Adaptive segmentation of noisy and textured images

An image segmentation algorithm is described which is based on the integration of signal model parameter estimates and maximum a posteriori labelling. The parameter estimation is based on either a maximum likelihood-based method for a quadric signal model or a maximum pseudo-likelihood based method for a Gauss-Markov signal model. The first case is applicable to standard grey-level image segmentation as well as segmentation of shaded 3D surfaces, while the second case is applicable to texture segmentation. A key aspect of the algorithm is the incorporation of a coarse to fine processing strategy which limits the search for the optimum labelling at any one resolution to a subset of labellings which are consistent with the optimum labelling at the previous coarser resolution. Consistency is in terms of a prior label model which specifies the conditional probability of a given label in terms of the labelling at the previous level of resolution. It is shown how such an approach leads to a simple relaxation procedure based on local pyramid node computations. An extension of the algorithm is also described which performs accurate inter-region boundary placement using a step-wise refinement procedure based on a simple adaptive filter. The problem of automatic determination of the number of regions is also addressed. It is shown how a simple agglomerative clustering idea, again based on pyramid node computations, can effectively solve this problem.     

基于马尔可夫随机场的纹理图像并行分割

基于消息传递接口（Message Passing Interface,MPI）和消息传递并行编程模型,提出了一种针对计算机集群（Cluster）的纹理图像并行分割算法。该算法使用马尔可夫随机场作为纹理特征,通过将图像分块,把特征提取的计算量均匀的分布到并行系统中的各个节点上,从而极大地减少了计算时间。在遥感图像上的实验发现,该算法在4机并行的环境下可以取得与单机串行程序一样精确的分割,而耗时仅为串行程序的31.95％。令人满意的实验结果表明该并行算法不但可以有效的应用于纹理图像分割,而且也为使用计算机集群实现高时间复杂度的图像处理提供了有益的启示。     

Turbo segmentation of textured images

We consider the problem of semi-supervised segmentation of textured images. Existing model-based approaches model the intensity field of textured images as a Gauss-Markov random field to take into account the local spatial dependencies between the pixels. Classical Bayesian segmentation consists of also modeling the label field as a Markov random field to ensure that neighboring pixels correspond to the same texture class with high probability. Well-known relaxation techniques are available which find the optimal label field with respect to the maximum a posteriori or the maximum posterior mode criterion. But, these techniques are usually computationally intensive because they require a large number of iterations to converge. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes, is obtained based on a factor graph approach. The proposed method estimates the unknown parameters using the Expectation-Maximization algorithm.     

Multiple resolution segmentation of textured images

A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior. In addition, an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions. Both algorithms use a causal Gaussian autoregressive model to describe the mean, variance, and spatial correlation of the image textures. Together, the algorithms can be used to perform unsupervised texture segmentation. The multiple resolution segmentation algorithm first segments images at coarse resolution and then progresses to finer resolutions until individual pixels are classified. This method results in accurate segmentations and requires significantly less computation than some previously known methods. The field containing the classification of each pixel in the image is modeled as a Markov random field. Segmentation at each resolution is then performed by maximizing the a posteriori probability of this field subject to the resolution constraint. At each resolution, the a posteriori probability is maximized by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or pixel blocks. The unsupervised parameter estimation algorithm determines both the number of textures and their parameters by minimizing a global criterion based on the AIC information criterion. Clusters corresponding to the individual textures are formed by alternately estimating the cluster parameters and repartitioning the data into those clusters. Concurrently, the number of distinct textures is estimated by combining clusters until a minimum of the criterion is reached     

Modeling textured images using generalized long correlation models

The long correlation (LC) models are a general class of random field (RF) models which are able to model correlations, extending over large image regions with few model parameters. The LC models have seen limited use, due to lack of an effective method for estimating the model parameters. In this work, we develop an estimation scheme for a very general form of this model and demonstrate its applicability to texture modeling applications. The relationship of the generalized LC models to other classes of RF models, namely the simultaneous autoregressive (SAR) and Markov random field (MRF) models, is shown. While it is known that the SAR model is a special case of the LC model, we show that the MRF model is also encompassed by this model. Consequently, the LC model may be considered as a generalization of the SAR and MRF models     

Automatic melanoma detection and segmentation in dermoscopy images using deep RetinaNet and conditional random fields

Multimedia Tools and Applications - Melanoma is one of the major causes of death around the world and is also known as malignant skin cancer. Melanoma detection is possible at an early stage by...     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

Texture segmentation using Gaussian-Markov random fields and neuraloscillator networks

We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a 2D array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method.