Suresh B. Marapane

Region-based stereo analysis for robotic applications

The authors consider the development of a practical binocular stereo approach for extracting depth information. This approach is particularly well suited for robot vision systems designed for inspection and manipulation tasks. The authors emphasize the importance of semantic content and stability of the primitive used in stereo matching and introduce the use of homogeneous regions as features in stereo matching. Considering the lower number of features and the high discrimination power of the primitive, the region-based matcher is more efficient and accurate than those using edge-based primitives. A region-based matching technique can utilize both local and global information and thus yield a more globally consistent solution. However, region-based matching processes typically yield coarse disparity maps. It is noted that it is critical that an efficient and robust stereo system utilize the most appropriate set of primitives at each state of the process. A hierarchical stereo approach that does so is proposed. Several experiments to evaluate the performance of a region-based stereo matcher and a straightforward disparity and depth generation module are described. >

Multi-primitive hierarchical (MPH) stereo analysis

This paper develops and demonstrates a new computational framework for an accurate, robust, and efficient stereo approach. In multi-primitive hierarchical (MPH) computational model, stereo analysis is performed in multiple stages, incorporating multiple primitives, utilizing a hierarchical control strategy. The MPH stereo system consists of three integrated subsystems: region-based analysis module; linear edge segment-based analysis module; and edgel-based stereo analysis module. Results of stereo analysis at higher levels of the hierarchy are used for guidance at the lower levels. The MPH stereo system does not overly rely on one type of primitive and therefore will reliably work on a wide range of scenes. The MPH stereo analysis results in the generation of several disparity maps of multiple abstraction. Disparity maps generated at each level can be fused to obtain an accurate and fine resolution disparity map. The MPH approach also provides the capability to selectively analyze image regions with varying detail. This provides the means for adaptively extracting range information of only sufficient resolution. Thus, a stereo system that utilizes primitives of different abstraction and a multilevel hierarchical computational strategy will be superior to a single-level, single-primitive system. Extensive experimentation is carried out on a wide array of scenes of varying complexity from two application domains to systematically evaluate the validity and performance of the MPH framework. The MPH stereo system is able to analyze images in most cases with 85%/spl sim/100% matching accuracy in under a minute of processing time and yield depth values typically within /spl plusmn/2% of the actual depth. >

A vision system for robotic inspection and manipulation

A model-based approach has been proposed to make object recognition computationally tractable. In this approach, models associated with objects expected to appear in the scene are recorded in the systems knowledge base. The system extracts various features from the input images using robust, low-level, general-purpose operators. Finally, matching is performed between the image-derived features and the scene domain models to recognize objects. Factors affecting the successful design and implementation of model-based vision systems include the ability to derive suitable object models, the nature of image features extracted by the operators, a computationally effective matching approach, knowledge representation schemes, and effective control mechanisms for guiding the systemss overall operation. The vision system they describe uses gray-scale images, which can successfully handle complex scenes with multiple object types.<<ETX>>

Real-time visual tracking using correlation techniques

A real-time correspondence based tracking algorithm is detailed. The system uses a pipeline processor, a general purpose processor, a camera and a display. The Minimum Noise and Correlation Energy (MINACE) filter is used in the tracking algorithm as it provides a good combination of speed, accuracy and flexibility for the targeted hardware system. The system designed is fast and tracking is accomplished at a rate of 15 hz. The system is adaptive and does not rely on a previous model of the object; the training image for filter synthesis is acquired from previous image frames and the filter is synthesized online to accommodate 3-D variations of the target being tracked. The system tracks an object consistently as is demonstrated by the low deviation of the results in the evaluation. The correlation filter-based tracking algorithm has proved to be useful in our research in cooperative mobile robots. A visual servoing system has been implemented using this tracking algorithm for convoying of multiple mobile robots.<<ETX>>

Active vision system for depth extraction using multiprimitive hierarchical stereo analysis and multiple depth cues

Binocular stereo, vergence stereo, and depth from focus have been extensively studied in isolation in the past for extraction of depth. These passive approaches have their own strengths and limitations. All these cues differ in their input image requirements, computational requirements, and the quality and nature of results they provide. None of them by themselves is sufficient to reliably extract depth information from a wide variety of scenes. It is evident an integrated system which employs multiple cues and exploits their strengths would be most critical for developing a robust depth extraction system. Active vision paradigm allows us to develop such a system where various depth cues cooperate to derive the 3-D structure of the scene. In this, the integration would be accomplished by active intelligent control of the acquisition processes tightly coupled to the analysis of image data.

