Image Processing Techniques to Separate Linear and Curvilinear Features in Textures

Varied image processing techniques have been developed to extract or detect linear features from images. However, these techniques are targeted at extracting or detecting linear features, and it has been shown in an existing technique that the Fourier transform can be used in conjunction with the polar transformation to essentially lift or separate linear features from the background image. Extracting or detecting linear features in images involves locating these features in the image while separating or lifting them involves separating them from the background image such that we get two images: one image containing the linear features and the other image containing the background. This thesis presents approaches to separate linear and curvilinear features from textured backgrounds. The problem of separating linear features from a textured background is of importance in applications such as lithography, layout design and pattern recognition. The existing Fourier transform based approach of linear feature separation effectively separates randomly located lines that are spread throughout the entire image and is found to be ineffective when the linear features are of varied lengths and thickness. This thesis presents an approach to overcome this limitation of the Fourier transform based approach. This thesis presents two new window based techniques relying on the Fourier transform and the wavelet transform to lift randomly located lines of varied in lengths and thickness. The proposed techniques are built upon the existing Fourier transform approach. The performances of the proposed techniques are compared to the Fourier Transform approach through application to several images. It is observed that the proposed Fourier based block approach and wavelet based block approach consistently perform better than the existing approach. It is also observed that the proposed techniques effectively lift curvilinear features from textures too. The mathematical analysis and experimental results verifying this claim are presented

From uncertainty to adaptivity : multiscale edge detection and image segmentation

This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textures and motion fields are also important for segmenting an image into disjoint regions. The major theoretical achievements of this thesis are twofold. First, a scale parameter for the local processing of an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution clustering scheme is proposed for texture segmentation (referred to as Texture Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous textures (motion fields) are analysed using coarse resolutions so as to achieve a better estimation of the textural content (optical flow), and the border region of a texture (motion field) is analysed using fine resolutions so as to achieve a better estimation of the boundary between textures (moving objects). Both of the above two achievements are the logical consequences of the uncertainty principle. Four algorithms, including a roof edge detector, a multiscale step edge detector, a texture segmentation scheme and a motion field segmentation scheme are proposed to address various aspects of edge detection and image segmentation. These algorithms have been implemented and extensively evaluated