In this paper we investigate methods for propagating automatically generated or user-defined labels through an image using edge-preserving filters. We focus on the domain transform filter as it has been used for propagation purposes in the past. The method we present addresses some of the numerical issues that arise with using the filter directly and also improve on the results by better respecting the underlying image structure during the label propagation. Finally we also demonstrate how a filter-based approach is preferable to using global optimization for interpolating automatically generated sparse features.

Modern, state-of-the-art disparity estimation techniques are able to very accurately estimate the disparity for a wide variety of scene types. However all of these methods assume that the input images are epipolar rectified. When an image pair is not rectified, it must be pre-processed before any estimation can be done. In this paper we propose a disparity estimation scheme that is able to handle non-rectified images without requiring a rectification step. We show how a minor modification to an existing estimation framework can allow for any disparity estimation framework to produce disparity maps for non-rectified images.

In this paper, we propose an adaptive method for 2D to 3D conversion of images using a user-aided process based on Graph Cuts and Random Walks. Given user-defined labeling that correspond to a rough estimate of depth, the system produces a depth map which, combined with a 2D image can be used to synthesize a stereoscopic image pair. The work presented here is an extension of work done previously combining the popular Graph Cuts and Random Walks image segmentation algorithms. Specifically, we have made the previous approach adaptive, as well as improved the quality of the results. This is achieved by feeding information from the Graph Cuts result into the Random Walks process at two different stages, and using edge and spatial information to adapt various weights. The results show that we can produce good quality stereoscopic 3D image pairs using a simple yet adaptive approach.

The conversion of monoscopic footage into stereoscopic or multiview content is a difficult and time consuming task. A number of semi-automatic methods have been developed to speed up the process and provide some control to the user. However these methods require that the user provide detailed labels indicating the relative depth of objects in the scene. In this paper we present a method to automatically estimate depth in such a way that it is amenable to semi-automatic conversion. The method is designed to simplify the depth labelling task so that the user does not have to provide as many depth labels.

In this paper, we present a semi-automated method for converting conventional 2D images to stereoscopic 3D. User-defined strokes that correspond to a rough estimate of the depth values in the scene are defined for the image of interest. With these strokes, our system thus determines what the depth values are for the rest of the image, producing a depth map that is ultimately used to create a stereoscopic image pair. Our work is based on a similar scheme which employs Random Walks. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic im age pair. Combined with the evident shortcomings of the related work, but noting the merits of Random Walks, we propose a system that is a hybrid between Random Walks, and the popular Graph Cuts segmentation paradigm. Both segmentation algorithms are used to generate a final cohesive depth map, thus combining the merits of both frameworks together. The generated results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related Random Walks scheme.

In this paper, we present a semi-automated method for converting conventional 2D images into stereoscopic 3D. User-defined strokes corresponding to a rough estimate of the depth values in the scene are defined for the image of interest. With these, our system determines the depth values for the rest of the image, producing a depth map that can be used to create stereoscopic 3D image pairs. Our work is based on a similar scheme, using the Random Walks segmentation paradigm. However, the related work is quite complex, with many processing steps required to produce the final stereoscopic image pair. Combined with its evident shortcomings, but noting the merits, we propose a system employing Random Walks, while incorporating information from the popular Graph Cuts segmentation paradigm. Thus, a final cohesive depth map is produced, combining the merits of both. The results show that we can produce good quality stereoscopic image pairs, while using a much more simplified method in comparison to the related work.

We present a method for easily generating depth maps from monoscopic (i.e. “2D”) video footage in order to convert them into stereoscopic, or “3D”, footage. Our method uses user-defined strokes for a number of keyframes in the original footage and interpolates between the keyframes to provide a sparse labelling for each frame. We then apply the Random Walks algorithm to the footage to provide depth estimates based on the input provided by the user. These depth maps can then be used to generate novel views through depth-based image rendering.

The large volume of data on the Internet makes it extremely difficult to extract high-level information, such as recurring or time-varying trends in document content. Dimensionality reduction techniques can be applied to simplify the analysis process but the amount of data is still quite large. If the analysis is restricted to just text documents then Latent Dirichlet Allocation (LDA) can be used to quantify semantic, or topical, groupings in the data set. This paper proposes a method that combines LDA with the visualization capabilities of Self-Organizing Maps to track topic trends over time. By examining the response of a map over time, it is possible to build a detailed picture of how the contents of a dataset change.

We present a novel rotoscoping method that provides accurate object boundaries while still remaining simple to use. We have designed this method so that a user familiar with existing, spline-based rotoscoping techniques will not need to significantly modify their workflow. Our method utilizes a modified version of the random walks segmentation algorithm, known as scale-space random walks (SSRW), to provide a level of accuracy simply not possible using splines alone. Furthermore, due to the nature of the SSRW algorithm, we are able to provide a realtime, or near realtime, level of interaction with the user.