Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Anton Nyström's Defense of Homosexuality

In 1919 Anton Nyström became the first person in Sweden to publish a comprehensive defense of homosexuality. He believed that its classification as a mental illness was erroneous and that Sweden's law against homosexual sex was both irrational and cruel. Nyström was a physician whose work in the medical area dealt primarily with dermatology, psychiatry and human sexuality; however he was also a prolific historian, who took a staunchly anti-Christian view in his analysis of how Christianity affected European culture, especially in the area of sexual morality. In fact, much of Nyström's medical texts dealing with human sexuality consisted of anti-Christian cultural and historical commentary. The object of this "C-uppsats" is to analyze Nyström's pamphlet, Om Homosexualitet och Hermafroditi: Belysning af Missförstådda Existenser and illustrate how its defensive structure was consistent with the pattern used by the author in his other books and articles on human sexuality. Specifically, that irrational and neurotic Christian beliefs caused both mental and physical suffering and were the source of deleterious forms of morality. Additionally, this paper will also show that the solution Nyström had for the problem of negative and erroneous attitudes towards homosexuality was to replace the sodomitic view of homosexuality with one based upon a more rational and naturalistic belief system, the basis of which could be found in the pre-Christian cultures of Europe, most especially in Greece. This new conception was to be constructed primarily out of historical example and cultural analyses. For Nyström, history writing was used both as a weapon to fight the source of negative attitudes towards homosexuality, as well as a tool that could be used to build a positive cultural model which would be beneficial for homosexuals.

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.

Ausencia, prohibición y carencia : Estudio de los personajes y el deseo frustrado en tres obras de García Lorca

In this thesis, the main male characters in three of the plays written by Federico García Lorca are analysed with the aim of seeingthe role they play in the frustration of desire. After two chapters dedicated to a review of published critical studies on Lorca and tocertain theoretical considerations, Chapter Three examines desire drawing on Ubersfeld's actancial model and observes that thesemale characters can be divided into two groups: those who are desired and those who are undesired.In Chapter Four this classification is linked to an analysis of absence, prohibition and lack. Absence is here defined not asrelated to their non-appearance on stage but rather to their non-presence in the lives of the desiring female protagonists. It isobserved that a number of male characters are absent in the plays mainly due to death or a journey. As far as prohibition isconcerned, in two of the works, there is a moral code associated with concepts such as "honour" and "decency", which blocks thefemale characters' access to the males they desire. Chapter Four also shows how several characters can be considered as lacking inthe sense that they do not possess the ideal male qualities contained in the plays. This chapter reaches the conclusion that desiredmale characters are either absent or forbidden in the world of the desiring female, whereas undesired male characters are lacking inthe sense that they fail to live up to the ideal highlighted in the plays.Chapter Five analyses the female characters' perception of the male figures, making use of René Girard's notion of"transfiguration", which alludes to a process of idealisation of the object of desire. Our analysis reveals a connection betweendesire, denied access to the object of desire and transfiguration in the main subjects of desire. The phenomenon of "transfiguration"has several functions in the play: firstly, the creation of hyperbolical male characters; secondly, that of transmitting the intensity ofthe desire experienced and, finally, the highlighting of the lack of certain qualities in several male characters.We thus observe that, in these three plays written by García Lorca, Girard's pessimistic view of desire is confirmed, since desireneeds a series of obstacles, such as absence or prohibition, to survive. However, this is not the only explanation for the frustrationof desire: other factors, like the actions of certain male characters or destiny, also play a decisive role.

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.

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.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

Convex Hulls in Image Processing : A Scoping Review

The demands of image processing related systems are robustness, high recognition rates, capability to handle incomplete digital information, and magnanimous flexibility in capturing shape of an object in an image. It is exactly here that, the role of convex hulls comes to play. The objective of this paper is twofold. First, we summarize the state of the art in computational convex hull development for researchers interested in using convex hull image processing to build their intuition, or generate nontrivial models. Secondly, we present several applications involving convex hulls in image processing related tasks. By this, we have striven to show researchers the rich and varied set of applications they can contribute to. This paper also makes a humble effort to enthuse prospective researchers in this area. We hope that the resulting awareness will result in new advances for specific image recognition applications.