Bounded diffusion for multiscale edge detection using regularized cubic B-spline fitting

This paper shows that in edge detection the regularization factor a is a better scale parameter than the standard deviation (sigma) of the Gaussian pre-filter. The a scale space, which exhibits the evolutionary behavior of an edge in various scales, is the basis for the design of a multiscale edge detector (MRCBS). In MRCBS, the scale is determined adaptively according to the local noise level; the thresholds which control the amount of edge details are adjusted according to the scale; and the anisotropic diffusion is applied in the finest scale to further suppress noise

A Vision System for Automating Municipal Waste Collection

This thesis describes an industry need to make municipal waste collection more efficient. In an attempt to solve this need Waterloo Controls Inc. and a research team at UWO are exploring the idea of combining a vision system and a robotic arm to complete the waste collection process. The system as a whole is described during the introduction section of this report, but the specific goal of this thesis was the development of the vision system component. This component is the main contribution of this thesis and consists of a candidate selection step followed by a verification step

Hybrid And Hierarchical Image Registration Techniques

A large number of image registration techniques have been developed for various types of sensors and applications, with the aim to improve the accuracy, computational complexity, generality, and robustness. They can be broadly classified into two categories: intensity-based and feature-based methods. The primary drawback of the intensity-based approaches is that it may fail unless the two images are misaligned by a moderate difference in scale, rotation, and translation. In addition, intensity-based methods lack the robustness in the presence of non-spatial distortions due to different imaging conditions between images. In this dissertation, the image registration is formulated as a two-stage hybrid approach combining both an initial matching and a final matching in a coarse-to-fine manner. In the proposed hybrid framework, the initial matching algorithm is applied at the coarsest scale of images, where the approximate transformation parameters could be first estimated. Subsequently, the robust gradient-based estimation algorithm is incorporated into the proposed hybrid approach using a multi-resolution scheme. Several novel and effective initial matching algorithms have been proposed for the first stage. The variations of the intensity characteristics between images may be large and non-uniform because of non-spatial distortions. Therefore, in order to effectively incorporate the gradient-based robust estimation into our proposed framework, one of the fundamental questions should be addressed: what is a good image representation to work with using gradient-based robust estimation under non-spatial distortions. With the initial matching algorithms applied at the highest level of decomposition, the proposed hybrid approach exhibits superior range of convergence. The gradient-based algorithms in the second stage yield a robust solution that precisely registers images with sub-pixel accuracy. A hierarchical iterative searching further enhances the convergence range and rate. The simulation results demonstrated that the proposed techniques provide significant benefits to the performance of image registration