Novel Method For Image Segmentation Research Articles

A novel method for image segmentation: two-stage decoding network with boundary attention

A novel method for image segmentation: two-stage decoding network with boundary attention

An Image analogy approach for multi-scale Image segmentation

Image segmentation is one of the challenging tasks in image processing. This paper introduces a novel method for image segmentation based on image analogy principle. First, contours of source image are located using 14 pairs of stereo patches. Next, elementary regions, considered as areas between contours located using to successive pairs of patches. Finally, we define border's stability of neighbouring regions which constitutes the measure for regions merging. Three scales are defined giving three segmentations at three resolutions (low, intermediate and high). Experiments conducted on Wiezmann dataset, the obtained results are presented and compared to the state-of-the-art.

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results.

A novel method for image segmentation using reaction–diffusion model

We propose an image segmentation model that is derived from reaction---diffusion equations and level set methods. In our model, a diffusion term is used for regularization of a level set function, and a reaction term has the desired sign property to force the level set function to move up or down and finally identify an object and its background. Our level set function can be initialized to any bounded function (e.g., a constant function). The proposed model can be applied to a wider range of images with promising results, especially for real images that have high noise and blurred boundaries. This study gives a new method for the further investigations of reaction---diffusion equations directly for segmentation.

Image segmentation via multi-scale stochastic regional texture appearance models

An ongoing challenge in the area of image segmentation is in dealing with scenes exhibiting complex textural characteristics. While many approaches have been proposed to tackle this particular challenge, a related topic of interest that has not been fully explored for dealing with this challenge is stochastic texture models, particularly for characterizing textural characteristics within regions of varying sizes and shapes. Therefore, this paper presents a novel method for image segmentation based on the concept of multi-scale stochastic regional texture appearance models. In the proposed method, a multi-scale representation of the image is constructed using an iterative bilateral scale space decomposition. Local texture features are then extracted via image patches and random projections to generate stochastic texture features. A texton dictionary is built from the stochastic features, and used to represent the global texture appearance model. Based on this global texture appearance model, a regional texture appearance model can then be obtained based on the texton occurrence probability given a region within an image. Finally, a stochastic region merging algorithm that allows the computation of complex features is presented to perform image segmentation based on proposed regional texture appearance model. Experimental results using the BDSD300 segmentation dataset showed that the proposed method achieves a Probabilistic Rand Index (PRI) of 0.83 and an F-measure of 0.77@(0.92, 0.68), and provides improved handling of color and luminance variation, as well as strong segmentation performance for images with highly textured regions when compared to a number of previous methods. These results suggest that the proposed stochastic regional texture appearance model is better suited for handling the texture variations of natural scenes, leading to more accurate segmentations, particularly in situations characterized by complex textural characteristics.

Improved Fuzzy C-Means Clustering Algorithm Using Watershed Transform on Level Set Method for Image Segmentation

An innovative novel method for image segmentation is proposed in this paper, which combines the watershed transform, Improved FCM and level set method. The watershed transform is original used to pre-segment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The Improved fuzzy c-means (IFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. IFCM algorithm computes the fuzzy membership values for each pixel. On the source of IFCM the edge indicator function was redefined. Using the edge indicator function of an image was performed to extract the boundaries of objects on the origin of the pre-segmentation. Therefore, the proposed method is computationally efficient. The efficiency and accuracy of the algorithm is demonstrated. The above process of segmentation showed a considerable improvement in the evolution of the level set function.

Use of aggregation pheromone density for image segmentation

Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of a data point, representing a pixel, an ant is placed; and the ants are allowed to move in the search space to find out positions with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different positions of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of different types of images using various cluster validity measures. Results are compared with those obtained using k-means and mean shift clustering algorithms and are found to be superior.

A Novel Method of Image Segmentation Based on Parallelized Firing PCNN

摘要: 提出一种并行点火脉冲耦合神经网络(Parallelized firing pulse coupled neural networks, PFPCNN)模型的图像分割方法. 首先用改进的Unit-linking PCNN (ULPCNN)模型对图像进行增强, 便于后续的图像分割. 然后采用PFPCNN新模型对增强后的图像进行分割, 最后用最大香农熵方法判定最佳分割结果. 各种复杂场景下的仿真实验及定量评价表明, 本文提出的图像分割方法, 其效果明显优于常规的PCNN分割方法. 关键词: 脉冲耦合神经网络 / 并行点火模型 / 图像增强 / 最大香农熵 / 图像分割

MRF-based image segmentation using Ant Colony System

In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality.

An architecture for a CBR image segmentation system

Image segmentation is a crucial step in extracting information from a digital image. It is not easy to set up the segmentation parameter so that it gives the best fit over the entire set of images that need to be segmented. This paper proposes a novel method for image segmentation based on CBR. It describe the whole architecture, as well as the methods used for the various components of the systems, and shows how the technique performs on medical images.