Edgeflow-driven variational image segmentation: Theory and performance evaluation

We introduce robust variational segmentation techniques that are driven by an Edgeflow vector field. Variational image segmentation has been widely used during the past ten years. While there is a rich theory of these techniques in the literature, a detailed performance analysis on real natural images is needed to compare the various methods proposed. In this context, this paper makes the following contributions: (a) designing curve evolution and anisotropic diffusion methods that use Edgeflow vector fields to obtain good quality segmentation results over a large and diverse class of images, and (b) a detailed experimental evaluation of these segmentation methods. Our experiments show that Edgeflow-based anisotropic diffusion outperforms other competing methods by a significant margin. Index Terms Variational image segmentation, Edgeflow, curve evolution, anisotropic diffusion, multiscale, textur

Multi-focus imaging using local focus estimation and mosaicking

We propose an algorithm to generate one multi-focus image from a set of images acquired at different focus settings. First images are registered to avoid large misalignments. Each image is tiled with overlapping neighborhoods. Then, for each region the tile that corresponds to the best focus is chosen to construct the multi-focus image. The overlapping tiles are then seamlessly mosaicked. Our approach is presented for images from optical microscopes and hand held consumer cameras, and demonstrates robustness to temporal changes and small misalignments. The implementation is computationally efficient and gives good results. Index Terms — Focus, seamless mosaicking, microscopy, consumer camera

Image segmentation using multi-region stability and edge strength

A novel scheme for image segmentation is presented. An image segmentation criterion is proposed that groups similar pixels together to form regions. This criterion is formulated as a cost function. This cost function is minimized by using gradient-descent methods, which lead to a curve evolution equation that segments the image into multiple homogenous regions. Homogeneity is specified through a pixel-to-pixel similarity measure, which is defined by the user and can be adaptive based on the current application. To improve the performance of the system, an edge function is also used to adjust the speed of the competing curves. The proposed method can be easily applied to vector valued images such as texture and color images without a significant addition to computational complexity. 1

Graph partitioning active contours for knowledge-based geospatial segmentation

Our contribution in this paper is two-fold. First, we extend our previous curve evolution method based on pairwise similarities. This curve evolution equation combines the grouping abilities of active contours and graph partitioning techniques. Connections of our method to spectral graph partitioning are investigated and comparisons are made. Second, in a model-based segmentation scenario, we propose a method to improve segmentation quality by iteratively modifying the model using feedback from segmentation of a labeled training set. Our purpose here is to segment objects in geo-spatial images by integrating domain knowledge with the segmentation method. We achieve our goal by combining a statistical model for the object with a knowledge-guided segmentation method. Experimental results show that this framework is effective for model-based segmentation of complex geo-spatial objects. 1

Tile-based framework for local enhancement of bio imagery

We present a simple framework for image enhancement using local image information. The main idea is to divide the image into small tiles and individually enhance each of these tiles. Enhanced tiles are then mosaicked back together. Our approach is presented for enhancement of fluorescent microscopy images and demonstrates better local contrast preservation and saturation reduction in comparison with traditional global approaches (histogram stretching, equalization or gamma correction). 1