Obstacle identification is one of the critical technologies of unmanned vehicle, edge detection is the basic step of obstacle identification based on video sensor and the magnitude guarantee of identification effect. In order to meet the demand of accuracy, real-time and stability of obstacle identification, a new multiple order morphology edge detection algorithm is proposed. We adopt two-dimensional histogram oblique segmentation to locate edge, then detected edge by improving the existing mathematical morphology edge detection operators and using appropriate structuring elements and percentile. Experimental results showed the edge detected is exquisite, continuous and intact. The algorithm possesses good robustness for different noised images, cuts operation time by nearly half compared with algorithm without edge location, then makes a good foundation for subsequent processing of obstacle identification.
In today’s industry, automatic detection of geologic features such as faults and channels is a challenging problem when the quality of data is not good. Edge detection filters are generally applied for the purpose of locating such features. Until now, edge detection has been carried out on rectangularly sampled 3D seismic data. The computational cost of edge detection can be reduced by exploring other sampling approaches instead of the regular rectangular sampling commonly used. Hexagonal sampling is an alternative to rectangular sampling that requires 13.4% less samples for the same level of accuracy. The hexagonal approach is an efficient method of sampling with greater symmetry compared with the rectangular approach. Spiral architecture can be used to handle the hexagonally sampled seismic data. Spiral architecture is an attractive scheme for handling 2D images that enables processing 2D data as 1D data in addition to the inherent hexagonal sampling advantages. Thus, the savings in number of samples, greater symmetry, and efficient data handling capability makes hexagonal sampling an ideal choice for computationally exhaustive operations. For the first time to our knowledge, we have made an attempt to detect edges in hexagonally sampled seismic data using spiral architecture. We compared edge detection on rectangular and hexagonally sampled seismic data using 2D and 3D filters in rectangular and hexagonal domains. We determined that hexagonal processing results in exceptional computational savings, when compared with its rectangular processing counterpart.
This paper introduces a design of gaussian Laplace edge detection algorithm model based on system generator which can be realized in FPGA.The data of a two- dimensional image was changed into a one-dimensional array，before line buffering in two Dual port RAM,the convolution of the image pixel data and the LOG template was carried out in the modules constituted of the component elements such as AddSub, Shift and Delay . After getting the absolute value with the modules of Slice，Negate and Mux ，the output was the image after edge-detection .The module function and the selecting principle was analyzed from the point of view of saving FPGA resources.The WaveScope and resource estimator showed that ：not only the detection result and the running speed was guaranteed but also the FPGA resources can be saved .
New edge detection algorithm facilitates two-dimensional echocardiographic on-line analysis of left ventricular (LV) performance