Rided-2d: A Rule-Based Instantaneous Denoising and Edge Detection Method for 2d Range Scan Line

Abstract

Edge detection is often regarded as a basic step in range image processing by virtue of its crucial effect. The majority of existing edge detection methods cannot satisfy the requirement of efficiency in many industrial applications due to huge computational costs. In this paper, a novel instantaneous method, named RIDED-2D is proposed for denoising and edge detection for 2D scan line in range images. In the method, silhouettes of 2D scan line are classified into eight types by defining a few new coefficients. Several discriminant criteria on large noise filtering and edge detection are stipulated based on qualitative feature analysis on each type. Selecting some feature point candidates, a practical parameter learning method is provided to determine the threshold set, along with the implementation of an integrated algorithm by merging calculation steps. Because all the coefficients are established based on distances among the points or their ratio, RIDED-2D is inherently invariant to translation and rotation transformations. Furthermore, a forbidden region approach is proposed to eliminate interference of the mixed pixels. Key performances of RIDED-2D are evaluated in detail by including computational complexity, time expenditure, accuracy and stability. The results indicate that RIDED-2D can detect edge points accurately from several real range images, in which large noises and systematic noises are involved, and the total processing time is less than 0.1 millisecond on an ordinary PC platform using the integrated algorithm. Comparing with other state-of-the-art edge detection methods qualitatively, RIDED-2D exhibits a prominent advantage on computational efficiency. Thus, the proposed method qualifies for real-time processing in stringent industrial applications. Besides, another contribution of this paper is to introduce CPU clock counting technique to evaluate the performance of the proposed algorithm, and suggest a convenient and objective way to estimate the algorithm's time expenditure in other platforms.

Variable Window for Outlier Detection and Impulsive Noise Recognition in Range Images

“…range/angular resolution of the range finder and relative height of the thin bulge etc. However, for the sake of high adaptability, it is recommended to determine these thresholds by parameter learning from selected target point candidates [21]. Basic principle of this parameter learning method is a refinement process of reverse computation on the thresholds of several selected feature point candidates.…”

An Enhanced Block Based Edge Detection Technique Using Hysteresis Thresholding

“…A novel technique for edge detection from digital images based on adjacent dispersion is proposed by Qindong Sun et al, in which adjacent dispersions are made use of for detecting edges [7]. A novel instantaneous technique for edge detection and denoising called Rided-2D, which is invariant to the geometric transformations, was proposed by Jian Wang et al [8]. A parameter-free real-time edge detection technique was proposed by Cuneyt Akinlar, which works based on the edge drawing algorithm and delivers only meaningful edge segments after execution [9].…”