Current noninvasive or minimally invasive methods for evaluating in vivo knee kinematics are inadequate for accurate determination of dynamic joint function due to limited accuracy and/or insufficient sampling rates. A three-dimensional (3-D) model-based method is presented to estimate skeletal motion of the knee from high-speed sequences of biplane radiographs. The method implicitly assumes that geometrical features cannot be detected reliably and an exact segmentation of bone edges is not always feasible. An existing biplane radiograph system was simulated as two separate single-plane radiograph systems. Position and orientation of the underlying bone was determined for each single-plane view by generating projections through a 3-D volumetric model (from computed tomography), and producing an image (digitally reconstructed radiograph) similar (based on texture information and rough edges of bone) to the two-dimensional radiographs. The absolute 3-D pose was determined using known imaging geometry of the biplane radiograph system and a 3-D line intersection method. Results were compared to data of known accuracy, obtained from a previously established bone-implanted marker method. Difference of controlled in vitro tests was on the order of 0.5 mm for translation and 1.4 degrees for rotation. A biplane radiograph sequence of a canine hindlimb during treadmill walking was used for in vivo testing, with differences on the order of 0.8 mm for translation and 2.5 degrees for rotation.

This correspondence describes a contour extraction algorithm which can gradually improve its results until the extracted contours are closed. This is achieved by an architecture with a feedback path for local smoothing. The feedback path is activated only when one or more contours obtained are not closed in order to initiate smoothing in noisy areas of the image to remove local irregularities that cause the problems. A forward/backward boundary tracing mechanism is employed to facilitate locating any troubled areas. A smoothing method appropriate for reducing local irregularities is discussed. The proposed algorithm is very suitable for those applications that demand closed contours, such as character recognition and blob detection.

This paper proposed a road map production system model using bidirectional search algorithm. The model enable a mobile robot to navigate an environment effectively and be able to execute a high level command such as "go from station I (STI) to station 2 (ST2)". The search algorithm yields the shortest path solution. In the event of unexepeted occurrences such as obstruction of the shortest path, the model would enable the robot to quickly search for the shortest alternative path from its current position to its destination. The system can readily adapt to a dynamically changing road map.

A method for obtaining the skeleton of a segmented gray-scale image is introduced. The gray-scale information is used to produce a connected and one-point wide skeleton positioned in the ridge areas. The input image is considered to be a set of connected nonzero elements as object areas and zero elements as background areas. The obtained skeleton does not depend on the boundary of the image. The gray value of each point in an image represents the degree of its membership. Using the gray value information, the method seeks the ridge areas to position the skeleton.

It is shown that the minimum canonical sum-of-products form of a given fuzzy function is the union of all its prime implicants. A reduction rule, which is necessary and sufficient, is presented that will generate all the prime implicants.