This paper presents a new image enhancement method based on self-adaptive piecewise linear transformation. It is most important to select division points during the processing course of piecewise linear transformation because these division points can decide the quality and efficiency of image enhancement. In this paper, division points are automatically and self-adaptive selected based on the gray grade and histogram feature of images. In order to increase the contrast of the image, it is necessary to extend the object section, maintain the transition section and compress the background section. Our method is easy to use, and the experiment results show that the technique can produce good results on a variety of images.
The Hough transform is a robust tool to extract features, such as straight edges, circles, or ellipses from images and describe them parametrically. However, linear edge in real image is not an ideal line. Tiny orientation change commonly exists in linear edge obtained by edge detector. Standard Hough transform need to discrete Hough space, discreteness must lead to statistical error, which makes it difficult to extract the tiny, short and small line when detecting long line. It makes detecting failure of the tiny line or cannot contain all information of these small lines. In this paper, proposed improved Hough transform can eliminate the statistical error caused by discreteness. Consequently eliminate the bad effect of detecting short and tiny line caused by detecting long line, it make the long line and the short, tiny line can be detected precisely at the same time.
A novel scale and rotation invariant ship recognition method using log-polar mapping and two-dimensional principal
component analysis (2DPCA) is proposed. Log-polar mapping is very useful for eliminating the rotation and scale
effects. 2DPCA is used to extract ship feature from normalized log-polar image become scale and rotation invariant.
Experimental results show that the proposed method has improved recognition performance.
To build a suit of integrated ship model database, a new method of constructing ship three-dimensional models is
proposed which partition ship into basic structure and special structure of ship body. According to the distributing of ship
structure, segmenting the whole ship area, and then marking off the basic hierarchies of practice ship, finally
disassembling to basic unit structure, thus setting up corresponding tree hierarchies of ships. The modeling procedures of
build ship 3D model are presented, Simulation results of three-dimensional ship models have gained vivid solid effect.
This paper proposes a fast and robust algorithm for classification and recognition of ships based on the two-dimensional
Principal Component Analysis (2DPCA) method. The three-dimensional ship models achieve by modeling software of
MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The 2DPCA
method as against conventional PCA method for simulated ship recognition using training and testing experiments, as
the training and testing sample size is large, and there are great variations in different azimuth and elevation for ship
viewpoints. The experiment of ship recognition using the global feature of ships is not satisfied with us, so we proposed
an improved 2DPCA method based on the local feature of ships. Some recognition results from simulated data are
presented, it shows that the improved 2DPCA method outperform PCA in ship recognition and also superior to PCA in
terms of computational efficiency for feature extraction. So our method is more preferable for ship classification and
This paper proposes a global threshold selection method to do infrared image segmentation, which uses both gray-level distribution and spatial information, namely, two-dimensional OTSU method (2D OTSU). It often gets better anti-noise performance. What's more, taking consideration of the complexity of its computation, we introduce a new heuristic optimization algorithm, called the particle swarm optimization (PSO) algorithm to search the result. So an algorithm for PSO-based 2D Otsu segmentation is proposed. The experiments of segmentation the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.
This paper deals with image quality analysis considering the impact of psychological factors involved in assessment. The
attributes of image quality requirement were partitioned according to the visual perception characteristics and the
preference of image quality were obtained by the factor analysis method. The features of image quality which support
the subjective preference were identified, The adequacy of image is evidenced to be the top requirement issues to the
display image quality improvement. The approach will be beneficial to the research of the image quality subjective
quantitative assessment method.
This paper proposes a fast and robust algorithm for classification and recognition of ships based on the Principal Component Analysis (PCA) method. The three-dimensional ship models are achieved by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The PCA method as against the Back-Propagation (BP) neural network method for simulated ship recognition using training and testing experiments, we can see that there is a sharp contrast between them. Some recognition results from simulated data are presented, the correct recognition rate of PCA method improved rapidly for each of the five ship types than that of neural network method, the number of times a ship type is recognized as one of the other ships is reduced greatly.