Tomohito Masuda

Flying Laser Range Sensor for Large-Scale Site-Modeling and Its Applications in Bayon Digital Archival Project

Abstract We have been conducting a project to digitize the Bayon temple, located at the center of Angkor-Thom in the kingdom of Cambodia. This is a huge structure, more than 150 meters long on all sides and up to 45 meters high. Digitizing such a large-scale object in fine detail requires developing new types of sensors for obtaining data of various kinds related to irregular positions such as the very high parts of the structure occluded from the ground. In this article, we present a sensing system with a moving platform, referred to as the Flying Laser Range Sensor (FLRS), for obtaining data related to these high structures from above them. The FLRS, suspended beneath a balloon, can be maneuvered freely in the sky and can measure structures invisible from the ground. The obtained data, however, has some distortion due to the movement of the sensor during the scanning process. In order to remedy this issue, we have developed several new rectification algorithms for the FLRS. One method is an extension of the 3D alignment algorithm to estimate not only rotation and translation but also motion parameters. This algorithm compares range data of overlapping regions from ground-based sensors and our FLRS. Another method accurately estimates the FLRS’s position by combining range data and image sequences from a video camera mounted on the FLRS. We evaluate these algorithms using a IS-based method and verify that both methods achieve much higher accuracy than previous methods.

Effective nearest neighbor search for aligning and merging range images

We describe a novel method, which extends the search algorithm of a k-d tree for aligning and merging range images. If the nearest neighbor point is far from a query, many of the leaf nodes must be examined during the search, which actually will not finish in logarithmic time. However, such a distant point is not as important as the nearest neighbor in many applications, such as aligning and merging range images; the reason for this is either because it is not consequently used or because its weight becomes very small. Thus, we propose a new algorithm that does not search strictly by pruning branches if the nearest neighbor point lies beyond a certain threshold. We call the technique the bounds-overlap-threshold (BOT) test. The BOT test can be applied without recreating the k-d tree if the threshold value changes. Then, we describe how we applied our new method to three applications in order to analyze its performance. Finally, we discuss the methods effectiveness.

Simultaneous determination of registration and deformation parameters among 3D range images

Conventional registration algorithms are mostly concerned with rigid-body transformation parameters between a pair of 3D range images. Our proposed framework aims to determine, in a unified manner, not only such rigid transformation parameters but also various deformation parameters, assuming that the deformation we handle here is strictly defined by some parameterized formulation derived from the deformation mechanism. While conventional registration algorithms usually calculate six parameters (three translation and three rotation parameters), our proposed algorithm estimates deformation parameters as well. In this paper, we describe how we formulated such an algorithm, implemented it, and evaluated its performance.

Shape difference visualization for ancient bronze mirrors through 3D range images

Japanese archaeologists have paid special attention to ancient Chinese bronze mirrors because the mirrors may provide a key for the exact location of Yamatai State, which is one of the major archaeological controversies. Currently, archaeologists visually analyse ancient Chinese bronze mirrors for their shape difference. The practice requires a huge amount of time and effort. In this paper, we propose an automatic method for detecting the shape difference between a pair of ancient mirrors. The 3D data of the mirrors are obtained using a laser range scanner. Our algorithm then aligns them into the same coordinate and visualizes their shape differences. Our proposed algorithm provides fast and non-damaging analysis for shape difference. Further analysis can be evaluated on our data instead of the actual mirror, so it can be performed by more than one group of archaeologists. Copyright

Flying laser range finder and its data registration algorithm

Scanning from the air is one of the most efficient methods for obtaining 3D data of large-scale objects. For this purpose, we have been developing a flying laser range finder that is suspended under a balloon. Even though the scanning speed of the finder is quite rapid, it is difficult to eliminate the influence of the swing of a balloon. As a result the scanned data have some distortion due to the intra-scanning movement. In order to compensate this intra-scanning movement, we propose a evolutional registration algorithm which not only aligns multiple range images to determine inter-scanning movement parameters, but also rectifies distortion of range image by determining intra-scanning movement parameters. In this paper, we describe our aerial scanning system especially focusing on the design of the flying laser range finder and deformation registration algorithm. To show the effectiveness of our method, we evaluate its performance using synthesized and real data.

Breed differentiation among Japanese native chickens by specific skull features determined by direct measurements and computer vision techniques

1. Inter-breed morphological comparisons were made among 11 breeds of Japanese native chickens (Gifujidori, Hinaidori, Shokoku, Totenko, Tomaru, Satsumadori, Shamo, Koshamo, Koeyoshi, Chabo and Nagoya), White Leghorn, broiler chickens (Chunky) and red junglefowl collected in the Philippines, based on results of direct measurements and analysis by computer vision techniques of the skull. 2. Analysis of direct measurements identified two groups of chicken: a small type that included the Chabo, Koshamo, red junglefowl, Gifujidori and Shokoku and a large type that included the remaining breeds studied. These groupings were made based on size determined both in the first (PC1) and second principal component (PC2). The greatest length of the cranium and condylobasal length greatly contributed to the morphological differences between these two groups. 3. Analysis by computer vision techniques, however, identified three groups: the Bantam group (which includes red junglefowl), Shokoku group and Shamo group. White Leghorn clustered within the Shokoku group while the broiler chicken belonged to the Shamo group. The region around the junction of the neural cranium and the visceral cranium contributed greatly to the morphological differences among breeds, both in the PC1 and PC2.