On account of restriction of man-made and collection environment, the fingerprint image generally has low quality,
especially a contaminated background. In this paper, an enhancement algorithm based on edge filter and Gabor filter is
proposed to solve this kind of fingerprint image. Firstly, a gray-based algorithm is used to enhance the edge and segment
the image. Then, a multilevel block size method is used to extract the orientation field from segmented fingerprint image.
Finally, Gabor filter is used to fulfill the enhancement of the fingerprint image. The experiment results show that the
proposed enhancement algorithm is effective than the normal Gabor filter algorithm. The fingerprint image enhance by
our algorithm has better enhancement effect, so it is helpful for the subsequent research, such as classification, feature
exaction and identification.
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
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.
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.
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.
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.