MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

Independent Domain of Symmetric Encryption using Least SignificantBit : Computer Vision, Steganography and Cryptography Techniques

The rapid development of data transfer through internet made it easier to send the data accurate and faster to the destination. There are many transmission media to transfer the data to destination like e-mails; at the same time it is may be easier to modify and misuse the valuable information through hacking. So, in order to transfer the data securely to the destination without any modifications, there are many approaches like cryptography and steganography. This paper deals with the image steganography as well as with the different security issues, general overview of cryptography, steganography and digital watermarking approaches.  The problem of copyright violation of multimedia data has increased due to the enormous growth of computer networks that provides fast and error free transmission of any unauthorized duplicate and possibly manipulated copy of multimedia information. In order to be effective for copyright protection, digital watermark must be robust which are difficult to remove from the object in which they are embedded despite a variety of possible attacks. The message to be send safe and secure, we use watermarking. We use invisible watermarking to embed the message using LSB (Least Significant Bit) steganographic technique. The standard LSB technique embed the message in every pixel, but my contribution for this proposed watermarking, works with the hint for embedding the message only on the image edges alone. If the hacker knows that the system uses LSB technique also, it cannot decrypt correct message. To make my system robust and secure, we added cryptography algorithm as Vigenere square. Whereas the message is transmitted in cipher text and its added advantage to the proposed system. The standard Vigenere square algorithm works with either lower case or upper case. The proposed cryptography algorithm is Vigenere square with extension of numbers also. We can keep the crypto key with combination of characters and numbers. So by using these modifications and updating in this existing algorithm and combination of cryptography and steganography method we develop a secure and strong watermarking method. Performance of this watermarking scheme has been analyzed by evaluating the robustness of the algorithm with PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error) against the quality of the image for large amount of data. While coming to see results of the proposed encryption, higher value of 89dB of PSNR with small value of MSE is 0.0017. Then it seems the proposed watermarking system is secure and robust for hiding secure information in any digital system, because this system collect the properties of both steganography and cryptography sciences.

A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

Jämförelse av två system för att bedöma vinter väglag : Ett nytt system i utvecklingen för att säkra Sveriges vägar

Trafikverket, är den statliga verksamhet som har hand om alla Sveriges vägar och järnvägar har den så kallade nollvisionen som ett huvudmål. Tanken bakom nollvisionen är att de som använder vägarna skall vara säkra och inte komma till skada. En del av uppfyllandet av detta mål är att Trafikverket ger ut korttidsprognoser för väglag och körförhållande. I nuläget så används ett mycket manuellt systemet som heter NTIS, men man håller på att utveckla det nya automatiska systemet RCC som skall kunna ta fram korttidsprognoser baserat på olika former av data, t.ex. data från väderstationer. Syftet med denna studie är att utvärdera hur väl de två olika systemen utför en korttidsprognos och jämföra de mot varandra, samt verkligheten. Denna studie gjordes i form av en förklarande fallstudie. Som datainsamling används dokument i olika former och analysen var kvantitativ då resultatet av utvärdering ger olika procenttal av hur rätt respektive system har. Under undersökningen gång så kom vi fram till att båda systemen hade sina fördelar och nackdelar. T.ex. så det gamla NTIS systemet fortfarande bäst på isigt och moddigt väglag. Medans det nya RCC systemet hade sina egna fördelar, t.ex. snöigt väglag och vått väglag. Samt så hade RCC en klar fördel med sin rapporteringstid, vilket var ett problem man såg med NTIS. Resultat var som sagt ett procenttal av hur rätt de två olika systemen hade, men även förslag till förbättringar. T.ex. hur man skulle kunna ändra RCC regler för bättre resultat.

Den offentliga sektorns komplexa adaptiva system : En kvalitativ studie om medarbetarpolicys i praktiken

Intresset för hur HR och ledning kan påverka medarbetarnas beteendemönster och den sociala strukturen inom organisationer har gett upphov till denna undersökning. Med utgångspunkt i teorier om komplexa adaptiva system som perspektiv har jag försökt fånga medarbetarnas förutsättningar att nå organisationers vision och därmed förverkliga medarbetarpolicyn i praktiken. Tillsammans med kontaktpersoner från den undersökta kommunen har jag gjort en djupdykning i en offentlig organisation i syfte att förklara byråkratins inverkan på beteendemönstret hos medarbetarna och hur de tillsammans skapar en social struktur vilken speglar organisationens vision. Syftet med studien är att undersöka hur det adaptiva systemet fungerar i en byråkratisk organisation och vad det betyder för medarbetarnas möjligheter att förverkliga organisationens vision och medarbetarpolicy. Undersökningen har genomförts på en socialförvaltning i en kommun i mellan Sverige och med en kvalitativ metod och semistrukturerade intervjuer har sex respondenter deltagit i undersökningen. Samtliga respondenter har olika befattningar inom organisationen och bidrar därmed med olika perspektiv på samma fenomen. Undersökningens resultat visar att den offentliga verksamhetens byråkratiska organisationsstruktur bidrar till att det bildas olika adaptiva system inom organisationen, där medarbetarnas beteendemönster bildar en social struktur som leder till att visionen och medarbetarpolicyn inte förverkligas. Undersökningen visar även att de adaptiva systemen inom organisationen inte påverkar varandra, då de inte interagerar med varandra inom organisationen. Det som ligger till grund för hur medarbetarnas beteendemönster etableras inom organisationen är kraven från omvärlden, hög arbetsbelastning och avsaknaden av stabilitet i den organisatoriska och sociala arbetsmiljön och inte vad tidigare forskning visat; att det skulle vara hög regelstyrning som ligger till grund för beteendemönstret inom den offentliga sektorn. Slutsatsen är att för att medarbetarna ska kunna förverkliga organisationens vision och medarbetarpolicy kräver det att HR, ledning och medarbetare alla ingår i samma adaptiva system. För att det ska vara möjligt behöver HR upprätta strategier för hur samtliga inom organisationen ska interagera med varandra i det dagliga arbetet. Ledningen och medarbetarna behöver även ha goda möjligheter att kommunicera med varandra regelbundet i en större omfattning än vad de gör idag. Konkreta åtgärder för hur det lämpligen bör genomföras presenteras under diskussionen.