Human Motion Recognition through Fuzzy Hidden Markov Model

A new type of Hidden Markov Model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently

Shape-VQ-based lossless hybrid ADPCM/DCT coder

The discrete cosine transform (DCT) has been shown as an optimum encoder for sharp edges in an image (Andrew and Ogunbona, 1997). A conventional lossless coder employing differential pulse code modulation (DPCM) suffers from significant deficiencies in regions of discontinuity, because the simple model cannot capture the edge information. This problem can be partially solved by partitioning the image into blocks that are supposedly statistically stationary. A hybrid lossless adaptive DPCM (ADPCM)/DCT coder is presented, in which the edge blocks are encoded with DCT, and ADPCM is used for the non-edge blocks. The proposed scheme divides each input image into small blocks and classifies them, using shape vector quantisation (VQ), as either edge or smooth. The edge blocks are further vector quantised, and the side information of the coefficient matrix is saved through the shape-VQ index. Evaluation of the compression performance of the proposed method reveals its superiority over other lossless coders

Scalable Multiresolution Image Segmentation and Its Application in Video Object Extraction Algorithm

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for the scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multiresolution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. Application of the spatial segmentation in video segmentation, compared to traditional image/video object extraction algorithms, produces more visually pleasing shape masks at different resolutions which is applicable for object-based video wavelet coding. Moreover it allows for larger motion, better noise tolerance and less computational complexity. In addition to spatial scalability, the proposed algorithm outperforms the standard image/video segmentation algorithms, in both objective and subjective tests

The crisis in ICT education: an academic perspective

A national Discipline-Based Initiative project for ICT, funded by the ALTC, has sought to identify the issues and challenges facing the sector. The crisis in ICT education spans high schools, universities and industry. The demand for skilled ICT graduates is increasing yet enrolments are declining. Several factors contribute to this decline including the perceived quality of teaching and a poor perception of the ICT profession amongst the general public. This paper reports on a consultation process with the academic community. Academic concerns include the capacity of the sector to survive the downturn, and improving relationships with industry which should benefit students, academics and industry. An outcome of the consultation process has been the formation of the Australian Council of Deans of ICT (ACDICT) which will have broad responsibility for addressing the issues affecting ICT higher education

Skill acquisition in transfer of manipulation skills from human to machine through a haptic virtual environment

A new paradigm for programming a robotics manipulator is developed. It is intended that the teaching of the machine will begin with the necessary skills being demonstrated by the human operator in a virtual environment with tactile sensing (haptics). Position and contact force and torque data generated in the virtual environment combined with a priori knowledge about the task is used to identify and learn the skills in the newly demonstrated tasks and then to reproduce them in the robotics system. The peg-in-hole insertion problem is used as a case study. The overall concept is described. The methodologies developed to build the virtual environment and to learn the basic skills are explained. The results obtained so far are presented

Application of Competitive Clustering to Acquisition of Human Manipulation Skills

The work carried out to explore the feasibility of reconstructing human constrained motion manipulation skills is reported. This is achieved by tracing and learning the manipulation performed by a human operator in a haptic rendered virtual environment. The peg-in-hole insertion problem is used as a case study. In the developed system, position and contact force and torque as well as orientation data generated in the haptic rendered virtual environment combined with a priori knowledge about the task are used to identify and learn the skills in the newly demonstrated task. The data obtained from the virtual environment is classified into different cluster sets using a competitive fuzzy clustering algorithm called Competitive Agglomeration (CA). The CA algorithm starts with an over specified number of clusters which compete for feature points in the training procedure. Clusters with small cardinalities lose the competition and gradually vanish. The optimal number of clusters that win the competition is eventually determined. The clusters in the optimum cluster set are tuned using Locally Weighted Regression (LWR) to produce prediction models for robot trajectory performing the physical assembly based on the force/position information received from the rig. A background on the work and its significance is provided. The approach developed is explained and the results obtained so far are presented

Virtual cochlear implant insertion for medical education

A surgical simulator has been developed for the purpose of training otologists in cochlear implantation. The simulation provides real-time visual and haptic feedback during implant insertion into the human Scala Tympani (ST). The benefits and possible outcomes for this type of simulator are presented. Methods for model generation are discussed, for anatomical and prosthetic structures used in the simulation. Development of the interactive model with force-feedback is presented, with results

Scalable multiresolution color image segmentation with smoothness constraint

This paper presents a multiresolution image segmentation method based on the discrete wavelet transform and Markov random field (MRF) modeling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it applicable for scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multire solution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Examining the corresponding pixels at different resolutions simultaneously enables the algorithm to directly segment the images in the YUV or similar color spaces where luminance is in full resolution and chrominance components are at half resolution. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. In addition to spatial scalability, the proposed algorithm outperforms the standard single and multire solution segmentation algorithms, in both objective and subjective tests

Postgraduate and undergraduate mechatronics\u27 courses at the University of Wollongong

This paper outlines the University\u27s involvement with industry based manufacturing projects, and how this has lead to the recent establishment of postgraduate and undergraduate mechatronics degrees. The nature of the industrial projects is described with examples of specific problems, test equipment and experimental rigs given. This work can be used to explain the reasons for the design of the mechatronics courses at Wollongong. A new teaching methodology particularly suitable for mechatronic\u27s education is also discussed