Name
Title | ||
|---|---|---|
Detection And Repair Of Defects In Range-And-Color Image Data Observed With A Laser Range Scanner |
Abstract | ||
|---|---|---|
Some types of laser range scanner can measure range and color data simultaneously, and are often used to acquire 31) structure of outdoor scenery. However, unfortunately a laser range scanner cannot give us perfect range information about target objects such as buildings, and various factors incur critical defects of range data. We present a defect detection scheme based on the region segmentation using observed range-and-color image data, and apply a nonlinear time-evolution method to the repair of defect regions of range data. As to the defect detection, performing the range-and-color segmentation, we divide observed data into several regions corresponding to buildings, the sky, the ground and so on. Using the segmentation results, we determine defect regions. Given defect regions, their range data will be repaired from observed data in their neighborhoods. For that purpose, reforming the transportation-based inpainting algorithm, previously developed for the defect repair of an intensity image by Bertalmio and others, for the defect repair of range data, we construct a new defect-repair method that applies the interleaved sequential updates, composed of the transportation-based inpainting and the data projection onto the original viewing direction of each sampling point of range data, to 3D point data converted from observed range data. The performance evaluations using artificially damaged test range data and really observed range data demonstrate that our repair method outperforms the existing repair methods both in quantitative performance and in subjective repair quality. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1117/12.501480 | VISUAL COMMUNICATIONS AND IMAGE PROCESSING 2003, PTS 1-3 |
Keywords | Field | DocType |
range image, range-and-color segmentation, region competition, defect detection, defect repair, digital inpainting, nonlinear time-evolution, Riemannian metric | Computer vision,Segmentation,Computer science,Laser,Inpainting,Data projection,Artificial intelligence,Scanner,Sampling (statistics),Color image | Conference |
Volume | ISSN | Citations |
5150 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takahiro Saito | 1 | 100 | 30.46 |
| Takashi Komatsu | 2 | 113 | 33.96 |
| Shin-ichi Sunaga | 3 | 0 | 0.68 |
| Masayuki Hashiguchi | 4 | 0 | 0.34 |