This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

This paper presents a subpixel edge localization method based on the adaptive weighting of gradients (AWG). The method first finds the optimal power factors to be used by the AWG through an error propagation scheme. Then, with the goal of fast implementation of the method, a process that directly relates the edge width to the optimal powers is proposed. A method that uses the squared weighting of gradients (SWG) is also proposed, which applies a power of 2 to the gradients of an edge profile to calculate the edge location with subpixel accuracy. Next, an enhanced version of the AWG that combines the SWG and AWG selectively is proposed to obtain the best localization. At present, a fitting method based on the error function (Erf) is considered to be the most accurate method among current state-of-the-art methods, but it has a very high computational cost. The experimental results show that the proposed method is computationally much less expensive and more accurate than the Erf-fitting method.

This study proposes a systematic method to estimate edge displacement (ED) from ground truth edges. To obtain ground truth edges and test edges, a series of target sheets containing the reference regions and brightness contrast regions were designed. Further, the brightness contrast regions were designed to contain foreground and background brightness between which the image edges occur. By varying the foreground brightness (FB), the influence of the FB on the edge locations was tested. Moreover, the influence of camera‐to‐object distance on the edge locations was tested. A simple least‐squares method using slope profiles and a slope‐based weighting scheme is proposed for calculating the edges occurring in contrast regions with subpixel accuracy. The experiment results revealed that a bright foreground moves the edge location towards the dark side of the edges in the range of 0–0.6 pixels with strong relationships between the FB and magnitude of ED.

We propose a building boundary modeling method using rectangles to overcome the zigzag boundary shapes of rooftop fenced buildings that are inherent in light detection and ranging (LiDAR) data. The method first finds 3-D patches through building detection using a morphological opening filter and histogram mode analysis. Then, rectangles that encase the traced boundaries of the 3-D patches are adjusted adaptively based on traced boundary coordinates and perpendicular constraints among the sides of the rectangles. The boundary of each 3-D patch is remodeled by subtracting void region rectangles from the parent rectangle. The superstructures of rooftops are also modeled with perpendicularity and adaptive constraints.