Gregory D. Hager – författare

Find the design principles you need to move vision-based control out of the lab and into the real world. In this edited collection of state-of-the-art papers, contributors highly regarded in robust vision bring you the latest applications in the field. Whatever your industry - from space ventures to mobile surveillance - you will discover throughout this comprehensive collection a strong emphasis on robust vision simply unmatched today. You will also gain an in-depth analysis of vision techniques used to control the motion of robots and machines. Expert contributors offer you key insights into: Current control issues including hardware design, system architecture, sensor data fusion, and visual trackingModeling methods for vision-based sensingUseful summaries of recent conclusions drawn from robust-vision workshopsFuture research needsIf you want to learn today's approaches to robust vision-based control of motion, this extensive collection is a must. Learn from the experts and, in the process, speed your project development and broaden your technical expertise for future collaborative efforts in your industry.

If you have ever hiked up a steep hill to reach a viewpoint, you will know that sensing can involve the expenditure of effort. More generally, the choice of which movement an intelligent system chooses to make is usually based on information gleaned from sensors. But the information required to make the motion decision may not be immediately to hand, so the system . first has to plan a motion whose purpose is to acquire the needed sensor information. Again, this conforms to our everyday experience: I am in the woods and don't know which direction to go, so I climb up to the ridge to get my bearings; I am lost in a new town, so I plan to drive to the next junction where there is sure to be a roadsign, failing that I will ask someone who seems to be from the locality. Why, if experiences such as these are so familiar, has the problem only recently been recognised and studied in Robotics? One reason is that until quite recently Robotics research was dominated by work on robot arms with limited reach and fixed in a workcell.

If you have ever hiked up a steep hill to reach a viewpoint, you will know that sensing can involve the expenditure of effort. More generally, the choice of which movement an intelligent system chooses to make is usually based on information gleaned from sensors. But the information required to make the motion decision may not be immediately to hand, so the system . first has to plan a motion whose purpose is to acquire the needed sensor information. Again, this conforms to our everyday experience: I am in the woods and don't know which direction to go, so I climb up to the ridge to get my bearings; I am lost in a new town, so I plan to drive to the next junction where there is sure to be a roadsign, failing that I will ask someone who seems to be from the locality. Why, if experiences such as these are so familiar, has the problem only recently been recognised and studied in Robotics? One reason is that until quite recently Robotics research was dominated by work on robot arms with limited reach and fixed in a workcell.

The collection of articles presented here emerged from the Block Island Workshop on Vision and Control, which brought together researchers from around the world. It records discussions of theoretical developments and empirical validation of techniques and applications, providing an overview of thinking in vision and control, as well as an introduction to many of the techniques used in this area.