Lik-Kwan Shark

Study of hand-dorsa vein recognition

A new hand-dorsa vein recognition method based on Partition Local Binary Pattern (PLBP) is presented in this paper. The proposed method employs hand-dorsa vein images acquired from a low-cost, near infrared device. After preprocessing, the image is divided into sub-images. LBP uniform pattern features are extracted from all the sub-images, which are combined to form the feature vector for token vein texture features. The method is assessed using a similarity measure obtained by calculating the Chi square statistic between the feature vectors of the tested sample and the target sample. Integral histogram method, original LBP and Partition LBP with 16, 32, 64 sub-images are tested on a database of 2040 images from 102 individuals built up by a custom-made acquisition device. The experimental results show that Partition LBP performs better than original LBP, Circular Partition LBP performs better than Rectangular Partition LBP, and when the image was divided into 32 performs better than others.

A Novel Medical Image Segmentation Method using Dynamic Programming

A novel method is proposed to segment objects in medical images whose boundaries can be described as closed curves. Based on an image with the enhanced boundary of an object of interest, the segmentation method consists of three key steps, namely, the polar transformation, dynamic programming and curve fitting. A 3D object in volumetric data can be segmented on a slice-by-slice basis by only specifying one point inside the 3D object of interest as the pole for the polar transformation. The method is also shown to be able to segment objects with very weak boundaries.

An Efficient Feature Based Matching Algorithm for Stereo Images

A novel efficient feature based stereo matching algorithm is presented in this paper. The proposed method links the detected feature points into chains and the matching process is achieved by comparing some of the feature points from different chains. A matching score based on 2 dimensional normalised cross correlation (2D NCC) is used to determine whether feature points are well matched to construct a feature correspondence. This process improves the reliability and the efficiency of the algorithm by concentrating on matching corresponding chains. The proposed method is tested and validated using real scenes and synthetic data images. Experimental results indicate that this novel algorithm is more reliable especially for images in which a number of vertical features are detected. It also compares well with existing methods in terms of speed of execution

Hand vein recognition based on multiple keypoints sets

Biometric authentication based on hand vein patterns has grown in popularity as a way to confirm personal identity. However, the imaging quality and variability of the vein images acquired by the near-infrared (NIR) device present challenges to achieve high classification accuracy. In this paper, a novel method for hand vein recognition by fusion of multiple sets of keypoints from the scale-invariant feature transform (SIFT) is proposed. While the use of SIFT enables classification to be unaffected by imaging quality and variability, the fusion reduces information redundancies and improves the discrimination power. The proposed method is tested on a database of 2040 images, and the experiment results show a good classification performance with a result of 97.95% recognition rate.

A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data

The paper presents a computationally efficient 3D-2D image registration algorithm for automatic pre-treatment validation in radiotherapy. The novel aspects of the algorithm include (a) a hybrid cost function based on partial digitally reconstructed radiographs (DRRs) generated along projected anatomical contours and a level set term for similarity measurement; and (b) a fast search method based on parabola fitting and sensitivity-based search order. Using CT and orthogonal x-ray images from a skull and a pelvis phantom, the proposed algorithm is compared with the conventional ray-casting full DRR based registration method. Not only is the algorithm shown to be computationally more efficient with registration time being reduced by a factor of 8, but also the algorithm is shown to offer 50% higher capture range allowing the initial patient displacement up to 15 mm (measured by mean target registration error). For the simulated data, high registration accuracy with average errors of 0.53 mm +/- 0.12 mm for translation and 0.61 +/- 0.29 degrees for rotation within the capture range has been achieved. For the tested phantom data, the algorithm has also shown to be robust without being affected by artificial markers in the image.

EMG Biofeedback Based VR System for Hand Rotation and Grasping Rehabilitation

Individuals who have upper limb movement problems include people with cerebral palsy (CP) and stroke victims. Both these conditions lead to difficulties in daily activities such as reaching, grasping etc. Virtual reality (VR), which could provide a repetitive multimodal task-oriented rehabilitation environment for patients to undertake self-training in safety, is considered to be a suitable tool for medical health rehabilitation. Using electromyography (EMG) biofeedback in rehabilitation could provide patients with opportunities to improve the ability by assessing their muscle activity response and learning self-control of movement during specific training tasks. This paper presents a study on developing EMG as an important interactive tool in a VR based system for hand rotation and grasping motion rehabilitation. The input interface includes an EMG system and a real-time magnetic motion tracking system, and the output interface is a PC monitor. The developed EMG biofeedback based VR system enables the user to interact with virtual objects in real-time with multiform feedback. Ten healthy subjects participated in the preliminary task evaluation test, and the results suggest that the specified skills have improved during training. The beneficial effects of the developed system indicate the potential values for further clinical application.

