The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectiv...The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivitydetection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.展开更多
This paper's main contributions are three-fold. Firstly, it is shown that the two existing template matching-like definitions of the Hough transform in the literature areinadequate. Secondly, an inherent probabili...This paper's main contributions are three-fold. Firstly, it is shown that the two existing template matching-like definitions of the Hough transform in the literature areinadequate. Secondly, an inherent probabilistic aspect of the Hough transform embedded in the transformation process from image space to parameter space is clarified.Thirdly, a new definition of the Hough transform is proposed which takes into account both the intersection scheme between the mapping curve (or mapping surface) and accumulator cells and the inherent probabilistic characteristics.展开更多
文摘The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivitydetection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.
文摘This paper's main contributions are three-fold. Firstly, it is shown that the two existing template matching-like definitions of the Hough transform in the literature areinadequate. Secondly, an inherent probabilistic aspect of the Hough transform embedded in the transformation process from image space to parameter space is clarified.Thirdly, a new definition of the Hough transform is proposed which takes into account both the intersection scheme between the mapping curve (or mapping surface) and accumulator cells and the inherent probabilistic characteristics.
