Mark Dougherty

The presence of vegetation on railway tracks threatens track safety and longevity. However, vegetation inspections in Sweden are currently being carried out manually. Manually inspecting vegetation is very slow and time consuming. Maintaining an even quality standard is also very difficult. A machine vision-based approach is therefore proposed to emulate the visual abilities of the human inspector. Work aimed at detecting vegetation on railway tracks has been split into two main phases. The first phase is aimed at detecting vegetation on the tracks using appropriate image analysis techniques. The second phase is aimed at detecting the rails in the image to determine the cover of vegetation that is present between the rails as opposed to vegetation present outside the rails. Results achieved in the current work indicate that the machine vision approach has performed reasonably well in detecting the presence/absence of vegetation on railway tracks when compared with a human operator.

Forgetting is an oft-forgotten art. Many artificial intelligence systems deliver good performance when first implemented; however, as the contextual environment changes, they become out of date and their performance degrades. Learning new knowledge is part of the solution, but forgetting outdated facts and information is a vital part of the process of renewal. However, forgetting proves to be a surprisingly difficult concept to either understand or implement. Much of AI is based on analogies with natural systems, and although all of us have plenty of experiences with having forgotten something, as yet we have only an incomplete picture of how this process occurs in the brain. A recent judgment by the European Court concerns the “right to be forgotten” by web index services such as Google. This has made debate and research into the concept of forgetting very urgent. Given the rapid growth in requests for pages to be forgotten, it is clear that the process will have to be automated and that intelligent systems of forgetting are required in order to meet this challenge.

. This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprint's foreground and its border image are treated using different fuzzy rules. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages: pre-processing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

This article describes a method of automatically detecting, counting and classifying logs on a timber truck using a photograph. An image-processing algorithm is developed to process the photograph to calculate an estimate of the number of logs present and their respective diameters. The algorithm uses color information in multiple color spaces as well as geometrical operators to segment the image and extract the relevant information. This information enables the sawmill to better plan internal logistics and production in advance of the truck’s arrival time. The algorithm is robust with respect to external factors such as varying lighting conditions and camera angle, but some inaccuracies remain, mainly caused by logs being occluded or covered in mud or snow.