For a lot of applications, and particularly for medical intra-operative applications, the exploration of and navigation through 3-D image data provided by sensors like ToF (Time-of-Flight)

In this technical note we present a system that uses time-of-flight (ToF) technology to acquire a real-time multidimensional respiratory signal from a 3D surface reconstruction of the patient's chest and abdomen without the use of markers. Using ToF sensors it is feasible to acquire a 3D model in real time with a single sensor. An advantage of ToF sensors is that their high lateral resolution makes it possible to define multiple regions of interest to compute an anatomy-adaptive multidimensional respiratory signal. We evaluated the new approach by comparing a ToF based respiratory signal with the signal acquired by a commercially available external respiratory gating system and achieved an average correlation coefficient of 0.88.

Abstract. This paper describes the first accomplishment of the Timeof-Flight (ToF) measurement principle via endoscope optics. The applicability of the approach is verified by in-vitro experiments. Off-the-shelf ToF camera sensors enable the per-pixel, on-chip, real-time, marker-less acquisition of distance information. The transfer of the emerging ToF measurement technique to endoscope optics is the basis for a new generation of ToF rigid or flexible 3-D endoscopes. No modification of the endoscope optic itself is necessary as only an enhancement of illumination unit and image sensors is necessary. The major contribution of this paper is threefold: First, the accomplishment of the ToF measurement principle via endoscope optics; second, the development and validation of a complete calibration and post-processing routine; third, accomplishment of extensive in-vitro experiments. Currently, a depth measurement precision of 0.89 mm at 20 fps with 3072 3-D points is achieved.

This paper presents a new approach for gesture classification using x-and y-projections of the image and optional depth features. The system uses a 3-D time-of-flight (TOF) sensor which has the big advantage of simplifying hand segmentation. For the presented system, a Photonic-Mixer-Device (PMD) camera with a resolution of 160 × 120 pixels and a frame rate of 15 frames per second is used. The goal of our system is to recognise 12 different static hand gestures. The x-and y-projections and the depth features of the captured image are good enough to use a simple nearest neighbour classifier, resulting in a fast classification. To evaluate the system, a set of 408 images is recorded, 12 gestures from 34 persons. With a 'Leave-One-Out' evaluation, the recognition rate of the system is 94.61 %, and classification time is about 30 ms on a standard PC.

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