[...]... Evaluation of Video Scenes 278 9.5 High-precision Visual Perception 290 9.5.1 Edge Feature Extraction to Subpixel Accuracy for Tracking 290 9.5.2 Handling the Aperture Problem in Edge Perception 292 10 Perception of Crossroads 297 10.1 General Introduction 297 10.1.1 Geometry of Crossings and Types of Vision Systems Required 298 10.1.2 Phases of Crossroad Perception and. .. understandable and motion continuity persists Also, it is in this space that inertial signals have to be interpreted and that integrals of accelerations yield 3-D velocity components; integrals of these velocities yield the corresponding positions and angular orientations for the rotational degrees of freedom Therefore, for visual dynamic scene understanding, images are considered intermediate carriers of. .. follow lanes and do convoy driving, perform lane changes, pass other vehicles, turn off onto a crossroad or slow down for parking All of the maneuvers mentioned are well known to human drivers, and they recognize the intention of performing one of those by its typical onset of motion over a short period of time For example, a car leaving the center of its lane and moving consistently toward 8 1 Introduction... (hundreds or thousands of km) The design of the vision system should be selected depending on the task at hand (see next section) 1.5 What Type of Vision System Is Most Adequate? For motion control, due to inertia of a body, the actual velocity vector determines where to look to avoid collisions with other objects Since lateral control may be applied to some extent and since other objects and subjects may... European standard), for frame grabbing and communication of the (huge amount of) image data, as well as for feature extraction, hypothesis generation, and state estimation Usually, three to five video cycles, that are 100 to 200 ms, will have passed until a control output 1.4 What are Appropriate Interpretation Spaces? 5 derived from vision will hit the real world For precise control of highly dynamic. .. well-known observer techniques in systems dynamics (Kalman filter and derivatives, Luenberger observers) These recursive methods reconstruct the time derivatives of state variables by prediction error feedback and knowledge about the dynamic behavior of the object and (for the Kalman filter) of the statistical properties of the system (dubbed “plant” in systems dynamics) and of the measurement processes The... between inertial and optical sensor data evaluation for handling this case; this will be discussed to some detail and applied to technical vision systems in several chapters of the book Also from biologists stems the differentiation of vision systems into “prey” and “predator” systems The former strive to cover a large simultaneous field of view for detecting predators sufficiently early and approaching... dynamic vision and provide the basic knowledge representation schemes underlying the approach developed Active subjects with capabilities for perception and control of behaviors are at the core of this unconventional approach Chapter 2 will deal with methods for describing models of objects and processes in the real world Homogeneous coordinates as the basic tool for representing 3-D space and perspective... first Perspective mapping and its inversion are discussed next Then, spatiotemporal embedding for circumnavigation of the inversion problems is treated Dynamic models and integration of information over time are discussed as a general tool for representing the evolution of processes observed A distinction between objects and subjects is made for forming (super-) classes The former (treated in Chapter... simple motion laws, in general Subjects (treated in Chapter 3) have the capability of sensing information about the environment and of initiating motion 1.5 What Type of Vision System Is Most Adequate? 19 on their own by associating data from sensors with background knowledge stored internally Chapter 4 displays several different kinds of knowledge components useful for mission performance and for behavioral . y0 w1 h1" alt="" Dynamic Vision for Perception and Control of Motion Ernst D. Dickmanns Dynamic Vision for Perception and Control of Motion 123 Ernst D in Publication Data Dickmanns, Ernst Dieter Dynamic vision for perception and control of motion 1. Compu ter vision - Industrial applic ations 2. Optical detect