In search of Leonardo: Computer-based facial image analysis of Renaissance artworks for identifying Leonardo as subject

One of the enduring mysteries in the history of the Renaissance is the adult appearance of the archetypical "Renaissance Man," Leonardo da Vinci. His only acknowledged self-portrait is from an advanced age, and various candidate images of younger men are difficult to assess given the absence of documentary evidence. One clue about Leonardo's appearance comes from the remark of the contemporary historian, Vasari, that the sculpture of David by Leonardo's master, Andrea del Verrocchio, was based on the appearance of Leonardo when he was an apprentice. Taking a cue from this statement, we suggest that the more mature sculpture of St. Thomas, also by Verrocchio, might also have been a portrait of Leonardo. We tested the possibility Leonardo was the subject for Verrocchio's sculpture by a novel computational technique for the comparison of three-dimensional facial configurations. Based on quantitative measures of similarities, we also assess whether another pair of candidate two-dimensional images are plausibly attributable as being portraits of Leonardo as a young adult. Our results are consistent with the claim Leonardo is indeed the subject in these works, but we need comparisons with images in a larger corpora of candidate artworks before our results achieve statistical significance

South African sign language recognition using feature vectors and Hidden Markov Models

Masters of ScienceThis thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.South Afric

A study on detection of risk factors of a toddler’s fall injuries using visual dynamic motion cues

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The research in this thesis is intended to aid caregivers’ supervision of toddlers to prevent accidental injuries, especially injuries due to falls in the home environment. There have been very few attempts to develop an automatic system to tackle young children’s accidents despite the fact that they are particularly vulnerable to home accidents and a caregiver cannot give continuous supervision. Vision-based analysis methods have been developed to recognise toddlers’ fall risk factors related to changes in their behaviour or environment. First of all, suggestions to prevent fall events of young children at home were collected from well-known organisations for child safety. A large number of fall records of toddlers who had sought treatment at a hospital were analysed to identify a toddler’s fall risk factors. The factors include clutter being a tripping or slipping hazard on the floor and a toddler moving around or climbing furniture or room structures. The major technical problem in detecting the risk factors is to classify foreground objects into human and non-human, and novel approaches have been proposed for the classification. Unlike most existing studies, which focus on human appearance such as skin colour for human detection, the approaches addressed in this thesis use cues related to dynamic motions. The first cue is based on the fact that there is relative motion between human body parts while typical indoor clutter does not have such parts with diverse motions. In addition, other motion cues are employed to differentiate a human from a pet since a pet also moves its parts diversely. They are angle changes of ellipse fitted to each object and history of its actual heights to capture the various posture changes and different body size of pets. The methods work well as long as foreground regions are correctly segmented

Stretching the Vitruvian Man: Investigating Affective and Representational Arts-based Methodologies Towards Theorizing a More Humanistic Model of Medicine

Westernized medicine can be said to illustrate its history and structure, as well as its current understanding of the capacity and appearance of the human through its visual representations of the body. Scientific images, this paper argues, become a site for interrogating the tangle of idealism, truth, objectivity and knowledge in how knowledge is actively used, replicated, paralleled and otherwise functions. First, asking how depictions of the medicalized body inform the epistemological foundations of medicine, and to what end, this work opens up the question of methodology, arguing that the integration of the modes of arts-based practices can bring medicine toward a much more realistic picture of the world. A parallel argument is a similarly concentrated interrogation of the affective quality of arts-based methodology, which is commonly understood to be the nucleus of work on the political dimensions of non-representational theory. I complicate the dominant scholarly preference for an ontologically rooted affect theory, finding it theoretically non-viable for art and humanistic medicine by thinking through subjectivity, autobiographical accounts of illness and epistemological flexibility. I see a path forward using a biologically and evolutionarily rooted affect theory, noting the ethical implications of its differences for a humanistic approach to medicine

Design Of Computer Vision Systems For Optimizing The Threat Detection Accuracy

This dissertation considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. The dissertation presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy. Additionally, the dissertation features an improved neural network system for object detection. The proposed system considers inherent video characteristics and employs different motion detection and clustering algorithms to focus on the areas of importance in consecutive frames, allowing the system to dynamically and efficiently distribute the detection task among multiple deployments of object detection neural networks. Our experimental results indicate that our proposed method can enhance the mAP (mean average precision), execution time, and required data transmissions to object detection networks. Finally, as recognizing an activity provides significant automation prospects in CV systems, the dissertation presents an efficient activity-detection recurrent neural network that utilizes fast pose/limbs estimation approaches. By combining object detection with pose estimation, the domain of activity detection is shifted from a volume of RGB (Red, Green, and Blue) pixel values to a time-series of relatively small one-dimensional arrays, thereby allowing the activity detection system to take advantage of highly capable neural networks that have been trained on large GPU clusters for thousands of hours. Consequently, capable activity detection systems with considerably fewer training sets and processing hours can be built