The strong form of the efficient market hypothesis (EMH) puts forward that it is impossible to achieve better than market results. Yet there are very famous investors, particularly a famous value based investor named Warren Buffett that have achieved better than market returns. The primary objective of this study is to investigate the role of value based investment in generating better than market or superior investment returns. The study was conducted both as a literature study and an empirical study. The objectives of the literature study were threefold. Firstly, to discover value based investment as part of a discussion on investment strategies. Secondly, to investigate the possibility of achieving better than market returns. Lastly, to investigate the role of value based investing in achieving better than market returns. Through the literature study, value based investment parameters were also identified for empirical testing. It was found in the literature that value based investing has a role to play in achieving superior returns. By way of the application of correlation-based research, as well as regression analysis it was found that there is significant statistical evidence to underscore that value based investment parameters can lead to superior returns. / Thesis (M.B.A.)--North-West University, Vaal Triangle Campus, 2010.

The strong form of the efficient market hypothesis (EMH) puts forward that it is impossible to achieve better than market results. Yet there are very famous investors, particularly a famous value based investor named Warren Buffett that have achieved better than market returns. The primary objective of this study is to investigate the role of value based investment in generating better than market or superior investment returns. The study was conducted both as a literature study and an empirical study. The objectives of the literature study were threefold. Firstly, to discover value based investment as part of a discussion on investment strategies. Secondly, to investigate the possibility of achieving better than market returns. Lastly, to investigate the role of value based investing in achieving better than market returns. Through the literature study, value based investment parameters were also identified for empirical testing. It was found in the literature that value based investing has a role to play in achieving superior returns. By way of the application of correlation-based research, as well as regression analysis it was found that there is significant statistical evidence to underscore that value based investment parameters can lead to superior returns. / Thesis (M.B.A.)--North-West University, Vaal Triangle Campus, 2010.

The rapid advances in technologies make the explosive growth of multimedia data possible and available to the public. Multimedia data can be defined as data collection, which is composed of various data types and different representations. Due to the fact that multimedia data carries knowledgeable information, it has been widely adopted to different genera, like surveillance event detection, medical abnormality detection, and many others. To fulfil various requirements for different applications, it is important to effectively classify multimedia data into semantic concepts across multiple domains. In this dissertation, a correlation-based multimedia semantic concept detection framework is seamlessly integrated with the deep learning technique. The framework aims to explore implicit and explicit correlations among features and concepts while adopting different Convolutional Neural Network (CNN) architectures accordingly. First, the Feature Correlation Maximum Spanning Tree (FC-MST) is proposed to remove the redundant and irrelevant features based on the correlations between the features and positive concepts. FC-MST identifies the effective features and decides the initial layer's dimension in CNNs. Second, the Negative-based Sampling method is proposed to alleviate the data imbalance issue by keeping only the representative negative instances in the training process. To adjust dierent sizes of training data, the number of iterations for the CNN is determined adaptively and automatically. Finally, an Indirect Association Rule Mining (IARM) approach and a correlation-based re-ranking method are proposed to reveal the implicit relationships from the correlations among concepts, which are further utilized together with the classification scores to enhance the re-ranking process. The framework is evaluated using two benchmark multimedia data sets, TRECVID and NUS-WIDE, which contain large amounts of multimedia data and various semantic concepts.

There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis. A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service. A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique. For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images. Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification.

This thesis covers with the problems of photogrammetry. It describes the instruments, theoretical background and procedures of acquiring, preprocessing, segmentation of input images and of the depth map calculating. The main content of this thesis is the description of the new method of stereovision. Its algorithm, implementation and evaluation of experiments. The covered method belongs to correlation based methods. The main emphasis lies in the segmentation, which supports the depth map calculation.