Abstract: The assistance system for correcting vessel path comprises a receiver, a memory, at least one sensor, a processor and a display. The processor connects to the receiver, the memory and the at least one sensor, and the display is coupled to the processor. The assistance system based on the processor is able to be utilizing the preset machine-readable grid in determining the distance parameters of obstacles above water, and in which these distance parameters are corrected by using the sensor measurement data acquired by the at least one sensor. Thereby the assistance system could finish the distance measurements of abovementioned obstacles without using Lidar, and even in the case of high-undulating water surface.

Abstract: The assistance system for correcting vessel path comprises a receiver, a memory, at least one sensor, a processor and a display. The processor connects to the receiver, the memory and the at least one sensor, and the display is coupled to the processor. The assistance system based on the processor is able to be utilizing the preset machine-readable grid in determining the distance parameters of obstacles above water, and in which these distance parameters are corrected by using the sensor measurement data acquired by the at least one sensor. Thereby the assistance system could finish the distance measurements of abovementioned obstacles without using Lidar, and even in the case of high-undulating water surface.

Abstract: Disclosed herein is a method for auto-depicting trends in object contours (referred to as ADTOC). At the heart of ADTOC is a sifting process to determine a significant angular value via evaluating a plurality of angular values in a predefined range. ADTOC is characterized in that a probe-ahead concept is applied to obtain a reference angular value along the current route, and then the probed angular value is used to modify the significant angular value in order to timely correct the subsequent trace direction, thus achieving more accurate trace result. Contours with discontinuous segments caused by noise, obstacles, illumination, shading variations, etc. can also be auto-depicted without requiring a predefined auxiliary route.

Abstract: Disclosed herein is a method for auto-depicting trends in object contours (referred to as ADTOC). At the heart of ADTOC is a sifting process to determine a significant angular value via evaluating a plurality of angular values in a predefined range. ADTOC is characterized in that a probe-ahead concept is applied to obtain a reference angular value along the current route, and then the probed angular value is used to modify the significant angular value in order to timely correct the subsequent trace direction, thus achieving more accurate trace result. Contours with discontinuous segments caused by noise, obstacles, illumination, shading variations, etc. can also be auto-depicted without requiring a predefined auxiliary route.

Abstract: A method for fast locating a decipherable pattern in an input image, which is characterized in utilizing an overly downscaled binary image to not only reduce computation time but also facilitate extraction of skeletons for fast and accurately locating pattern, is disclosed. First, a pre-process is applied to an input image to acquire a binary image downscaled n times, from which at least one skeleton corresponding to a decipherable pattern is extracted. Coordinate values of at least one pixel of each skeleton are respectively enlarged n1/2 times and used as the central points on the original image plane for establishing a plurality of detecting blocks with the identical size. Subsequently, a grading mechanism is employed to determine the corresponding detecting blocks of the decipherable pattern.

Abstract: A method for fast locating a decipherable pattern in an input image, which is characterized in utilizing an overly downscaled binary image to not only reduce computation time but also facilitate extraction of skeletons for fast and accurately locating pattern, is disclosed. First, a pre-process is applied to an input image to acquire a binary image downscaled n times, from which at least one skeleton corresponding to a decipherable pattern is extracted. Coordinate values of at least one pixel of each skeleton are respectively enlarged n1/2 times and used as the central points on the original image plane for establishing a plurality of detecting blocks with the identical size. Subsequently, a grading mechanism is employed to determine the corresponding detecting blocks of the decipherable pattern.