Research Publications
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By Type By Year By Research Group | Lightness illusions, such as the seemingly opposing effects of brightness contrast and assimilation, are characterized by visually perceived intensity images that differ from physical reality. Traditional hypotheses from signal processing community ... Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to ... We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments. First, we extend the Hough hypothesis space to include both object location and its visibility pattern, and design a new score function that ... Due to the high dimensionality of spectral data, spectrum representation techniques have often concentrated on modelling the spectra as a linear combination of a small basis set. Here, we focus on the evaluation of the Gaussian mixture model in [3], the ... Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to ... Purpose: Optimising visual acuity is an important challenge in prosthetic vision. With a small pupil size, the eye optics can result in low-pass filtering of the retinal image at the Nyquist frequency with respect to the photoreceptor density so that ... In this paper we propose a biologically inspired computational model based upon the human visual pathway in order to achieve a feature pair that is robust to changes in scene illumination variation. Here, we draw inspiration from the V4 area in the ... Motion segmentation is a key underlying problem in computer vision for dynamic scenes. Given 3D data from a RGB-D camera, this paper presents a novel method for motion segmentation without explicitly estimating motions. Building up from a recent ... Linear Regression Classification (LRC) based face recognition achieves high accuracy while being highly efficient. As with most other linear-subspace-based methods, the faces of a subject are assumed to reside on a linear manifold; however, where ... |