This paper presents a new algorithm for color-based tracking of objects with radical color using modified Mean shift. Conventional color-based object tracking using mean shift does not provide appropriate result when initial color distribution disappears. In this proposed algorithm, Mean shift analysis is first used to derive the object candidate with maximum increase of density direction from current position. Then the proposed algorithm is used iteratively to update the object color information if the object color is changed. The implementation of the new algorithm achieves effective real-time tracking of objects with complete color changed by time. The validity of the effective approach is illustrated by the presentation of experimental results obtained using the methods described in the paper.
The problem of using light stripe projection (LSP) for 3D surface reconstruction is addressed in this paper. By using an adaptive filter, we show that we can recover 3D points that normally would go undetected due to light reflection and shape. Further, we show that the filter improves the accuracy of the 3D point coordinates. The filter is based on polynomial and parabola fitting. It generates a bounded polynomial or a smooth parabolic function based on a peak curve from a stripe sample and it adapts the smooth function so that other stripe sample pixels fit a new stripe sample. We then show how the filter is designed to correct the reflection affective pixels of a stripe sample and how it can improve the edge position extracted by any common edge detection method. The effectiveness of the missing 3D point recovery and the 3D point position accuracy improvement is demonstrated by the presentation of experimental results obtained using the methods described in the paper. A test demonstrating the differences between 3D point models generated with and without the adaptive filter is also presented.
A Color Feature and Density based (CFD) image mosaicing (IM) algorithm is presented in this paper. In the initial step, color image segmentation is used to provide a global match between an image pair. The well-known Iterated Closest Point (ICP) algorithm is used to find a transformation for global alignment. Finally, the ICP algorithm is applied again to find a transformation for local adjustment. By using this approach, it is shown that we can guarantee global alignment accuracy because the feature based method is used to find the initial matching of two image frames. We achieve local optimal pixel alignment based on the minimization of the Sum of Squares of Differences (SSD) between two images from the same overlapping area.
The problem of using light stripe projection (LSP) for reconstructing the 3D surface of several objects positioned at different distances from the camera is addressed in this paper. It is shown using LSP for 3D surface reconstruction of objects at different depths can be significantly improved if adaptive edge detection filter is added. The filter is designed as a local dynamic hysteresis thresholding value generator. It adapts knowledge of the wavelet coefficients in a small neighborhood on the row profile and generates the local hysteresis threshold values to detect a meaningful and useful edge. It is then shown how the filter may be designed to recover missing LSP features by using common edge detection technique and how it increases the accuracy and reliability of 3D surface reconstruction. The adaptive edge detection filter is illustrated by the presentation of experimental results obtained using the methods described in the paper. A test outlines the differences between LSP edges detected with the adaptive edge detection filter and edges detected without the filter.
The problem of tracking the motion of a functional, electrically controlled hand is addressed in this paper. We show that by using skin color and hand segments, the motion of a human hand controlled by a functional electrical stimulation (FES) system can be recovered and predicted. The hand motion tracking and prediction information can then be used as feedback in a closed loop control system to improve the control of the hand motion. We show how skin color tracking can be used to segment the human hand regions and how the hand segments can be used to identify the hand moving positions and how the changes of hand segments between two frames can be used to track the hand orientations. We then show how the hand motion can be recovered based on the changes of the hand positions and orientations and how the motion can be simulated and predicted. The tracking and motion identification methods are verified by the presentation of experimental results obtained using the methods described in the paper.
The increase in quality and the decrease in price of digital camera equipment have led to growing interest in reconstructing 3-dimensional objects from sequences of 2-dimensional images. The accuracy of the models obtained depends on two sets of parameter estimates. The first is the set of lens parameters - focal length, principal point, and distortion parameters. The second is the set of motion parameters that allows the comparison of a moving camera’s desired location to a theoretical location.
In this paper, we address the latter problem, i.e. the estimation of the set of 3-D motion parameters from data obtained with a moving camera. We propose a method that uses Recursive Least Squares for camera motion parameter estimation with observation noise. We accomplish this by calculation of hidden information through camera projection and minimization of the estimation error. We then show how a filter based on the motion parameters estimates may be designed to correct for the errors in the camera motion. The validity of the approach is illustrated by the presentation of experimental results obtained using the methods described in the paper.