Study and Development of Some Novel Image Segmentation Techniques

Some fuzzy technique based segmentation methods are studied and implemented and some fuzzy c means clustering based segmentation algorithms are developed in this thesis to suppress high and low uniform random noise. The reason for not developing fuzzy rule based segmentation method is that they are application dependent In many occasions, the images in real life are affected with noise. Fuzzy c means clustering based segmentation does not give good segmentation result under such condition. Various extension of the FCM method for segmentation are present in the literature. But most of them modify the objective function hence changing the basic FCM algorithm present in MATLAB toolboxes. Hence efforts have been made to develop FCM algorithm without modifying their objective function for better segmentation . The fuzzy technique based segmentation methods that are studied and developed are summarized here. (A) Fuzzy edge detection based segmentation: Two fuzzy edge detection methods are studied and implemented for segmentation: (i) FIS based edge detection and (ii) Fast multilevel fuzzy edge detector (FMFED). (i): The Fuzzy Inference system (FIS) based edge detector consists of some fuzzy inference rules which are defined in such a way that the FIS system output (“edges”) is high only for those pixels belonging to edges in the input image. A robustness to contrast and lightining variations were also taken into consideration while developing these rules.The output of the FIS based edge detector is then compared with the existing Sobel, LoG and Canny edge detector results. The algorithm is seen to be application dependent and time consuming. (ii) Fast Multilevel Fuzzy Edge Detector: To realise the fast and accurate detection of edges, the FMFED algorithm is proposed. It first enhances the image contrast by means of a fast multilevel fuzzy enhancement algorithm using simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by using a two stage edge detector operator that identifies the edge candidates based on local characteristics of the image and then determines the true edge pixels using edge detector operator based on extremum of the gradient values. Finally the segmentation of the edge image is done by morphological operator by edge linking. (B) FCM based segmentation: Two fuzzy clustering based segmentation methods are developed: (i) Modified Spatial Fuzzy c-Means (MSFCM) (ii) Neighbourhood Attraction Fuzzy c-Means (NAFCM). . (i) Contrast-Limited Adaptive Histogram Equalization Fuzzy c-Means (CLAHEFCM): This proposed algorithm presents a color segmentation process for low contrast images or unevenly illuminated images. The algorithm presented in this paper first enhances the contrast of the image by using contrast limited adaptive histogram equalization. After the enhancement of the image this method divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. Clustering is done here by using Fuzzy c means algorithm. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied on a number of color test images and it is observed to give good segmentation results (ii) Modified Spatial Fuzzy c-means (MSFCM): The proposed algorithm divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. A robust segmentation technique based on extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed. The spatial information of each pixel in an image has been taken into consideration to get a noise free segmentation result. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied to some color test images and its performance has been compared to FCM and FCM based methods to show its superiority over them. The proposed technique is observed to be an efficient and easy method for segmentation of noisy images. (iv) Neighbourhood Attraction Fuzzy c Means Algorithm: A new algorithm based on the IFCM neighbourhood attraction is used without changing the distance function of the FCM and hence avoiding an extra neural network optimization step for the adjusting parameters of the distance function, it is called Neighborhood Atrraction FCM (NAFCM). During clustering, each pixel attempts to attract its neighbouring pixels towards its own cluster. This neighbourhood attraction depends on two factors: the pixel intensities or feature attraction, and the spatial position of the neighbours or distance attraction, which also depends on neighbourhood structure. The NAFCM algorithm is tested on a synthetic image (chapter 6, figure 6.3-6.6) and a number of skin tumor images. It is observed to produce excellent clustering result under high noise condition when compared with the other FCM based clustering methods

The application of Machine Learning for Early Detection of At -Risk Learners in Massive Open Online Courses

With the rapid improvement of digital technology, Massive Open Online Courses (MOOCs) have emerged as powerful open educational learning platforms. MOOCs have been experiencing increased use and popularity in highly ranked universities in recent years. The opportunity to access high-quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a growth in participant numbers. Despite the increasing participation in online courses, the low completion rate has raised major concerns in the literature. Identifying those students who are at-risk of dropping out could be a promising solution in solving the low completion rate in the online setting. Flagging at-risk students could assist the course instructors to bolster the struggling students and provide more learning resources. Although many prior studies have considered the dropout issue in the form of a sequence classification problem, such works only address a limited set of retention factors. They typically consider the learners’ activities as a sequence of weekly intervals, neglecting important learning trajectories. In this PhD thesis, my goal is to investigate retention factors. More specifically, the project seeks to explore the association of motivational trajectories, performance trajectories, engagement levels and latent engagement with the withdrawal rate. To achieve this goal, the first objective is to derive learners’ motivations based on Incentive Motivation theory. The Learning Analytic is utilised to classify student motivation into three main categories; Intrinsically motivated, Extrinsically motivated and Amotivation. Machine learning has been employed to detect the lack of motivation at early stages of the courses. The findings reveal that machine learning provides solutions that are capable of automatically identifying the students’ motivational status according to behaviourism theory. As the second and third objectives, three temporal dropout prediction models are proposed in this research work. The models provide dynamic assessment of the influence of the following factors; motivational trajectories, performance trajectories and latent engagement on students and the subsequent risk of them leaving the course. The models could assist the instructor in delivering more intensive intervention support to at-risk students. Supervised machine learning algorithms have been utilised in each model to identify the students who are in danger of dropping out in a timely manner. The results demonstrate that motivational trajectories and engagement levels are significant factors, which might influence the students’ withdrawal in online settings. On the other hand, the findings indicate that performance trajectories and latent engagement might not prevent students from completing online courses

