Adaptive Nonlocal Random Walks for Image Superpixel Segmentation

We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.

2020

There is a growing demand for image processing in a wide range of applications such as photography, robotics, television, remote sensing, industrial inspection, and medical diagnosis. This study overviews some of the existing image segmentation methods that focus on producing superpixels. A superpixel or segment is a homogeneous, local coherent structure that specifies information oversampling or scales resolutions. There are many image segmentation or superpixelization methods which divide color image with different techniques according to their characteristics and parameters as image acquisition might be seriously affected by many factors such as light and shadow. Several image segmentation algorithms were investigated in image processing research for creating superpixels that may lack the ability to control the size, number, and compactness of segments. Superpixel generation algorithms can be categorized into graph-based methods and gradient-ascent

Journal of Electronic Imaging

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixel similarity in which region comparisons are made both at content and common border level. Secondly, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adpative merging criterion to ensure that best region aggregations are given highest priorities. This allows to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach.

Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel's purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

2019

Image segmentation is an important part of image analysis process, since it differentiates between the salient objects and the other objects or from their background. It is the process of dividing digital image into multiple segments and the main aim of segmentation is to pinpoint objects and boundaries. There are different methods for segmenting image, here we are considering the concept of superpixels inorder to segment image. Superpixel can mainly accelerate the successive processing since the superpixels of an image carry more information than a normal pixel. This paper deals with detailed survey on different superpixel segmentation techniques. IndexTerms: Salient object, Superpixel, Discriminability. ________________________________________________________________________________________________________

2009

Image segmentation plays an important role in computer vision and human scene perception. Image oversegmentation is a common technique to overcome the problem of managing the high number of pixels and the reasoning among them. Specifically, a local and coherent cluster that contains a statistically homogeneous region is denoted as a superpixel. In this paper we propose a novel algorithm that segments an image into superpixels employing a new kind of shape centered feature which serve as a seed points for image segmentation, based on Gradient Vector Flow fields (GVF) . The features are located at image locations with salient symmetry. We compare our algorithm to state-of-the-art superpixel algorithms and demonstrate a performance increase on the standard Berkeley Segmentation Dataset.

2019 IEEE International Conference on Image Processing (ICIP), 2019

Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.

El título de este trabajo puede ser un poco pretensioso, en la medida en que tanto la fenomenología como la educación son dos campos de reflexión bastante amplios y con características bien complejas. El método fenomenológico ha sido base fundamental para la filosofía contemporánea, pues sólo el retorno a las cuestiones ontológicas y a la experiencia permitieron superar la visión Metafísica que cargó la filosofía a lo largo de su historia, y desde esta superación se ha posibilitado el encontrar nuevas potencias creadoras para este oficio del pensar que es la filosofía.

Canon&Culture, 2020

A short historical survey of scholarly interpretations of the concept of bérit in the Hebrew Bible is offered in this article as a background to understanding the urge for different interpretations offered by scholars in recent decades. The survey starts with a detailed discussion of the contribution of Mendenhall’s interpretations of the biblical covenant in light of legal treaties from the Ancient Near East during the second millennium BCE. Those mainly Hittite international treaties opened the door for a new interpretation of the biblical texts in light of legal rather than just religious relations between the God of Israel and his people. The second part of the article shows how the Hittite treaty-documents have led to new interpretations of the Hebrew Bible covenant, based on ancient Near Eastern legal-political as well as cultic material, and how it may still be used in current research, together with Mesopotamian-Akkadian documents, which were also brought into that discussion. The major dispute between scholars today is the attempt to pose an historical measurement on the possible connection between the Hittite legal documents titled in Hittite išḫiul- which are dated to the second half of the second millennium, and the Akkadian documents titled adê mainly of the first millennium BCE, with the Hebrew Bible texts drawn during the first millennium.