Fingerprint Segmentation

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Filmljudets funktioner i krigsfilm : En audio-visuell analys av Apocalypse Now och Saving Private Ryan

I denna uppsats har filmljudet i krigsfilmerna Apocalypse Now och Saving Private Ryan undersökts. Detta har gjorts för att försöka bidra med ökad förståelse för filmljudets användningsområde och funktioner, främst för filmerna i fråga, men även för krigsfilm rent generellt. Filmljud i denna kontext omfattar allt det ljud som finns i film, men utesluter dock all ickediegetisk musik. Båda filmerna har undersökts genom en audio-visuell analys. En sådan analys görs genom att detaljgranska båda filmernas ljud- och bildinnehåll var för sig, för att slutligen undersöka samma filmsekvens som helhet då ljudet och bilden satts ihop igen. Den audio-visuella analysmetod som nyttjats i uppsatsen är Michel Chions metod, Masking. De 30 minuter film som analyserades placerades sedan i olika filmljudzoner, där respektive filmljudzons ljudinnehåll bland annat visade vilka främsta huvudfunktioner somfilmljudet hade i dessa filmer. Dessa funktioner är till för att bibehålla åskådarens fokus och intresse, att skapa närhet till rollkaraktärerna, samt att tillföra en hög känsla av realism och närvaro. Intentionerna med filmljudet verkade vara att flytta åskådaren in i filmens verklighet, att låta åskådaren bli ett med filmen. Att återspegla denna känsla av realism, närvaro, fokus samt intresse, visade sig också vara de intentioner som funnits redan i de båda filmernas förproduktionsstadier. Detta bevisar att de lyckats åstadkomma det de eftersträvat. Men om filmljudet använts på samma sätt eller innehar samma funktioner i krigsfilm rent genrellt går inte att säga.I have for this bachelor’s thesis examined the movie sound of the classic warfare movies Apocalypse Now and Saving Private Ryan. This is an attempt to contribute to a more profound comprehension of the appliance and importance of movie sound. In this context movie sound implies all kinds of sounds within the movies, accept from non-diegetic music. These two movies have been examined by an audio-visual analysis. It's done by auditing the sound and picture content separately, and then combined to audit the same sequence as a whole. Michel Chion, which is the founder of this analysis, calls this method Masking. The sound in this 30 minute sequence was then divided into different zones, where every zone represented a certain main function. These functions are provided to create a stronger connection to the characters, sustain the viewers interest and bring a sense of realism and presence. It seems though the intention with the movies sound is to bring the viewers to the scene in hand, and let it become their reality. To mirror this sense of realism, presence, focus and interest, proves to be the intention from an early stage of the production. This bachelor’s thesis demonstrates a success in their endeavours. Although it can’t confirm whether the movie sound have been utilized in the same manner or if they posess the same functions to warefare movies in general.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Filmljudets funktioner i dramafilm : En audio-visuell analys av filmen The King's Speech

Johansson, Fredrik (2012). Filmljudets funktioner i dramafilm – En audio-visuell analys av filmen The King´s Speech. Examensuppsats inom Ljudproduktion, Högskolan Dalarna, Akademin för språk och medier, Falun. I denna uppsats undersöktes filmljudet i dramafilmen The King´s Speech. Detta för att ta reda på vilka funktioner filmljudet fyller i de valda sekvenserna ur nämnda film, samt hur ljudet är placerat i filmens flerkanalsmix. Filmen granskades med hjälp av en audio-visuell analys. Denna metod går ut på att ljudet och bilden undersöks separat, för att sedan åter kombineras och analyseras som helhet. Den audio-visuella analysmetod som använts kommer från ljudteoretikern Michel Chion, och kallas Masking. Resultatet av den audio-visuella analysen pekade mot att ljudets huvudsakliga funktioner var att skapa en realistisk skildring av karaktärer och omgivningar, skapa en känsla av närvaro, samt att skapa och bibehålla olika perspektiv i den narrativa världen. Den stora majoriteten av ljud visade sig vara placerade i centerkanalen, medan främst ickediegetisk musik och ambiensljud var placerade i front- och surroundkanalerna. Detta kanalanvändande tycktes gynna de funna funktionerna, främst genom att bidra till känslan av närvaro och realism, genom att omsluta filmpubliken med ambienta ljud.

Game Audio in Audio Games : Towards a Theory on the Roles and Functions of Sound in Audio Games

For the past few decades, researchers have increased our understanding of how sound functions within various audio–visual media formats. With a different focus in mind, this study aims to identify the roles and functions of sound in relation to the game form Audio Games, in order to explore the potential of sound when acting as an autonomous narrative form. Because this is still a relatively unexplored research field, the main purpose of this study is to help establish a theoretical ground and stimulate further research within the field of audio games. By adopting an interdisciplinary approach to the topic, this research relies on theoretical studies, examinations of audio games and contact with the audio game community. In order to reveal the roles of sound, the gathered data is analyzed according to both a contextual and a functional perspective. The research shows that a distinction between the terms ‘function’ and ‘role’ is important when analyzing sound in digital games. The analysis therefore results in the identification of two analytical levels that help define the functions and roles of an entity within a social context, named the Functional and the Interfunctional levels. In addition to successfully identifying three main roles of sound within audio games—each describing the relationship between sound and the entities game system, player and virtual environment—many other issues are also addressed. Consequently, and in accordance with its purpose, this study provides a broad foundation for further research of sound in both audio games and video games.

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.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.