Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112212/1/rob21542.pd

Precision of pose estimation using corner detection.

The aim of this research was to develop a method for recording ground truth with performance comparable to motion capture, in order to produce high-quality outdoor visual odometry datasets. A novel fiducial marker system was developed, featuring a smooth pattern which is used in an optimisation process to produce refined estimates. On average, precision was increased by 27 % compared to traditional fiducial markers. To investigate the limit of the increase in pose estimation precision possible with this method, the marker was modelled as a dense grid of checkerboard corners and the Cramér-Rao lower bound of the corresponding estimator was derived symbolically. This gave a lower bound for the variance of a pose estimated from a given image. The model was validated in simulation and using real images. The distribution of the error for a common checkerboard corner detector was evaluated to determine whether modelling it using independent and identically distributed Gaussian random variables was valid. In a series of experiments where images were collected from a tripod, a robot arm, and a slider-type electric actuator, it was determined that the error is usually normally distributed but its variance depends on the amount of lens blur in the image, and that any amount of motion blur can produce correlated results. Furthermore, in images with little blur (less than approximately one pixel) the estimates are biased, and both the bias and the variance are dependent on the location of the corner within a pixel. In real images, the standard deviation of the noise was around 80 % larger at the pixel edges than at the centre. The intensity noise from the image sensor was also found not to be identically distributed: in one camera, the standard deviation of the intensity noise varied by a factor of approximately four within the region around a checkerboard corner. This research suggests that it is possible to significantly increase fiducial marker pose estimation precision, presents a novel approach for predicting and evaluating pose estimation precision, and highlights sources of error not considered in prior work

Classifier design for computerâ aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135032/1/mp8805.pd

On Multifractal Structure in Non-Representational Art

Multifractal analysis techniques are applied to patterns in several abstract expressionist artworks, paintined by various artists. The analysis is carried out on two distinct types of structures: the physical patterns formed by a specific color (``blobs''), as well as patterns formed by the luminance gradient between adjacent colors (``edges''). It is found that the analysis method applied to ``blobs'' cannot distinguish between artists of the same movement, yielding a multifractal spectrum of dimensions between about 1.5-1.8. The method can distinguish between different types of images, however, as demonstrated by studying a radically different type of art. The data suggests that the ``edge'' method can distinguish between artists in the same movement, and is proposed to represent a toy model of visual discrimination. A ``fractal reconstruction'' analysis technique is also applied to the images, in order to determine whether or not a specific signature can be extracted which might serve as a type of fingerprint for the movement. However, these results are vague and no direct conclusions may be drawn.Comment: 53 pp LaTeX, 10 figures (ps/eps

Extracting field hockey player coordinates using a single wide-angle camera

In elite level sport, coaches are always trying to develop tactics to better their opposition. In a team sport such as field hockey, a coach must consider both the strengths and weaknesses of both their own team and that of the opposition to develop an effective tactic. Previous work has shown that spatiotemporal coordinates of the players are a good indicator of team performance, yet the manual extraction of player coordinates is a laborious process that is impractical for a performance analyst. Subsequently, the key motivation of this work was to use a single camera to capture two-dimensional position information for all players on a field hockey pitch. The study developed an algorithm to automatically extract the coordinates of the players on a field hockey pitch using a single wide-angle camera. This is a non-trivial problem that requires: 1. Segmentation and classification of a set of players that are relatively small compared to the image size, and 2. Transformation from image coordinates to world coordinates, considering the effects of the lens distortion due to the wide-angle lens. Subsequently the algorithm addressed these two points in two sub-algorithms: Player Feature Extraction and Reconstruct World Points. Player Feature Extraction used background subtraction to segment player blob candidates in the frame. 61% of blobs in the dataset were correctly segmented, while a further 15% were over-segmented. Subsequently a Convolutional Neural Network was trained to classify the contents of blobs. The classification accuracy on the test set was 85.9%. This was used to eliminate non-player blobs and reform over-segmented blobs. The Reconstruct World Points sub-algorithm transformed the image coordinates into world coordinates. To do so the intrinsic and extrinsic parameters were estimated using planar camera calibration. Traditionally the extrinsic parameters are optimised by minimising the projection error of a set of control points; it was shown that this calibration method is sub-optimal due to the extreme camera pose. Instead the extrinsic parameters were estimated by minimising the world reconstruction error. For a 1:100 scale model the median reconstruction error was 0.0043 m and the distribution of errors had an interquartile range of 0.0025 m. The Acceptable Error Rate, the percentage of points that were reconstructed with less than 0.005 m of error, was found to be 63.5%. The overall accuracy of the algorithm was assessed using the precision and the recall. It found that players could be extracted within 1 m of their ground truth coordinates with a precision of 75% and a recall of 66%. This is a respective improvement of 20% and 16% improvement on the state-of-the-art. However it also found that the likelihood of extraction decreases the further a player is from the camera, reducing to close to zero in parts of the pitch furthest from the camera. These results suggest that the developed algorithm is unsuitable to identify player coordinates in the extreme regions of a full field hockey pitch; however this limitation may be overcome by using multiple collocated cameras focussed on different regions of the pitch. Equally, the algorithm is sport agnostic, so could be used in a sport that uses a smaller pitch