Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe

Image segmentation using region merging combined with a multi-class spectral method

In this paper we propose an image segmentation algorithm that combines region merging with spectral-based techniques. An initial partitioning of the image into primitive regions is produced by applying a region merging approach which produces a chunk graph that takes in attention the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process that produces the final segmentation. The latter process uses a multi-class partition that minimizes the normalized cut value for the region graph. We have efficiently applied the proposed approach with good visual and objective segmentation quality results

SLFS: Semi-supervised light-field foreground-background segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.info:eu-repo/semantics/acceptedVersio

Graph-Based Image Segmentation Methods

Import 11/07/2012Cílem diplomové práce bylo nastudovat metody pro segmentaci obrazu založené na teorii grafů, vybrat a implementovat jednu metodu, na závěr provést a vyhodnotit praktické testy. Text nejprve čtenáře seznamuje s problematikou segmentace obrazu a její úlohou při analýze obrazu. Následuje přehled několika algoritmů založených na teorii grafů, stručný popis jejich vlastností, vhodnost použití a zamyšlení nad odlišností oproti tradičním metodám. Ve střední části je detailněji popsán a vysvětlen jeden z grafových algoritmů a v krátké kapitole lze nahlédnout na specifika jeho implementace. V závěrečné kapitole je algoritmus podroben praktickým testům na sadě různých obrazů.The aim of this thesis was to study selected graph-based image segmentation method. The text begins with general description of image analysis and it specifies the role of image segmentation in that process. Since there are many graph-based image segmentation methods already published couple of them were selected and briefly described their thoughts. The “Efficient graph-based image segmentation” method was chosen for deep learning. Method was explained in detail and algorithm was implemented in C++. In the last chapter are commented several results of image segmentation producing by the algorithm.460 - Katedra informatikyvelmi dobř

Stochastic Multiscale Segmentation Constrained by Image Content

International audienceWe introduce a noise-tolerant segmentation algorithm efficient on 3D multiscale granular materials. The approach uses a graph-based version of the stochastic watershed and relies on the morphological granulometry of the image to achieve a content-driven unsupervised segmentation. We present results on both a virtual material and a real X-ray microtomographic image of solid propellant