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Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios...
In this paper, we propose a phase-based approach to estimate disparity between stereo images using the Dual-Tree Complex Wavelet transform and adaptive structured light. Firstly, a random noise adaptive structured light pattern is projected onto objects and two cameras capture stereo images. The adaptive colors are acquired using principle component analysis in the RGB color space of the image of...
Graph-based energy minimization is now the state of the art in stereo matching methods. In spite of its outstanding performance, few efforts have been made to enhance its capability of occlusion handling. We propose an occlusion constraint, an iterative optimization strategy and a mechanism that proceeds on both the digital pixel level and the super pixel level. Our method explicitly handles occlusion...
In this paper we examine the causes of one of the major shortcomings of current natural feature registration approaches, failure to register when the camera's view approaches parallel to the marker. The methods used by current registration algorithms in the attempt to overcome this problem are reviewed, and a novel tracking based approach called the Optical-flow Perspective Invariant Registration...
Multi-class pixel labeling is an important problem in computer vision that has many diverse applications, including interactive image segmentation, semantic and geometric scene understanding, and stereo reconstruction. Current state-of-the-art approaches learn a model on a set of training images and then apply the learned model to each image in a test set independently. The quality of the results,...
Probabilistic topic models have recently been used for activity analysis in video processing, due to their strong capacity to model both local activities and interactions in crowded scenes. In those applications, a video sequence is divided into a collection of uniform non-overlaping video clips, and the high dimensional continuous inputs are quantized into a bag of discrete visual words. The hard...
The interpretation of line drawings of trihedral planer objects is a classic problem. In this paper, it is formulated as a Bayesian inference problem. Given a line drawing image, a Markov random field can be built whose nodes represent the labels of edges. Its clique potential functions are designed to encode the valid junctions in the Huffman-Clowes catalogue. The belief propagation algorithm is...
Pedestrian detection is an important field in computer vision with applications in surveillance, robotics and driver assistance systems. The quality of such systems can be improved by the simultaneous use of different sensors. This paper proposes three different fusion techniques to combine the advantages of two vision sensors -- a far-infrared (FIR) and a visible light camera. Different fusion methods...
This paper presents a scene classification method using criterion mining and adaptive integration. Since scene classification requires scene composition and shift-invariant similarity of fine parts, the latter two are represented by global and local Kernel Principal Component Analysis (KPCA), respectively. In addition, the reconstruction errors obtained with either KPCA are integrated adaptively with...
Visual activity detection of lip movements can be used to overcome the poor performance of voice activity detection based solely in the audio domain, particularly in noisy acoustic conditions. However, most of the research conducted in visual voice activity detection (VVAD) has neglected addressing variabilities in the visual domain such as viewpoint variation. In this paper we investigate the effectiveness...
The automated extraction of roads from aerial imagery can be of value for tasks including mapping, surveillance and change detection. Unfortunately, there are no public databases or standard evaluation protocols for evaluating these techniques. Many techniques are further hindered by a reliance on manual initialisation, making large scale application of the techniques impractical. In this paper, we...
Multiresolution representations and Subspace analysis have been widely accepted in the face recognition systems. This research paper combines the benefits and presents the feature extraction method using Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA). The DWT provides multiresolution representations and are effective in analyzing the information content of the image and...
This paper tackles the issue of still image object categorization. The objective is to infer the semantics of 2D objects present in natural images. The principle of the proposed approach consists of exploiting categorized 3D synthetic models in order to identify unknown 2D objects, based on 2D/3D matching techniques. Notably, we use 2D/3D shape indexing methods, where 3D models are described through...
In this paper, we present a model-based video coding method that uses input from colour and depth cameras, such as the Microsoft Kinect. The model-based approach uses a 3D representation of the scene, enabling several other applications besides video playback. Some of these applications are stereoscopic viewing, object insertion for augmented reality and free viewpoint viewing. The video encoding...
The number of defect pixels in solid state image sensor grows as the digital imagers continue increasing in image size and pixel density. However, limited number of defect pixels is usually allowed after detection and correction techniques are applied. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Without an excellent...
All existing video coding standards consider a video as a temporal (along T-axis) collection of two dimensional pictures (formed by XY axes) and compress them by exploiting spatial and temporal redundancy in the pictures. A recent optimal compression plane (OCP) determination technique shows that better compression can be achieved by relaxing the physical meaning of axes by exploring information redundancy...
Compressive sensing (CS) has emerged as an efficient signal compression and recovery technique, that exploits the sparsity of a signal in a transform domain to perform sampling and stable recovery. The existing image compression methods have complex coding techniques involved and are also vulnerable to errors. In this paper, we propose a novel image compression and recovery scheme based on compressive...
Although most greyscale morphology is performed with ``flat'' structuring functions because these are widely available, the use of scaled paraboloid (or quadratic) structuring functions offers a far wider range of applicability, better theoretical properties, and can also be computed efficiently. We demonstrate the novel application of scaled paraboloid structuring functions to parallel algorithms...
This paper presents an alternative spatial image compression method that can be directly applied to compressing certain classes of grey-scale and/or colour (RGB, 24 bits) imagery of any size. The process involves the vectorisation of digital images into contour maps with subsequent converting of the contours to a pixel format. Contours are often approximated by polygons (linear or otherwise), and...
Whilst effective methods exist for character recognition in certain contexts, such as characters taken from printed or handwritten documents, these methods have not performed well when tested with characters taken from natural images. In this work we introduce oriented Basic Image Features (oBIFs), a system based upon local symmetry and orientation, and demonstrate how they can be used within a mutliscale...
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