Immersive manipulation of virtual objects through glove-based hand gesture interaction

Immersive visualisation is increasingly being used for comprehensive and rapid analysis of objects in 3D and object dynamic behaviour in 4D. Challenges are therefore presented to provide natural user interaction to enable effortless virtual object manipulation. Presented in this paper is the development and evaluation of an immersive human–computer interaction system based on stereoscopic viewing and natural hand gestures. For the development, it is based on the integration of a back-projection stereoscopic system for object and hand display, a hybrid inertial and ultrasonic tracking system to provide the absolute positions and orientations of the user’s head and hands, as well as a pair of high degrees-of-freedom data gloves to provide the relative positions and orientations of digit joints and tips on both hands. For the evaluation, it is based on a two-object scene with a virtual cube and a CT (computed tomography) volume created for demonstration of real-time immersive object manipulation. The system is shown to provide a correct user view of objects and hands in 3D with depth, as well as to enable a user to use a number of simple hand gestures to perform basic object manipulation tasks involving selection, release, translation, rotation and scaling. Also included in the evaluation are some quantitative tests of the system performance in terms of speed and latency.

Discovering differences in acoustic emission between healthy and osteoarthritic knees using a four-phase model of sit-stand-sit movements.

By performing repeated sit-stand-sit movements to create stress on knee joints, short transient bursts of high frequency acoustic emission (AE) released by the knee joints were acquired from two age matched groups consisting of healthy and osteoarthritic (OA) knees, and significant differences between these two groups were discovered from the signal analysis performed. The analysis is based on a four-phase model of sit-stand-sit movements and a two-feature descriptor of AE bursts. The four phases are derived from joint angle measurement during movement, and they consist of the ascending-acceleration and ascending-deceleration phases in the sit-to-stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. The two features are extracted from AE measurement during movement, and they consist of the peak magnitude value and average signal level of each AE burst. The proposed analysis method is shown to provide a high sensitivity for differentiation of the two age matched healthy and OA groups, with the most significant difference found to come from the peak magnitude value in the ascending-deceleration phase, clear quantity and strength differences in the image based visual display of their AE feature profiles due to substantially more AE bursts from OA knee joints with higher peak magnitude values and higher average signal levels, and two well separated clusters in the space formed by the principal components. These results provide ample support for further development of AE as a novel tool to facilitate dynamic integrity assessment of knee joints in clinic and home settings.

Real-Time Immersive Table Tennis Game for Two Players with Motion Tracking

Presented in this paper is a novel real-time virtual reality game developed to enable two participants to play table tennis immersively with each other’s avatar in a shared virtual environment. It uses a wireless hybrid inertial and ultrasonic tracking system to provide the positions and orientations of both the head (view point) and hand (racket) of each player, as well as two large rear-projection stereoscopic screens to provide a view-dependent 3D display of the game environment. Additionally, a physics-based ball animation model is designed for the game, which includes fast detection of the ball colliding with table, net and quick moving rackets. The system is shown to offer some unique features and form a good platform for development of other immersive games for multiple players.

Dynamic Hand Gesture Tracking and Recognition for Real-Time Immersive Virtual Object Manipulation

Immersive visualisation is increasingly being used for comprehensive and rapid analysis of objects in 3D and object dynamic behaviour in 4D. Challenges are therefore presented to provide natural user interaction to enable effortless virtual object manipulation. Presented in this paper is the development and evaluation of a human-computer interaction system based on natural hand gestures. By employing a hybrid inertial and ultrasonic tracking system to provide the absolute positions and orientations of the user’s head and hands as well as a pair of high degrees-of-freedoms data gloves to provide the relative positions and orientations of finger joints and tips in both hands, the proposed system is shown to be able to automatically track and recognise a number of simple hand gestures. The effectiveness and potential of the proposed system is demonstrated through the five basic object manipulation tasks involving selection, release, translation, rotation and scaling of a 3D virtual cube.