Multi-primitive hierarchical (MPH) stereo system

A computational framework for an accurate, robust, and efficient stereo approach is developed. Most of the deficiencies prevailing in current computational models of stereo can be attributed to their use of a single, typically edge-element-based, primitive for stereo analysis and to their use of a single-level control strategy. The multi-primitive hierarchical (MPH) framework for stereo analysis presented is directed toward overcoming these deficiencies. In the MPH model, stereo analysis is performed in multiple stages, incorporating multiple primitives and utilizing a hierarchical control strategy. The higher levels of the hierarchical system are based on primitives containing more semantic information, and the results of stereo analysis at higher levels are used for guidance at the lower levels. It is shown that such a stereo system is superior to a single-level, single-primitive system.<<ETX>>

Adaptive visual tracking algorithm and real-time implementation

A real-time correspondence based tracking algorithm is detailed. The system uses a pipeline processor, a general purpose processor, a camera and a display. The minimum noise and correlation energy (MINACE) filter is used in the tracking algorithm as it provides a good combination of speed, accuracy and flexibility for the targeted hardware system. The system designed is fast and tracking is accomplished at a rate of 15 Hz. The system is adaptive and does not rely on a previous model of the object; the training image for filter synthesis is acquired from previous image frames and the filter is synthesized online to accommodate 3D variations of the target being tracked. The system tracks an object consistently as is demonstrated by the low deviation of the results in the evaluation.

Experiments in active vision with real and virtual robot heads

In the emerging paradigm of animate vision, the visual processes are not thought of as being independent of cognitive or motor processing, but as an integrated system within the context of visual behavior. Intimate coupling of sensory and motor systems have found to improve significantly the performance of behavior based vision systems. In order to study active vision systems one requires sensory-motor systems. Designing, building, and operating such a test bed is a challenging task. In this paper we describe the status of on-going work in developing a sensory-motor robotic system, R2H, with ten degrees of freedoms (DOF) for research in active vision. To complement the R2H system a Graphical Simulation and Animation (GSA) environment is also developed. The objective of building the GSA system is to create a comprehensive design tool to design and study the behavior of active systems and their interactions with the environment. GSA system aids the researchers to develop high performance and reliable software and hardware in a most effective manner. The GSA environment integrates sensing and motor actions and features complete kinematic simulation of the R2H system, its sensors and its workspace. With the aid of the GSA environment a Depth from Focus (DFF), Depth from Vergence, and Depth from Stereo modules are implemented and tested. The power and usefulness of the GSA system as a research tool is demonstrated by acquiring and analyzing images in the real and virtual worlds using the same software implemented and tested in the virtual world.

Coordinating motion of cooperative mobile robots through visual observation

This paper describes the development of an integrated autonomous multi-robot system, where two heterogeneous robots exhibit coordinated convoying behavior. Coordination is accomplished without any communication but by visual observation. An active perception system uses visual servoing to track the leading vehicle to determine its heading and relative distance. A fuzzy logic based real-time motion controller uses the heading to control the steering and relative distance and velocity information to adjust the speed to let the trailing robot smoothly follow the leader. A novel correlation algorithm based on minimum noise and correlation energy (MINACE) filter is used for object tracking. The MINACE based tracker is adaptive and when the tracking degrades due to the change in appearance of the leading robot a new filter is synthesized online to replace the old one. The performance and robustness of the convoying system has been verified in a series of extensive laboratory trials.<<ETX>>

Edge-segment-based stereo analysis

Abstract not available.