Disseny i aplicació d’eines quimiomètriques per a l’anàlisi d’imatges hiperespectrals

[cat] Les imatges hiperespectrals són una mesura instrumental singular i de gran interès, ja que proporcionen informació química (espectral) i de distribució espacial (imatge) dels constituents de les mostres. Aquest fet les fa especialment interessants en aplicacions de la indústria farmacèutica, dels camps mediambiental i biomèdic i en la recerca i identificació de materials. L’objectiu d’aquesta tesi ha estat el coneixement de la naturalesa de la mesura de les imatges hiperespectrals amb la finalitat de dissenyar o adaptar eines d’anàlisi de dades més específiques i de proporcionar protocols d’actuació per a la interpretació d’aquest tipus de mesura en funció del tipus de tècnica espectroscòpica utilitzada i del problema químic d’interès. De manera específica, aquest treball s’ha centrat en l’estudi del potencial del mètode de resolució multivariant de corbes per mínims quadrats alternats, MCR-ALS, per a l’anàlisi d’imatges hiperespectrals, que proporciona mapes de distribució i espectres purs dels constituents de les imatges a partir únicament del coneixement de la mesura original. S’ha treballat amb l’anàlisi d’imatges individuals i l’anàlisi conjunta d’imatges obtingudes amb la mateixa tècnica o amb diferents plataformes espectroscòpiques. A partir de l’estudi d’imatges Raman i IR associades a problemes químics de diferents tipologies, s’han proposat protocols d’anàlisi que inclouen el preprocessat de les dades originals, l’obtenció dels mapes de distribució i espectres purs dels constituents de la imatge i el postprocessat dels mapes i espectres resolts per a l’obtenció d’informació addicional. L’ús dels mapes i espectres resolts proporciona informació molt diversa, com és ara la identificació, la quantificació i la caracterització de l’heterogeneïtat dels constituents de la imatge o la interpretació global i local d’un procés. Els mapes resolts han estat també una informació de partida excel·lent en altres tipus d’anàlisi, com la segmentació de la imatge o en procediments de superresolució, orientats a millorar la resolució espacial de les imatges instrumentals. La combinació de l’anàlisi multiconjunt de resolució i segmentació s’ha revelat de gran utilitat per a distingir poblacions de mostres de teixits biològics amb diferents estats patològics. Per últim, s’ha proposat un procediment per a la fusió i anàlisi d’imatges adquirides amb diferents tècniques espectroscòpiques i de diferent resolució espacial mitjançant una nova variant del mètode MCR-ALS per a estructures multiconjunt incompletes, que permet aprofitar la informació complementària de les tècniques acoblades i preservar la màxima resolució espacial.[eng] Hyperspectral images are unique instrumental measurements that contain chemical (spectral) information and detailed knowledge of the distribution of the sample constituents on the sample surface scanned. This thesis is mainly oriented to know in depth the nature of this instrumental measurement in order to design and adapt specific chemometric tools that help in the proposal of general protocols for the interpretation of hyperspectral images according to the spectroscopic technique used and the chemical problem of interest. Particularly, much work has been focused on the study of the potential of multivariate curve resolution-alternating least squares, MCR-ALS, for the analysis of hyperspectral images. This algorithm provides distribution maps and pure spectra for the image constituents from the sole information contained in the raw measurement. Within this framework, individual analysis of images and image multiset analysis on data structures formed by images collected with the same technique or by images coming from different spectroscopic platforms have been explored. From the study of Raman and IR hyperspectral images linked to different chemical problem typologies, data analysis protocols have been proposed that include preprocessing of original data, recovery of distribution maps and pure spectra of image constituents and postprocessing of resolved maps and pure spectra to obtain further information. Resolved distribution maps and pure spectra provide diverse information, such as identification, quantification and heterogeneity characterization of the image constituents or the global and local description of a process. The use of resolved distribution maps has proven to be an excellent starting point for other kinds of analysis, such as image segmentation or super-resolution algorithms, oriented to improve the spatial resolution of experimental hyperspectral images. Combined multiset resolution and segmentation analysis has been shown to be very useful for the differentiation of populations of biological tissues with different pathological conditions. Finally, a strategy for data fusion of hyperspectral images from different spectroscopic platforms and different spatial resolution has been proposed. This approach uses a new variant of MCR-ALS for incomplete multiset structures that takes advantage of the complementary information provided by the different spectroscopic techniques without losing spatial resolution