An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. Af...An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.展开更多
In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time con...In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example.展开更多
In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by comb...In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.展开更多
文摘An integrated novel method of recognizing huge target is described that combines some relatively mature image processing techniques such as edge detection, thresholding, morphology, image segmentation and so forth. After thresholding the edge image obtained by using Sobel operator, erosion is firstly used to reduce noise and extrusive pixels; then dilation is used to expand some separated pixels into various regions, after that the image segmentation technique is utilized to distinguish the target region with a criterion. The location of the target is also offered. Each technique adopted herein seems not complicated at all, the experimental results demonstrate the efficiency of the combination of these techniques. It is its high computational speed and remarkable robustness resulting from its simplicity that make the method promise to be applied in practical problems requiring real time processing.
文摘In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example.
基金Supported by the National Natural Science Foundation of China (No.40071061).
文摘In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.
