International Journal of Pattern Recognition and Artificial Intelligence

One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case roughly uniform white light.

Depth from defocused algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera’s point spread function is shift invariant, an adaptive algorithm is developed in the frequency domain. The constraints imposed upon the solution power spectrum vector vary temporally. When the camera’s point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. The constraints imposed upon the solution point spread vector vary spatially.

Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted. By relying upon the motion of the observed object on a conveyor belt, a complete range image may be generated.

This paper addresses the problem of determining the reliability of individual sensors in a multi-sensor robotic system in an unknown environment. The inherent difficulty in this problem is that the decision must be based solely upon the data from the sensors themselves. While some previous research has considered unstructured environments (see Refs. 1 and 2 for examples) little if any consideration has been given to totally unknown environments. This problem has usually been avoided by assuming that the sensors would not provide erroneous data or ignoring sensors when they appeared to provide erroneous data. We believe a more robust solution is to consider each sensor’s performance over time compared to other sensors, and from this determine a measure of confidence in each sensor. This allows sensors which temporarily provide erroneous data to be accommodated. A system which can determine the reliability of its sensors is more robust since it can wisely decide which sensors are most appropriate for a given task and can also determine whether sensor conflicts are the result of poorly performing sensors.

Many tasks, such as pose determination, object recognition and model building rely on a geometrical description of the visible surfaces derived from scene measurements. Even though much effort has been invested in surface description, robust extraction of surface parameters from scattered and noisy 3-D measurements is still a delicate task. For a given visible surface section, the extracted parameters should not depend on the sensor position in the scene nor on a particular measurement set on this surface. In this work, we show that a viewpoint invariant and stable local surface description can be extracted on sections where measurement constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify such stable sections. The approach is based on a measurement error model which takes into account the sensor’s viewpoint. An application of the approach to the extraction of straight line sections from single scan 3-D surface profiles is presented. The extracted stable linear sections are stored in a list that includes the estimated descriptive parameters for each section and indices of reliability for each description. The descriptive parameters obtained from images of the objects is compared with pre-measured parameters.

This paper discusses detection and matching of arbitrary image features or patterns. The common characteristics of feature extraction and matching are summarized which show that they can be considered as special cases of a more general problem—signal detection. However, the existing signal detection theories do not solve feature extraction and matching problems readily. Therefore, a general formulation of feature extraction and matching as a problem of signal detection is presented. This formulation unifies feature extraction and matching into a more general framework so that the two can be better integrated to form an automatic system for image matching or object recognition. Following this formulation, guidelines for designing algorithms for detection or matching of arbitrary image features or patterns which can be easily implemented or reconfigurated for many practical applications are derived. Sample algorithms resulting from this formulation and the associated experimental results with real image data are provided which demonstrate the performance and robustness of the methods.

Lowe⁸ demonstrated a method for automatically segmenting and smoothing image curves by varying degrees. It was intended to remove noise and unnecessary fine detail, aiding subsequent processing such as grouping and matching. An alternative technique is described in this paper that is based on recursively subdividing the curve into alternative sets of sections. Rather than use thresholds on the values of curvature and its derivatives to determine the segmentation and degree of smoothing our technique is driven by three qualitative measures: (1) a criterion for selecting potential breakpoints, (2) a criterion for determining the amount of smoothing for curve sections, (3) a significance measure that determines which sections form the best selection. The advantages of the technique are robustness, scale invariance, and the absence of parameters.

Both man-made and natural objects are described by both their geometric shapes and by their non-geometric attributes such as color. The objective of the proposed research is to create a system which integrates geometric and non-geometric attribute information for fast 3D model-based object recognition. Hashing is employed in a hypothesize and verify approach to the 3D model-based object recognition problem. Viewpoint independent attributes are used in the hypothesis generation stage to eliminate model objects from consideration during hypothesis formation.

Utilizing more than one attribute in the proposed hashing scheme helps to ensure a reduction in the actual execution time for object recognition over a larger number of model bases. A nice feature of the system is that new object attributes can be added with relative ease. Issues concerning the ranking of attributes by their distinctiveness with respect to the objects in the model base are discussed. Current work on the use of an information theoretic measure in the design of an optimal multiple attribute hash table is presented.

The design of a robot head for active computer vision tasks is described. The stereo head/eye platform uses a common elevation configuration and has four degree-of-freedom. The joints are driven by DC servo motors coupled with incremental optical encoders and backlash minimizing gearboxes. The details of mechanical design, head controller design, the architecture of the system, and the design criteria for various specifications are presented.

Recent related approaches in the areas of vision, motor control and planning are attempting to reduce the computational requirements of each process by restricting the class of problems that can be addressed. Active vision, differential kinematics and reactive planning are all characterized by their minimal use of representations, which simplifies both the required computations and the acquisition of models. This paper describes an approach to visually-guided motor control that is based on active vision and differential kinematics, and is compatible with reactive planning. Active vision depends on an ability to choose a region of the visual environment for task-specific processing. Visual attention provides a mechanism for choosing the region to be processed in a task-specific way. In addition, this attentional mechanism provides the interface between the vision and motor systems by representing visual position information in a 3-D retinocentric coordinate frame. Coordinates in this frame are transformed into eye and arm motor coordinates using kinematic relations expressed differentially. A real-time implementation of these visuomotor mechanisms has been used to develop a number of visually-guided eye and arm movement behaviors.

A vision system was built using a behavior-based model, the subsumption architecture. The so-called active eye moves the camera’s axis through the environment, detecting areas with high concentration of edges, with the help of a kind of saccadic movement. The design and implementation process is detailed in the article, paying particular attention to the fovea-like sensor structure which enables the active eye to efficiently use local information to control its movements. Numerical measures for the eye’s behavior were developed, and applied to evaluate the incremental building process and the effects of the saccadic movements on the whole system. A higher level behavior was also implemented, with the purpose of detecting long straight edges in the image, producing pictures similar to hand drawings. Robustness and efficiency problems are addressed at the end of the paper. The results seem to prove that interesting behaviors can be achieved using simple vision methods and algorithms, if their results are properly interconnected and timed.