Shu-Fai Wong

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We introduce a new framework, namely tensor canonical correlation analysis (TCCA) which is an extension of classical canonical correlation analysis (CCA) to multidimensional data arrays (or tensors) and apply this for action/gesture classification in videos. By tensor CCA, joint space-time linear relationships of two video volumes are inspected to yield(More)
Local spatiotemporal features or interest points provide compact but descriptive representations for efficient video analysis and motion recognition. Current local feature extraction approaches involve either local filtering or entropy computation which ignore global information (e.g. large blobs of moving pixels) in video inputs. This paper presents a(More)
Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotemporal interest points (part-based approach). Holistic approaches tend to be more sensitive to noise e.g. geometric variations, while part-based approaches usually ignore structural(More)
This paper presents a new incremental learning solution for linear discriminant analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to(More)
An approach to recognise 10 elementary gestures is proposed and it can be applied to sign language recognition. In this work, a motion gradient orientation image is extracted directly from a raw video input and transformed to a motion feature vector. This feature vector is then classified into one of the 10 elementary gestures by a sparse Bayesian(More)
An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10(More)
Low back pain becomes one of the significant problem in the industrialized world. Efficient and effective spinal motion analysis is required to understand low back pain and to aid the diagnosis. Videofluoroscopy provides a cost effective way for such analysis. However, common approaches are tedious and time consuming due to the low quality of the images.(More)
Human body tracking is useful in applications like medical diagnostic, human computer interface, visual surveillance etc. In most cases, only rough position of the target is needed, and blob tracking can be used. The blob region is located within a searching window, which is shifted and resized in each frame based on previous observations. The observations(More)
Video fluoroscopy provides a cost effective way for the diagnosis of low back pain. Backbones or vertebrae are usually segmented manually from fluoroscopic images of low quality during such a diagnosis. In this paper, we try to reduce human workload by performing automatic vertebrae detection and segmentation. Operators need to provide the rough location of(More)