Huiyu Zhou

Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.

Reducing Drifts in the Inertial Measurements of Wrist and Elbow Positions

In this paper, we present an inertial-sensor-based monitoring system for measuring the movement of human upper limbs. Two wearable inertial sensors are placed near the wrist and elbow joints, respectively. The measurement drift in segment orientation is dramatically reduced after a Kalman filter is applied to estimate inclinations using accelerations and turning rates from gyroscopes. Using premeasured lengths of the upper and lower arms, we compute the position of the wrist and elbow joints via a proposed kinematic model. Experimental results demonstrate that this new motion capture system, in comparison to an optical motion tracker, possesses an RMS position error of less than 0.009 m, with a drift of less than 0.005 ms-1 in five daily activities. In addition, the RMS angle error is less than 3°. This indicates that the proposed approach has performed well in terms of accuracy and reliability.

Fuzzy clustering for colour reduction in images

The aim of colour quantisation is to reduce the number of distinct colour in images while preserving a high colour fidelity as compared to the original images. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper we investigate the performance of various fuzzy c-means clustering algorithms for colour quantisation of images. In particular, we use conventional fuzzy c-means as well as some more efficient variants thereof, namely fast fuzzy c-means with random sampling, fast generalised fuzzy c-means, and a recently introduced anisotropic mean shift based fuzzy c-means algorithm. Experimental results show that fuzzy c-means performs significantly better than other, purpose built colour quantisation algorithms, and also confirm that the fast fuzzy clustering algorithms provide similar quantisation results to the full conventional fuzzy c-means approach.

An interactive Internet-based system for tracking upper limb motion in home-based rehabilitation

In this paper, we introduce an interactive telecommunication system that supports video/audio signal acquisition, data processing, transmission, and 3D animation for post stroke rehabilitation. It is designed for stroke patients to use in their homes. It records motion exercise data, and immediately transfers this data to hospitals via the internet. A real-time videoconferencing interface is adopted for patients to observe therapy instructions from therapists. The system uses a peer-to-peer network architecture, without the need for a server. This is a potentially effective approach to reducing costs, allowing easy setup and permitting group-rehabilitation sessions. We evaluate this system using the following steps: (1) motion detection in different movement patterns, such as reach, drink, and reach-flexion; (2) online bidirectional visual telecommunication; and (3) 3D rendering using a proposed offline animation package. This evaluation has subjectively been proved to be optimal.

Combining Perceptual Features With Diffusion Distance for Face Recognition

Face recognition and identification is a very active research area nowadays due to its importance in both human computer and social interaction. Psychological studies suggest that face recognition by human beings can be featural, configurational, and holistic. In this paper, by incorporating spatially structured features into a histogram-based face-recognition framework, we intend to pursue consistent performance of face recognition. In our proposed approach, while diffusion distance is computed over a pair of human face images, the shape descriptions of these images are built using Gabor filters that consist of a number of scales and levels. It demonstrates that the use of perceptual features by Gabor filtering in combination with diffusion distance enables the system performance to be significantly improved, compared to several classical algorithms. The oriented Gabor filters lead to discriminative image representations that are then used to classify human faces in the database.

A mean shift based fuzzy c-means algorithm for image segmentation

Image segmentation is an important task in many medical applications. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. C-means based approaches, in particular fuzzy c-means has been shown to work well for clustering based segmentation, however due to the iterative nature are also computationally complex. In this paper we introduce a new mean shift based fuzzy c-means algorithm that we show to be faster than previous techniques while providing good segmentation performance. The proposed clustering method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centres, the entire strategy is capable of optimally segmenting clusters within an image.

Feature extraction and clustering for dynamic video summarisation

In this paper an effective dynamic video summarisation algorithm is presented using audio-visual features extracted from videos. Audio, colour and motion features are dynamically fused using an adaptively weighting mechanism. Dissimilarities of temporal video segments are formulated using the extracted features before these segments are clustered using a fuzzy c-means algorithm with an optimally determined cluster number. The experimental results demonstrate the ability of the proposed algorithm to automatically summarise the videos with good performance.

Application of semantic features in face recognition

We propose a new face recognition strategy, which integrates the extraction of semantic features from faces with tensor subspace analysis. The semantic features consist of the eyes and mouth, plus the region outlined by the centers of the three components. A new objective function is generated to fuse the semantic and tensor models for finding similarity between a face and its counterpart in the database. Furthermore, singular value decomposition is used to solve the eigenvector problem in the tensor subspace analysis and to project the geometrical properties to the face manifold. Experimental results demonstrate that the proposed semantic feature-based face recognition algorithm has favorable performance with more accurate convergence and less computational efforts.

Adaptive fusion of particle filtering and spatio-temporal motion energy for human tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

Classification of Upper Limb Motion Trajectories Using Shape Features

To understand and interpret human motion is a very active research area nowadays because of its importance in sports sciences, health care, and video surveillance. However, classification of human motion patterns is still a challenging topic because of the variations in kinetics and kinematics of human movements. In this paper, we present a novel algorithm for automatic classification of motion trajectories of human upper limbs. The proposed scheme starts from transforming 3-D positions and rotations of the shoulder/elbow/wrist joints into 2-D trajectories. Discriminative features of these 2-D trajectories are, then, extracted using a probabilistic shape-context method. Afterward, these features are classified using a k-means clustering algorithm. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art techniques.