As the clothing industry moves away from traditional models of mass production there has been increased interest towards customised clothing. The technology to produce cost effective customised clothing is already in place however the prerequisite to customised clothing is accurate body dimensional data. In response, image capture systems have been developed which are capable of recording a three-dimensional image of the body, from which measurements and shape information may be extracted. The use of these systems for customised clothing has, to date, been limited due to issues of inaccuracy, cost and portability. To address the issue of inaccuracy a diagnostic procedure has been developed through the performance evaluation of an image capture system. By systematically evaluating physical and instrumental parameters the more relevant sources of potential error were identified and quantified and subsequently corrected to form a `closed loop' experimental procedure. A systematic test procedure is therefore presented which may be universally applied to image capture systems working on the same principle. The methodology was based upon the isolation and subsequent testing of variables that were thought to be potential sources of error. The process therefore included altering the physical parameters of the target object in relation to the image capture system and amending the configuration and calibration settings within the system. From the evaluation the most relevant sources of error were identified as the cosine effect, measurement point displacement, the dimensional differences between views and the influence of the operator in measurement. The test procedure proved to be effective in both evaluating the performance of the system under investigation and in enabling the quantification of errors. Both random and systematic errors were noted which may be quantified or corrected to enable improved accuracy in the measured results. Recommendations have been made for the improvement of the performance of the current image capture system these include the integration of a cosine effect correction algorithm and suggestions for the automation of the image alignment process. The limitations of the system such as its reliance on manual intervention for both the measurement and stitching processes, are discussed, as is its suitability for providing dimensional information for bespoke clothing production. Recommendations are also made for the creation of an automated test procedure for testing the performance of alternative image capture systems, which involves evaluating the accuracy of object replication both for multiple and single image capture units using calibration objects which combine a range of surfaces.

published_or_final_version / Computer Science / Master / Master of Philosophy

Although handwritten signature verification has been extensively researched, it has not achieved optimum accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). Two different feature extraction techniques were used, namely the Enhanced Modified Direction Feature (EMDF) and the Local Directional Pattern feature (LDP). These were used to analyze the effect of using several different distance-based classification techniques. Among the classification techniques used, are the cosine similarity measure, Mahalanobis, Canberra, Manhattan, Euclidean, weighted Euclidean and fractional distances. Additionally, the novel weighted fractional distances, as well as locally optimized resampling of feature vector sizes were tested. The best accuracy was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. This combination of multiple distance-based classification techniques achieved accuracy rate of 89.2% when using the EMDF feature extraction technique, and 90.8% when using the LDP feature extraction technique. These results are comparable to those in literature, where the same feature extraction techniques were classified with SLTs. The best of the distance-based classification techniques were found to produce greater accuracy than the SLTs. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

Naval Postgraduate School's (NPS) Tactical Network Topology (TNT) experiments seek to develop, implement and identify sensor-unmanned vehicle network, and network-centric operations to assist DoD warfighters in the Global War on Terrorism (GWOT). Using biometric data for rapid identification of High Value Targets (HVT) in ground and Maritiime Interdiction Operations (MIO) is critical to the emeging special operations concept. The goal is to explore solutions and operational constraints associated with biometric data analysis and rapid identification by means of adhoc self forming sensor unmanned vehicle (UV) wireless networks. The objectives of this thesis are to look at how biometrics has performed in a testbed environment that is simulating a real special operations environment in theatre. This thesis is meant to explore and explain the biometrics process that was conducted on top of the tactical network and evaluate its performance. This thesis provided the process model for biometrics identification in the tactical networks environment. This thesis also evaluated the length of time that it took to transmit the fingerprint data from the field to the ABIS databvase, with an identification result then sent back to the field. The longest time that was observed was 70 minutes (using low bandwidth Satellite communications), while the shortest time was 4 minutes for reachback to ABIS and 2 minutes for a local database.

The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object in wide video scene. The sequence of the positions of each moving point can be used to generate a trajectory containing both spatial and temporal information of object's movement. In this study, we investigate how the relationship between two trajectories can be used to recognize multi-agent interactions. For this purpose, we present a simple set of qualitative atomic disjoint trajectory-segment relations which can be utilized to represent the relationships between two trajectories. Given a pair of adjacent concurrent trajectories, we segment the trajectory pair to get the ordered sequence of related trajectory-segments. Each pair of corresponding trajectory-segments then is assigned a token associated with the trajectory-segment relation, which leads to the generation of a string called a pairwise trajectory-segment relationship sequence. From a group of pairwise trajectory-segment relationship sequences, we utilize an unsupervised learning algorithm, particularly the k-medians clustering, to detect interesting patterns that can be used to classify lower-level multi-agent activities. We evaluate the effectiveness of the proposed approach by comparing the activity classes predicted by our method to the actual classes from the ground-truth set obtained using the crowdsourcing technique. The results show that the relationships between a pair of trajectories can signify the low-level multi-agent activities.

