavelet. Experimentations based on GVF model and
MRI segmentation show that the proposed method is a good resolution to the initialization sensitivity and the large
Level set methods provide powerful numerical techniques for analyzing and solving interface evolution problems based on
partial differential equations. Level sets display interesting elastic behaviors and can handle topological changes. Although
level set methods have many advantages, they still often face difficult challenges such as poor image contrast, noise, and
missing or diffuse boundaries. The robust level set method of this paper is based on the anisotropic diffusion method. The
fast marching method provides a fast implementation for level set methods, the anisotropic diffusion is allowed to better
control the amount of smoothing effect and this process can get both noise smoothing and edge enhancement at the same
time. Experimental results indicate that the method can greatly reduce the noise without distorting the image and made the
level set methods more robust and accurate.
In this paper, we present a new image segmentation algorithm based on the concept of two-dimensional Renyi's entropy
along with statistical variance from the assumed data sets of object and the background to produce the appropriate
threshold. So the statistic infonnation, or relative spatial distribution, or co-occurrence, of pixel grey levels, was taken
into account. Experimental results show that the method we proposed performed better than one-dimensional and
two-dimensional entropy-based methods with lower segmentation errors, and a reduction in the amount of noise present
in the resultant images. This method can be extended to any other entropy segmentation method based on
two-dimensional gray histogram and may also be useful for pattern recognition and image sequence analysis. Especially
when the gray value of the object and the background overlap greatly or there is big noises in the image, the
segmentation result can be drastically improved.
This paper discusses the principle and implementation method of Ray-casting volume rendering algorithm. In order to
enhance the image quality and speed of alternate operation, we improve the grads formula in Ray-casting volume
rendering algorithm and compound method of the sampling points.
It is difficult to segment and detect the boundary of cloud images because of the complicated and various shapes and blurry edges of cloud. In this paper, we present an idea and a set of realizable design about self-adaptive segmentation by means of mathematical morphology firstly. Then, we proposed a boundary detection method base on wavelet transform. Some practices show that the segmentation models are self-adaptive, the whole processing system is general and the algorithm is high efficient in the processing of infrared satellite cloud image.
Although the snake model has been widely used nowadays and obtained quite good results, there are still some key difficulties with it: the narrow capture range and the disability to move into boundary concavities. A new snake model, Gradient Vector Flow snake, can overcome this difficulty. GVF snake model creates its own external force field called GVF force field, this make it insensitive to the initialization and able to move into concave boundary regions. However, GVF snake need large amount of computation and is easily interfered by noise. Accordingly, the wavelet-based GVF snake model can lessen the amount of computation because the multi-scale character of wavelet transform. Due to the different singularities of signal and noise, the module local maxima of their wavelet coefficients vary in different way in multi resolution, so noise can also be distinguished from signal with wavelet-based GVF snake model. The wavelet-based GVF snake model is more quickly and robust contrast to traditional snake model.
An automatic locating algorithm is presented for typhoon center locating using cloud motion wind vectors derived from
the satellite cloud images. The cloud motion wind vectors are obtained by implementing template matching to a pair of
interrelated satellite cloud images with stated time interval. The template matching is a process to find the child image
that corresponds to the given pattern image in an unknown pattern image. Three matching algorithms are compared.
Namely, the absolute difference matching algorithm, the sequential similarity detection algorithm and the infrared
cross-correlation coefficients matching algorithm. The third one is selected to acquire the set of cloud motion wind
vectors duo to its desirable vector results. Aiming at the specific typhoon cloud image, two simplifications are processed
in the course of acquiring the cloud motion wind vectors. According to meteorological analysis, typhoon center motion
has two important characteristics: (1) The translation in the central area is great while the spin is feeble. (2) The center
moving direction is compatible to that of the whole typhoon clouds. According to these characteristics, the algorithm for
automatically locating the typhoon center can be depicted as follows: firstly pick up the vectors that compatible to the
whole typhoon cloud motion vectors in the cloud motion wind vectors image, then find out the thickest area of the
satisfied vectors, lastly process the thickest area with mathematical morphology until there exists only one pixel point.
The locating result shows that the thought in the paper is good and can be a promising application in the typhoon center
A new method of minimal fuzzy entropy segmentation is introduced. It adopts a new membership function for the consistency and concentricity in the object and its background. A new 2D fuzzy entropy thresholding method is also developed, which is based on 2D gray historgram. The gray values of every pixel and its neighboring region are used in this 2D method. The experimental results show that the minimal fuzzy entropy method is very useful in the segmentation of some images and the 2D method has a good performance of resisting noise and good robustness. The segmentatiaon of using 2D is much better than 1D for most images, and the new method can be easily extended to other 1D entropy imaging thresholding.
Typhoon can be classified into two classes: eyed typhoon and non-eyed typhoon. The center of a non-eyed typhoon with good circularity is the geometric center of the cloud system and the center of a non-eyed typhoon with bad circularity can be located in the high grayness value area near the side of the greater grayness gradient sector. A new mathematical morphology-based algorithm is proposed to automatically achieve the center location of a non-eyed typhoon. Multispectral image fusion of infrared spectrum and water vapor spectrum is used to verify the result of locating the typhoon center. For a given infrared satellite cloud image, the locating steps are as followed: a) noises removing, b) main cloud systems segmenting, c) center locating and d) multispectral image fusion verification. The experimental results show that the algorithm locates the centers of most non-eyed typhoons successfully.
The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2is described. Study of the result shows that this is a highly efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.
It is difficult to segment cloud images because of the complicated and various shapes and blurry edges of cloud. In this paper, we present an idea and a set of realizable design about self-adaptive segmentation by means of mathematical morphology. Created models can show some characteristics of clouds such as shapes, scales and temperature exactly. Some practices show that the segmentation models are self-adaptive, the program is general and the algorithm is high efficient with the parallel operation.