Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach

Statistical modelling for facial expression dynamics

PhDOne of the most powerful and fastest means of relaying emotions between humans are facial expressions. The ability to capture, understand and mimic those emotions and their underlying dynamics in the synthetic counterpart is a challenging task because of the complexity of human emotions, different ways of conveying them, non-linearities caused by facial feature and head motion, and the ever critical eye of the viewer. This thesis sets out to address some of the limitations of existing techniques by investigating three components of expression modelling and parameterisation framework: (1) Feature and expression manifold representation, (2) Pose estimation, and (3) Expression dynamics modelling and their parameterisation for the purpose of driving a synthetic head avatar. First, we introduce a hierarchical representation based on the Point Distribution Model (PDM). Holistic representations imply that non-linearities caused by the motion of facial features, and intrafeature correlations are implicitly embedded and hence have to be accounted for in the resulting expression space. Also such representations require large training datasets to account for all possible variations. To address those shortcomings, and to provide a basis for learning more subtle, localised variations, our representation consists of tree-like structure where a holistic root component is decomposed into leaves containing the jaw outline, each of the eye and eyebrows and the mouth. Each of the hierarchical components is modelled according to its intrinsic functionality, rather than the final, holistic expression label. Secondly, we introduce a statistical approach for capturing an underlying low-dimension expression manifold by utilising components of the previously defined hierarchical representation. As Principal Component Analysis (PCA) based approaches cannot reliably capture variations caused by large facial feature changes because of its linear nature, the underlying dynamics manifold for each of the hierarchical components is modelled using a Hierarchical Latent Variable Model (HLVM) approach. Whilst retaining PCA properties, such a model introduces a probability density model which can deal with missing or incomplete data and allows discovery of internal within cluster structures. All of the model parameters and underlying density model are automatically estimated during the training stage. We investigate the usefulness of such a model to larger and unseen datasets. Thirdly, we extend the concept of HLVM model to pose estimation to address the non-linear shape deformations and definition of the plausible pose space caused by large head motion. Since our head rarely stays still, and its movements are intrinsically connected with the way we perceive and understand the expressions, pose information is an integral part of their dynamics. The proposed 3 approach integrates into our existing hierarchical representation model. It is learned using sparse and discreetly sampled training dataset, and generalises to a larger and continuous view-sphere. Finally, we introduce a framework that models and extracts expression dynamics. In existing frameworks, explicit definition of expression intensity and pose information, is often overlooked, although usually implicitly embedded in the underlying representation. We investigate modelling of the expression dynamics based on use of static information only, and focus on its sufficiency for the task at hand. We compare a rule-based method that utilises the existing latent structure and provides a fusion of different components with holistic and Bayesian Network (BN) approaches. An Active Appearance Model (AAM) based tracker is used to extract relevant information from input sequences. Such information is subsequently used to define the parametric structure of the underlying expression dynamics. We demonstrate that such information can be utilised to animate a synthetic head avatar. Submitte

Hybrid learning-based model for exaggeration style of facial caricature

Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeration style of an artist is difficult to be coded in algorithmic method. Fortunately, artificial neural network, which possesses self-learning and generalization ability, has shown great promise in addressing the problem of capturing and learning an artist’s style to predict a facial caricature. However, one of the main issues faced by this study is inconsistent artist style due to human factors and limited collection on image-caricature pair data. Thus, this study proposes facial caricature dataset preparation process to get good quality dataset which captures the artist’s exaggeration style and a hybrid model to generalize the inconsistent style so that a better, more accurate prediction can be obtained even using small amount of dataset. The proposed data preparation process involves facial features parameter extraction based on landmark-based geometric morphometric and modified data normalization method based on Procrustes superimposition method. The proposed hybrid model (BP-GANN) combines Backpropagation Neural Network (BPNN) and Genetic Algorithm Neural Network (GANN). The experimental result shows that the proposed hybrid BP-GANN model is outperform the traditional hybrid GA-BPNN model, individual BPNN model and individual GANN model. The modified Procrustes superimposition method also produces a better quality dataset than the original one

Spatiotemporal analysis of human actions using RGB-D cameras

Markerless human motion analysis has strong potential to provide cost-efficient solution for action recognition and body pose estimation. Many applications including humancomputer interaction, video surveillance, content-based video indexing, and automatic annotation among others will benefit from a robust solution to these problems. Depth sensing technologies in recent years have positively changed the climate of the automated vision-based human action recognition problem, deemed to be very difficult due to the various ambiguities inherent to conventional video. In this work, first a large set of invariant spatiotemporal features is extracted from skeleton joints (retrieved from depth sensor) in motion and evaluated as baseline performance. Next we introduce a discriminative Random Decision Forest-based feature selection framework capable of reaching impressive action recognition performance when combined with a linear SVM classifier. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). The approach can also be used to provide insights on the spatiotemporal dynamics of human actions. A novel therapeutic action recognition dataset (WorkoutSU-10) is presented. We took advantage of this dataset as a benchmark in our tests to evaluate the reliability of our proposed methods. Recently the dataset has been published publically as a contribution to the action recognition community. In addition, an interactive action evaluation application is developed by utilizing the proposed methods to help with real life problems such as 'fall detection' in the elderly people or automated therapy program for patients with motor disabilities

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Generating 3D product design models in real-time using hand motion and gesture

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Three dimensional product design models are widely used in conceptual design and in the early stage of prototyping during the design processes. A product design specification often demands a substantial amount of 3D models to be constructed within a short period of time. Current methods begin with designers sketching product concepts in 2D using pencil and paper, which in turn are then translated into 3D models by a design individual with CAD expertise, using a 3D modelling software package such as Pro Engineer, Solid Works, Auto CAD etc. Several novel methods have been used to incorporate hand motion as a way of interacting with computers. There are three main types of technology available to capture motion data, capable of translating human motion into numeric data which can be read by a computer system. The first being, hand gesture glove-based systems such as “Cyberglove”, these systems are generally used to capture hand gesture and joint angle information. The second is full body motion capture systems, optical and non-optical-based, and finally vision based gesture recognition systems which capture full degree of - freedom (DOF) hand motion estimation. There has yet to be a method using any of the above mentioned input devices to rapidly produce 3D product design models in real time, using hand motion and gestures. In this research, a novel method is presented, using a motion capture system to capture hand gestures and motion in real time, to recreate 3D curves and surfaces, which can be translated into 3D product design models. The main aim of this research is to develop a hand motion and gesture-based rapid 3D product modelling method, allowing designers to interactively sketch out 3D concepts in real time using a virtual workspace. A database of a number of hand signs was built for both architectural hand signs (preliminary study) and Product Design hand signs. A marker set model with a total of eight markers (five on the left hand and three on right hand/marker pen) was designed and used in the capture of hand gestures with the use of an Optical Motion Capture System. A preliminary testing session was successfully completed to determine whether the Motion Capture system would be suitable for a real-time application, by effectively modelling a train station in an offline state using hand motion and gesture. An OpenGL software application was programmed using C++ and the Microsoft Foundation Classes which was used to communicate and pass information of captured motion from the EVaRT system to the user