Research Symposium-2013

Permanent URI for this collection

https://erepo.lib.uwu.ac.lk/handle/123456789/6576

Browse

Browsing Research Symposium-2013 by Subject "Automation"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Artificial Neural Network based Signature Recognition and Verification System
    (Uva Wellassa University of Sri Lanka, 2013) Prabodith, N.P.C.; Wimaladharma, S.T.C.I.
    The signature is an important biometric attribute of a person which can be used to authenticate the personal identity because of its uniqueness for each person. Now a day the personal signature has a significant value in day to day works. Because of its uniqueness there is a certain pattern which can be identified by extracting certain unique features. Though in present days signatures are using as the valid authentication mechanism, some peoples are trying to imitate another person’s signature to achieve some narrow goals. It is also common sight in Sri Lanka place like banks, government organizations, universities etc. Therefore, it is essential to introduce a high accuracy validation mechanism for personal authentication. The proposed system uses an efficient image processing and feature extraction methods as well as well-trained neural network system. Methodology Implementation of the system mainly based on two phases, Training phase and recognition phase. Several technologies, Programming languages and Libraries are used to design and implement the system. In the training phase there are several steps to be completed by the system before the training get started. In pre-processing activities, system is mainly focusing on background elimination, cropping (crop signature image according to the signature bounds), thresholding, thinning, and image width transformation (Abikoye et al., 2011). Feature extraction is one of the important parts of the system because powerful features directly affect to the accuracy of the final output. In here firstly, system will collect some global features such as pixel density, width to height ratio, maximum horizontal count and maximum vertical count. Then system will extract some unique points based on vertical splitting and horizontal splitting (Ashwiniet al., 2012). Those feature points are related with the image geometric centre point. After the feature extraction process is completed and then system normalizes all the features and added them to the input vector. Main purpose of the normalization is to convert values into an acceptable range for the neural network (range between 0 and 1). Then, the normalize data are used to train the Neural Network. In the Recognition phase, all the steps up to feature normalization are accomplished and those features are compared with trained Neural Network. Finally, the system will generate a unique value and which reveals the acceptance and rejection of the relevant signature.
  • No Thumbnail Available
    Item
    Factors Affecting on Adoption to Factory Automation in Low Country Tea Manufacturing Process
    (Uva Wellassa University of Sri Lanka, 2013) Abeynayake, M.M.; Rathnayaka, R.M.S.D.
    The legacy of Sri Lankan tea industry shows that Sri Lanka is one of the oldest tea producing countries in the world. The tea produced in this country is popularly known as “Ceylon Tea” and ranks among the best available teas in the international trade. Over the years, the word Ceylon has become synonymous with quality tea. Sri Lanka’s tea cultivators and manufacturers are the custodians of the traditional, orthodox method of black tea producers. This method is still agreed by most experts to produce the best black tea. Even with the technological improvements introduced to the tea industry over the last thirty or forty years, the orthodox method is relatively slow and labour-intensive method. In tea industry worker productivity plays an important role (Mohamed and Zoysa, 2006). Arising the worker shortage and Lower efficiency of workers in recent time Sri Lanka tea industry has faced several problems. Labour availability in the tea industry has declined by 50 % from the 1980s. Other than, the wage rate has catapulted from Rs 290/- five-years back to Rs 620/- as at 2013 which is an increase of 114%. But the net sales prices from the auctions reveal that in 2008 the average was at Rs 310/- per kg and today the prices are at around 400/- which reflects only a 29% increase. Therefore cost of production has increased significantly. Automation of tea processing, is a best solution to minimize over-dependence on workers and improve efficiency of the stages in manufacturing process. Automated tea processing systems able to convert the tea processing industry with state of the art material moving conveyor systems and other automation facilities. This has successfully improved the quality and quantity of the tea output from the tea production process. Even though it gains more benefits to the industry only a few have modernized to a notable degree (Kodithuwakku and Priyanath, 2007). But there are no studies that help to identify the current situation of adoption of factory automations in Sri Lankan tea industry. Therefore this study aimed to determine the factors that influence the adoption to factory automation in tea manufacturing.
  • No Thumbnail Available
    Item
    NoobaVSS: Video Processing Framework to Enhance Processing and Automated Manipulation of Surveillance Videos
    (Uva Wellassa University of Sri Lanka, 2013) Nanayakkara, A.; Dissanayaka, A.; Wijenayake, C.; Hettiarachchi, C.; De Silva, C.
    Surveillance cameras are becoming artificial eyes capable of monitoring behaviors, activities, or other visual information with the purpose of influencing, managing, directing, or protecting. However they still depend on human assistance in interpreting any anomalies in the scenes they capture. Next generation smart surveillance systems are expected to be capable of detecting anomalies by themselves releasing human operators from constant, manual observation of the video feeds. In the recent past Sri Lanka has shown a rapid increase in the use of CCTV surveillance systems in different types of environments including commercial, non-commercial and government sectors. Most of these however are used only for post-incident investigation purposes mainly due to the higher effort and cost required for real time analysis. The unavailability of video analyzing platforms in the public domain and non-existence of open source video analyzing software has deterred their use for pre-incident investigation and real time analysis. Our research effort is to develop a software framework that will act as a testing framework and software basement for automated surveillance video analysis with the aim of improving quality and level of security provided by video surveillance systems. A sample scenario for a banking environment is