Research On Feature Extraction And Target Identification In Machine Vision Underwater And Surface Image

Machine vision is used for visual monitoring, precision positioning and non-contact measurement of the observed objects in ocean engineering. With the further advance of ocean research and the national security needs, the intelligent underwater detector has been widely used as an important part of ocean engineering high-tech research. The actions such as object location, identification, barrier-avoiding and path planning are often taken by the smart underwater detector in the underwater surroundings, so complexities and uncertainties of working environments make the vision system of the detector stand out especially, further upgrading the performance of machine vision system for underwater research is not only a challenging task, but also has important theoretical significance and practical value.The extraction of the features is the basic technique of the image processing. According to the need of the matching and object recognition and image synthesis, the method of feature detection can detect the different features of corners and edges and precision of the characteristics location are relatively good. This dissertation deals with the following key issues in machine vision underwater and surface image feature extraction and matching techniques research.(1) Underwater image segmentation based on an ant colony optimization algorithm. Image segmentation is the basis of the image understanding and recognition, and the segmentation quality can directly affect on the results of the subsequent image processing. However, as far as the underwater image is concerned, the fuzzy and non-additive information of the image make the traditional method of image segmentation hardly meet the requirements. So a method of image segmentation based on the intelligent ant colony algorithm is designed in this paper, each pixel of an image is classified by analyzing the nature of image segmentation, and ultimately achieving the purpose of image segmentation. In the classification process, the basic ant colony algorithm has been improved by adopting the concepts of the entropy and clustering method, which making the underwater image segmentation program with self-adaptability, robustness, parallelism and fast convergence and so on.(2) The detection of underwater image based on the empirical mode decomposition algorithm and the phase information. Two-dimensional empirical mode decomposition algorithm can achieve multi-scale image structure analysis, and deal with some problems of image such as the image fusion and noise reduction and feature extraction and the image compression and so on; in addition, the phase information is one of the most stable and important features of an image. Therefore, the EP model is proposed based on the feature detection for underwater image analysis by analyzing and synthesizing the empirical mode decomposition method and the phase information. This model is fully inherited the advantages of the two methods, and can be used for underwater image processing and analysis. The multi-scale and multi-pixel edge detection is achieved for an underwater image. And the positioning accuracy of the matching target is also improved. The multi-scale image segmentation can be completed based on this model.(3) The underwater image matching technology based on the scale-invariant feature detection. Feature points matching algorithm is sensitive to the image scale change. In order to overcome this problem, an improved SIFT-based image registration scheme is proposed. The improved registration strategy can solve the above problem by using the rotation and scaling invariant property of the SIFT feature points as well as its robustness to added noise, illumination change and viewpoint change, and taking into account underwater environment with the low-light characteristic and different experimental situations. The proposed algorithm is effective in improving the accuracy and the speed of the image matching, as well as solves the scale changes to the influence of image registration, which makes it possible to achieve successful underwater image registration and mosaic when large scale change occurs.(4) The problem of ship wakes identify based on the image texture feature. In general, many features of the ship wakes relate to the information of the ship’s hull, geometric scale, the host’s position in the boat, the propeller geometry parameter and its working conditions and other factors. In addition, the ship’s speed and the navigation direction, as well as the information of the salinity, temperature, and density of sea water are also relevant with the ship wakes. In a word, the ship wake is widely studied in the ship design, marine environment monitoring and remote sensing areas, as well as in naval air force reconnaissance. It plays important roles in actual practice and military. This paper, taking a ship wakes image as an object of research, using the local binary patterns and the GLCM method to detect the natural texture and the statistical characteristics of the symbiotic as the input vectors of the BP neural network, establishes an automatic identification system for the ship wakes image. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over80%.