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This thesis aims to contribute to the improvement of online machine translation software. We identify errors in the process of translation between English and French and make recommendations. The systems evaluated are Promt, Babylon, Google Translate and Bing and the reference corpus is taken from BankGloss. Promt made the most errors, followed by Babylon, Bing and Google. The systems together produced a total of 147 grammatical errors, 74 semantic errors, 17 lexical errors, and 6 stylistic errors. To improve Promt, we suggest expanding its dictionary. For Babylon, we advise adding more grammar rules. In order to reduce the number of semantic errors in Bing and Google, the software should learn to identify words according to context. Machine translation is not an end in itself, but a good aid in accomplishing translation tasks.

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This paper investigates two distinct, but related, topics in machine vision. The first is the effect of lighting on the performance of a 2D vision-based inspection system. The lighting component of machine vision has often been overlooked; an attempt was made to quantify the impact on existing machine vision algorithms. The second topic explores the applications of a data-rich 3D vision sensor that is capable of providing depth data in a wide range of ambient lightning conditions for industrial applications. A focus is placed on inspection systems with the depth data provided by the sensor. Three basic lighting geometries were compared quantitatively based on discriminant analysis in an inspection task that checked for the presence of J-clips on an aluminum carrier. Two different LabVIEW® machine vision algorithms were used to evaluate backlight, bright field and dark field illumination on their ability to minimize the span of the pass (clip present) and fail (clip absent) sample sets, as well as maximize the separation between these sample sets. Results showed that there were clear differences in performance with the different lighting geometries, with over a 30% change in performance. Although it has long been accepted that the choice of lighting for machine vision systems is not a trivial exercise, this paper provides a quantitative measure of the impact lighting has on the performance of feature-based machine vision. The Microsoft Kinect® is a commercial vision sensor that can simultaneously provide a colour video stream, comparable to current webcam technologies, in addition to a depth stream that provides three-dimensional information of the camera’s field of view and is invariant to environmental lighting. An experiment was carried out to characterize the sensor’s accuracy and precision, and to evaluate its performance as an inspection system to determine the orientation of a wheel. Tests were also conducted to determine the effect that changes in the physical environment had on performance. These changes included camera height, lighting and surface material. Results of the experiment have shown that the sensor has an average precision of ±0.12 cm and average accuracy of 0.5 cm, both with less than a 30% change when varying physical features. A discriminant analysis was performed to measure inspection performance, which showed less than 30% change with set separation, but not for set span. No trends were apparent with the change in set span relating to the change in physical features. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-02-29 18:33:20.505

Production downtime caused by machine faults presents a major area of concern for the manufacturing industry and can especially impact the productivity of assembly systems. Traditional fault detection systems use a variety of conventional sensors that measure operating variables such as pressure, force, speed, current and temperature. Faults are detected when a reading from one of these sensors exceeds a preset threshold or does not match the predicted value provided by a mathematical model of the system. The primary disadvantage of these methods is that the relationship between sensor reading and fault is often indirect (if one exists at all). This can lead to time delays between fault occurrence and ‘fault reading’ from a sensor, during which additional machine damage could accumulate. This thesis describes progress with a project whose goal is to examine the effectiveness and feasibility of using machine vision to detect ‘visually cued’ machine faults in automated assembly equipment. It is proposed that machine vision technology could complement traditional methods and improve existing detection systems. Two different vision-based fault detection methods were developed and tests were conducted using a laboratory-scale assembly machine that assembles a simple 3-part component Typical faults that occurred with this machine were targeted for inspection. The first method was developed using Automated Visual Inspection (AVI) techniques that have been used extensively for quality inspection of manufactured products. The LabVIEW 2010 software was used to develop the system. Test results showed that the Colour Inspection tool performed the best with 0% false negative and false positive fault detection rates. Despite some success, this approach was found to be limited as it was unable to detect faults that varied in physical appearance or those that had not been identified prior to testing. The second method was developed using a video event detection method (spatiotemporal volumes) that has previously been used for traffic and pedestrian monitoring. This system was developed with MATLAB software and demonstrated strong false negative and false positive fault detection rates. It also showed the ability to detect faults that had not previously been identified as well as those that varied in appearance. Recommendations were made for future work to further explore these methods. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-07-13 16:04:57.829