Actividades físicas para el calentamiento y la parte inicial de una

Vision System based on Line Detection using the Hough

transform

A vision system has been developed for a mobile robot based on line detection with the Hough transform. Several processes were carried out towards development of the system. They are summarised in the sub-sections which follow.

8.3.1 Customisation and Implementation of the Hough Transform

Towards customisation and implementation of the Hough transform, tasks carried out include choice of origin for image space, choice of resolution for the parameters of the transform based on analysis of the input images and the nature of the output required, and determination of the range of the parameters to be used for the transform. The Hough transform has been applied using the polar form of the equation of a straight line.

This work has developed and implemented a scheme for peak detection which includes automatic determination of the most appropriate threshold for the image under consideration, application of the reduced butterfly filter of (Boyce et al 1987) to entries above the threshold found only, and selection of elements of the accumulator array that are local maxima within a 5 x 5 neighbourhood. This scheme has been found to be very efficient and accurate.

By way of post-processing, a scheme has been developed to determine endpoints for genuine sub-lines from the original image while eliminating ‘false’ lines which emerged by accumulating votes from random points by ‘coincidence’. A group of contributing points constitutes a valid sub-line if it meets two criteria – there are at least a certain number, L_min of points in the group, and the separation between

the group and any other points is at least S_min pixels. Values of L_minand S_min were determined by analysis of the nature of the images in this work. Results from this scheme have been very good.

8.3.2 Vanishing Point Estimation

The determination of high-level features in this work relies heavily on knowledge of the position of the primary vanishing point on the image. A scheme has been developed for determination of the primary vanishing point which involves finding intersection points for all pairs of actual lines found and awarding them points based on a scheme developed by this work. The intersection point that scores the highest using this scheme is returned as the vanishing point, provided the total of points it has scored has reached a certain threshold which was determined empirically a priori. If the threshold is not reached by any of the intersection points, there is not enough evidence to confidently point out the vanishing point.

This scheme has performed very well.

8.3.3 Line Categorisation

In order to facilitate determination of high-level features, this work proposed a categorisation scheme which categorises lines into vertical, vertical-backslash, backslash, horizontal-backslash, horizontal, horizontal-slash, slash, and vertical- slash categories depending on the angle of the line. It also assigns a magnitude and a sign to the line depending on its distance from the vanishing point.

This categorisation has proved to be very helpful in post-processing.

8.3.4 High-Level Features Determination

Corridor recognition is critical to successful navigation in a corridor. In this work, this has been achieved by searching for a left-corridor-edge and a right-corridor- edge using a points-based scheme developed for the purpose. To select a left- corridor-edge for example, points are assigned to lines according to whether or not they fit a certain criterion - whether they are in the range 90° to 180° - and if so, how they score against three further criteria. The three criteria relate to the distance of the most south-westerly point on the lines to the bottom-left corner of

the image, the shortest distance of the lines to the primary vanishing point on the image if it has been found, and which categories the lines belong to (using the categorisation scheme developed by this work described earlier). The line which scores the highest is the left-corridor-edge provided its score is equal to or higher than a certain cut off score determined empirically.

Similar schemes have been developed to detect doors, whether they are at the end of a corridor, or in a wall.

These schemes have worked well.

8.3.5 Navigation

A navigation scheme has been designed to work with the outcome of post- processing and high-level features detection, and the navigation objective of the robot. Three simple navigation objectives were developed – move-along-corridor, navigate-into-door-on-the-left, and navigate-into-door-on-the-right. These simple objectives can be combined in any way to derive more sophisticated and more useful objectives.

This scheme has not been tested exhausted due to time constraint.

8.4

Achievements and Conclusions Related to a Mobile Robot

Vision System based on Line Detection using Artificial Neural

Networks

Attempts were made to develop another vision system based on line detection using artificial neural networks. Two approaches were investigated.

One of them attempts to find lines belonging to the categories defined in this work. It looks for lines in each category with a separate neural network trained to recognise lines in that category in parallelograms extracted from the image of with shapes with dimensions dictated by the range of the angle lines in the category take. Lines found would then be analysed for existence of high level features. This approach worked quite well for the vertical category in which lines tend to be very long. It failed woefully for a slanted category for which lines tend to be very short relative to the stripes that contain them. The approach was aborted.

The second approach involved breaking an image down to sub-images, and then determining whether the sub-image contains a line in any of the categories set up in this work using a group of 8 neural networks called the stage 1 networks. Results from these stage 1 networks are then passed to a stage 2 network which is set up to work out the direction the robot should move in. The stage 1 networks did reasonable well in recognising the target lines, but the stage 2 network failed to properly recognise the correct direction the robot should take for most test images. This means the system as a whole did not do well. However, the stage 1 networks which did do well were helpful in the development of a hybrid Hough transform/neural network vision system.