Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Passive MVC och MVVM designmönster i Android : En jämförelse av kodkomplexitet mellan Passive MVC och MVVM

Det mobila operativsystemet Android är idag ett ganska dominerande operativsystem på den mobila marknaden dels på grund av sin öppenhet men också på grund av att tillgängligheten är stor i och med både billiga och dyra telefoner finns att tillgå. Men idag har Android inget fördefinierat designmönster vilket leder till att varje utvecklare får bestämma själv vad som ska användas, vilket ibland kan leda till onödigt komplex kod i applikationerna som sen blir svårtestad och svårhanterlig. Detta arbete ämnar jämföra två designmönster, Passive Model View Controller (PMVC) och Model View View-Model (MVVM), för att se vilket designmönster som blir minst komplext med hjälp av att räkna fram mätvärden med hjälp av Cyclomatic Complexity Number (CCN). Studien är gjord utifrån arbetssättet Design & Creation och ämnar bidra med: kunskap om vilket mönster man bör välja, samt om CCN kan peka ut vilka delar i en applikation som kommer att ta mer eller mindre lång tid att testa. Under studiens gång tog vi även fram skillnader på om man anväder sig av den så kallade Single Responsibilyt Principle (SRP) eller inte. Detta för att se om separerade vyer gör någon skillnad i applikationernas komplexitet. I slutändan så visar studien på att komplexiteten i små applikationer är väldigt likvärdig, men att man även på små applikationer kan se skillnad på hur komplex koden är men också att kodkomplexitet på metodnivå kan ge riktlinjer för testfall.