studied extensively to guide the development process. Methodology The framework is developed as a component based model. A set of individual plugins have been developed separately and connected to the main engine where each individual plugin is responsible for a separate feature extracting task. A plug-in is basically capable of processing a given sequence of image frames from a video and extract designated features (ex: Number of faces in the scene, Speed of an object in the scene). To identify these key features to be extracted from the video imagery, a scenario analysis is conducted over capturing domain (in our extensive study-banking environment). Scenario analysis is useful in identifying what is needed to be extracted from the input video and what is not needed to be extracted. Since the approach in writing scenarios is not restricted to any formal method or constrained by any event sequence, more free flowing and different scenarios are captured. These scenarios ultimately make it easier to identify the nature of the environment and give more insight in identifying computer vision techniques that need to be used. Next, to extract each of those features of the video, a separate plugin has been developed. Knowledge representation platform has been developed using the Qt framework. This framework has the unique capability of loosely coupling functions using signal slot mechanism. Each processing plugin essentially has the same structure, where it may or may not subscribe to outputs of some other plugins. It processes the inputs accordingly within the given time frame and emits its output, if any. They all are feature detectors which take input from a surveillance video feed. A global timing signal has been used to keep track of time and an abstract processing node facilitates signal slot mechanism. It has an abstract process method, so that the processing modules inherited from it can implement a different functionality for a process method. However, nodes named as D does not subscribe to any other nodes. They can be feature detectors which take input from a video feed. In the testing environment, it can read from a file and emit the content as an event for the given time frame.
  • No Thumbnail Available
    Item
    Object Tracking Automated Camera
    (Uva Wellassa University of Sri Lanka, 2013) Udangamuwa, E.M.D.T.E.; Rajapaksha, R.W.V.P.C.
    Because of the advance in surveillance systems, object tracking has been an active research topic in the computer vision community over the last two decades as it is an essential prerequisite for analyzing and understanding video data. Tracking an object is to get its spatial- tempo information by estimating its trajectory in the image plane as it moves around a scene, which in turn helps to study and predict its future behavior. Enhancement of object tracking systems is building up a pan tilt moving cameras based on the movements of the detected object by combining the object tracking and computer vision technologies with microcontrollers. These systems are capable of continuing the tracking even though the object runs away from the boundaries of the normal still camera. Therefore, this research project was carried out to develop an object tracking Pan Tilt moving automatic camera with a low cost and better performance. Methodology This proposed system mainly consists of two modules such as a hardware module and a Software module. The Software module comprises of image processing algorithms and tracking algorithms. The hardware module consists with a Pan Tilt moving camera that was used to take video inputs and a Microcontroller to pass the control instructions to the Servo motors attached to the Pan Tilt mechanism. In this proposed system the video from the camera was processed using Digital Image Processing (Kirillov, 2008). The video was read as frames (Kirillov, 2009)and frame was a still image. The user was allowed to click on any object in this image using a mouse pointer. Then the color of the selected point was read as RGB values. The colors of the image were filtered out according to the selected color. Using a Color filtering algorithm it was able to filter the colors of the selected color and filled the rest of the image with black color. After the above process, developed algorithm was able to find the blobs with same color in the image and saved each and every blob in an array sorted by the size of the blobs. Then the largest blob was selected and drawn a rectangle around largest blob to clearly point out the selected object, and calculated the X and Y coordinates of the center of the rectangle with respect to the display pane. The above scenario was repeated for every frame. Thus any object in the frame can be tracked by its color, using the above algorithm. Universal Serial Bus (USB) communication was very important to read and write data to the external hardware. First the USB port which was used to communicated with the implemented hardware and the Software had to be configured. Once the baud rate was specified in the system, the serial port started to perform the communication. The Microcontroller attached to the serial port was programmed to read the data from the USB port, and the program run in a Computer was able to read the data from the Microcontroller via USB. In this proposed system, two servo motors were used and initially they were set to 90 degrees. Then in tracking algorithm two variables X and Y were declared and they were used to hold the values which were going to pass to the Microcontroller. Above X and Y variables also set to 90 degrees initially. Then calculated the display pane’s center X and Y coordinates. Subtracted the display pane’s center X coordinates from the largest blob’s center X coordinate, if the value was a negative value, and then decreased the variable value which hold the X value and wrote it to the serial port. If the subtract value was positive increased the variable value which hold the X value and wrote it to the serial port. If it equal to 0 did nothing. With parallel to the above process, subtracted the display pane’s center Y coordinates from the largest blob’s center Y coordinate, if the value was negative value, and then increased the variable value which hold the Y value and wrote it to the serial port. If the subtract value was positive decreased the variable value which hold the Y value and wrote it to the serial port. If it equal to 0 did nothing. These two parallel processes executed until the program stopped inside an infinite loop. The Microcontroller was programmed to accept those two values and it was programmed to identify the X and Y values uniquely and pass correct values to relevant servo motors. This algorithm and the programmed microcontroller kept the tracked object in the center of the display pane always by rotating the camera towards the object.