Pattern Detection in Airborne LiDAR Data Using Laplacian of Gaussian Filter

Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed.

High performance hardware architecture for depth measurement by using binocular-camera

High performance hardware architecture for depth measurement by using binocular-camera is proposed.In the system,at first,video streams of the target are captured by left and right charge-coupled device(CCD)cameras to obtain an image including the target.Then,two different images with two different view points are obtained,and they are used in calculating the position deviation of the image's pixels based on triangular measurement.Finally,the three-dimensional coordinate of the object is reconstructed.All the video data is processed by using field-programmable gate array(FPGA)in real-time.Hardware implementation speeds up the performance and reduces the power,thus,this hardware architecture can be applied in the portable environment.

Integrated phase unwrapping algorithm for the measurement of 3D shapes by Fourier transform profilometry

An integrated and reliable phase unwrapping algorithm is proposed based on residues and blocking-lines detection, closed contour extraction and quality map ordering for the measurement of 3D shapes by Fourier-transform profilometry (FTP). The proposed algorithm first detects the residues on the wrapped phase image, applies wavelet analysis to generate the blocking-lines that can just connect the residues of opposite polarity, then carries out the morphology operation to extract the closed contour of the shape, and finally uses the modulation intensity information and the Laplacian of Gaussian operation of the wrapped phase image as the quality map. The unwrapping process is completed from a region of high reliability to that of low reliability and the blocking-lines can prevent the phase error propagation effectively. Furthermore, by using the extracted closed contour to exclude the invalid areas from the phase unwrapping process, the algorithm becomes more efficient. The experiment shows the effec-tiveness of the new algorithm.

Fine edge detection in single-pixel imaging

Typical single-pixel imaging techniques for edge detection are mostly based on first-order differential edge detection operators.In this paper,we present a novel edge detection scheme combining Fourier single-pixel imaging with a second-order Laplacian of Gaussian(LoG)operator.This method utilizes the convolution results of an LoG operator and Fourier basis patterns as the modulated patterns to extract the edge detail of an unknown object without imaging it.The simulation and experimental results demonstrate that our scheme can ensure finer edge detail,especially under a noisy environment,and save half the processing time when compared with a traditional first-order Sobel operator.