This paper describes a practical and reliable image analysis and tracking algorithm to achieve automated instrument localization and scope maneuvering in robotically assisted laparoscopic surgery. Laparoscopy is a minimally invasive surgical procedure that utilizes multiple small incisions on the patient's body through which the surgeon inserts tools and a videoscope in order to conduct an operation. The scope relays images of internal organs to a camera, and the images are displayed on a video screen. The surgeon performs the operation by viewing the scope images rather than performing the traditional “open” procedure, where a large incision is made on the patient's body for direct viewing. The current mode of laparoscopy employs an assistant to hold the scope and position it in response to the surgeon's verbal commands. However, this results in suboptimal visual feedback, because the scope is often aimed incorrectly and vibrates due to hand trembling. We have developed a robotic laparoscope positioner to replace the assistant. The surgeon commands the robotic positioner through a hand/foot controller interface. To further simplify the human‐machine interface that controls the robotic scope positioner, we report here a novel scope‐positioning scheme using automated image analysis and robotic visual servoing. The scheme enables the surgeon to control visual feedback and to perform surgery more efficiently without requiring additional use of the hands. J Image Guid Surg 1:308–325 (1995). © 1996 Wiley‐Liss, Inc.

In this paper, we introduce a new algorithm for inferring the structure of three-dimensional (3-D) objects from multiple viewing directions using an integration of active and passive sensing. Construction of the structural description of a 3-D object is composed of two stages: i) the surface orientation and partial structure are first inferred from a set of single views, and ii) the visible surface structures inferred from different viewpoints are integrated to complete the description of the 3-D object. In the first stage, an active stripe coding technique is used for recovering visible surface orientation and partial structure. In the second stage, an iterative constructionhefinement scheme is used which exploits both passive and active sensing for representing the object surfaces. The active sensing technique projects spatially modulated light patterns to encode the object surfaces for analysis. The visible surface orientation is inferred using a constraint satisfaction process based upon the observed orientation of the projected patterns. The visible surface structure is recovered by integrating a dense orientation map. For multiple view integration, the bounding volume description of the imaged object is first constructed using multiple occluding contours which are acquired through passive sensing. The bounding volume description is then refined using the partial surface structures inferred from active sensing. The final surface structure is recorded in a data structure where the surface contours in a set of parallel planar cross sections are stored. The system construction is inexpensive and the algorithms introduced are adaptive, versatile, and suitable for applications in dynamic environments. We expect this approach to be widely applicable in the field of robotics, geometric modeling, and factory automation.

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