Shape and data driven texture segmentation using local binary patterns

Image segmentation is a fundamental step in image analysis. Segmentation can be done by isolating homogeneous regions within an image or finding the boundaries between such regions. There are several cases that intensity values, color, mean or variance of image intensity distributions and edge information cannot play a discriminative role in image segmentation. While other features are not sufficient to discriminate regions, texture might be a good feature to handle the segmentation problem. Texture analysis and texture segmentation are still challenging problems; there is no method which can clearly identify and discriminate all kind of textures. Especially identifying nonumform textures and discriminating from other textures are still difficult problems in texture analysis. For this reason texture segmentation approaches may give unsatisfactory segmentation results with missing data or with corrupted boundaries of regions. Using prior information about the shape of the object can aid segmentation which can also obtain a solution to occlusion problems. In this thesis, we propose a shape and data driven texture segmentation method using local binary pattern (LBP). In particular, we train our LBP based texture filter with the texture which belongs to the region that we want to segment. We input the textured image into our filter to produce a "filtered image" which has been eluded from the structural properties of texture. Then by an energy functional, which combines the data term produced from the filtered image and shape prior term under a Bayesian framework, we evolve our level set based active contour for segmentation

Shape and data-driven texture segmentation using local binary patterns

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions

A Local binary patterns and shape priors based texture segmentation method

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions

Maximizing chatter free material removal rate in milling through optimal selection of axial and radial depth of cut pairs

Chatter vibrations in milling, which develop due to dynamic interactions between the cutting tool and the workpiece, result in reduced productivity and part quality. Several stability models have been considered in previous publications, where mostly the stability limit in terms of axial depth of cut is emphasized for chatter free machining. In this paper, it is shown that, for the maximization of chatter free material removal rate, radial depth of cut is of equal importance. A method is proposed to determine the optimal combination of depths of cut, so that chatter free material removal rate is maximized. The application of the method is demonstrated on a pocketing example where significant reduction in the machining time is obtained using the optimal depths. The procedure can easily be integrated to a CAD/CAM system or a virtual machining environment in order to identify the optimal milling conditions