The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black shapes on the white background.

D.Ing. / This thesis deals with an investigation into facial recognition and some variables that influence the performance of such a system. Firstly there is an investigation into the influence of image variability on the overall recognition performance of a system and secondly the performance and subsequent suitability of a neural network based system is tested. Both tests are carried out on two distinctly different databases, one more variable than the other. The results indicate that the greater the amount of variability the more negatively affected is the performance rating of a specific facial recognition system. The results further indicate the success with the implementation of a neural network system over a more conventional statistical system.

Thesis (M.Sc.(Electrical Engineering))--University of the Witwatersrand, Faculty of Engineering, 1992.

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg, August 31, 2015. / Visual password schemes can be considered as an alternative to alphanumeric passwords. Studies have shown that alphanumeric passwords can, amongst others, be eavesdropped, shoulder surfed, or guessed, and are susceptible to brute force automated attacks. Visual password schemes use images, in place of alphanumeric characters, for authentication. For example, users of visual password schemes either select images (Cognometric) or points on an image (Locimetric) or attempt to redraw their password image (Drawmetric), in order to gain authentication. Visual passwords are limited by the so-called password space, i.e., by the size of the alphabet from which users can draw to create a password and by susceptibility to stealing of passimages by someone looking over your shoulders, referred to as shoulder surfing in the literature. The use of automatically generated highly similar abstract images defeats shoulder surfing and means that an almost unlimited pool of images is available for use in a visual password scheme, thus also overcoming the issue of limited potential password space. This research investigated visual password schemes. In particular, this study looked at the possibility of using tree picture grammars to generate abstract graphics for use in a visual password scheme. In this work, we also took a look at how humans determine similarity of abstract computer generated images, referred to as perceptual similarity in the literature. We drew on the psychological idea of similarity and matched that as closely as possible with a mathematical measure of image similarity, using Content Based Image Retrieval (CBIR) and tree edit distance measures. To this end, an online similarity survey was conducted with respondents ordering answer images in order of similarity to question images, involving 661 respondents and 50 images. The survey images were also compared with eight, state of the art, computer based similarity measures to determine how closely they model perceptual similarity. Since all the images were generated with tree grammars, the most popular measure of tree similarity, the tree edit distance, was also used to compare the images. Eight different types of tree edit distance measures were used in order to cover the broad range of tree edit distance and tree edit distance approximation methods. All the computer based similarity methods were then correlated with the online similarity survey results, to determine which ones more closely model perceptual similarity. The results were then analysed in the light of some modern psychological theories of perceptual similarity. This work represents a novel approach to the Passfaces type of visual password schemes using dynamically generated pass-images and their highly similar distractors, instead of static pictures stored in an online database. The results of the online survey were then accurately modelled using the most suitable tree edit distance measure, in order to automate the determination of similarity of our generated distractor images. The information gathered from our various experiments was then used in the design of a prototype visual password scheme. The generated images were similar, but not identical, in order to defeat shoulder surfing. This approach overcomes the following problems with this category of visual password schemes: shoulder surfing, bias in image selection, selection of easy to guess pictures and infrastructural limitations like large picture databases, network speed and database security issues. The resulting prototype developed is highly secure, resilient to shoulder surfing and easy for humans to use, and overcomes the aforementioned limitations in this category of visual password schemes.

Object recognition is imperfect; often incomplete processing or deprived information yield misperceptions (i.e., misidentification) of objects. While quickly rectified and typically benign, instances of such errors can produce dangerous consequences (e.g., police shootings). Through a series of experiments, this study examined the competitive process of multiple object interpretations (candidates) during the earlier stages of object recognition process using a lexical decision task paradigm. Participants encountered low-pass filtered objects that were previously demonstrated to evoke multiple responses: a highly frequented interpretation (“primary candidates”) and a lesser frequented interpretation (“secondary candidates”). When objects were presented without context, no facilitative effects were observed for primary candidates. However, secondary candidates demonstrated evidence for being actively suppressed. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection