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This paper presents an automatic detection system capable of detecting an automobile dashboard with high accuracy. Since the structure of an automobile dashboard is quite different from general instruments, commonly used algorithms for instrument detection can hardly meet the accuracy and robustness. In this paper, a novel approach is presented to detect an automobile dashboard. The contour retrieving...
Detection of repetitive patterns in images is subject of several research papers. The majority of them deals with detection of lattice patterns of repetitive elements. However, there are many situations, when element's repetition doesn't follow any particular pattern. In this paper we focus on the following two objectives. Firstly, our algorithm detects repetitive elements regardless of their relative...
In modern remote sensing procedures, one of the most important issues is to distinguish specific types of land coverage. Discrimination between different land coverages especially in metropolitan surveying is so important that the in front civilization projects are basically dependent to them. In this paper, an innovative image processing strategy is employed for distinguishing green lands from other...
Fast generalized fuzzy C-means (FGFCM) is one of the popular image segmentation algorithms. However, a single FGFCM algorithm is easy to get stuck at local optimum and the choices of the initial cluster centers affect segmentation consequences seriously. Furthermore, conventional FGFCM ignores the relationship between the selections of neighbors and edges so that some image details are not considered...
Image edge information is very important in application areas such as machine learning, image processing, stereo vision, object tracking and pattern recognition. Intensity discontinuities or sudden intensity changes in a region are indicative of the edge region in that region. Although there are many approaches to detecting edge, generally intensity discontinuities or sudden intensity changes in a...
In this paper a new method based on Region Growing (RG) and Spectral Cluster (SC) for segmentation of synthetic aperture radar (SAR) images is introduced. In the proposed method first RG is applied to the SAR images in order to find the edge and then segmentation is done using SC method. The proposed method (RG+SC) is compared with some state-of-the-art segmentation algorithms on real SAR image. Obtained...
This survey aims at finding suitable algorithms for each phase for recognizing users based on locations in secured areas. Now-a days the security system is keep on updating its features every accepts. Here, in this project we have mainly focused on developing a multimodal system for government organization. The multimodal system contains the character recognition and face recognition. The main objective...
Traditional mean shift image segmentation algorithms need selecting fixed bandwidth manually, which leads to under-segmentation and non-global optimum. To overcome these disadvantages, a bandwidth adaptive mean shift algorithm is proposed. In this algorithm, a new bandwidth window function is defined, with the bandwidth is determined automatically based on probability distribution characteristics...
Capillary nonperfusion (CNP), which is one of characteristic features in diabetic retinopathy patients, is an important judgement of the appearance of retinal diseases. This paper presents a novel, fast and hybrid method to detect CNP. This model combines region-based active contour model(ACM), Graph-Cut and Fuzzy Possibilistic C-Means(FPCM). We modify ACM with a new energy function to reduce suspicious...
We propose a new superpixel algorithm based on exploiting the boundary information of an image, as objects in images can generally be described by their boundaries. Our proposed approach initially estimates the boundaries and uses them to place superpixel seeds in the areas in which they are more dense. Afterwards, we minimize an energy function in order to expand the seeds into full superpixels....
Recently, there has been renewed interest in the fusion of image segmentation. However, previous relevant research has been impeded by the lack of an appropriate single segmentation criterion, which yields an improved final segmentation result. This paper proposes a new framework to tackle this problem. It is based on multi-objective optimization strategy, followed by a decision making technique called:...
Prior knowledge has been considered as valuable information in many image processing techniques. In this paper, we take the original image itself as the prior and develop a new fuzzy clustering algorithm for image segmentation by adding a new term to the objective function of Fuzzy C-means. The new term comes from Guided Filter for its capability of suppressing noise and preserving edge information...
Synthetic aperture radar is used for land cover change detection which can either be mounted on a drone or an aircraft, spacecraft. It can be used for land cover change detection by comparing two images which are taken at different intervals of time. For this we are using differencing methods. The operators used in differencing methods are log ratio and mean ratio. For obtaining a better difference...
Image segmentation is a hot topic of image processing. The challenging problem is how to preserve the weak edges as well as suppress the over-segmentation. In this paper, we proposed a novel image segmentation algorithm by combining the watershed transform and feature clustering. Firstly, the input image is pre-processed to suppress the noise and smooth the fin details. Secondly, a marker-based watershed...
Superpixel segmentation refers to represent an image by small regions composed of pixels with the similar characteristics, which can carry more perceptual and semantic meaning than their simple pixel grid counterparts. Therefore, it is very important to distribute superpixels with different size over an image for describing image details. In this paper, a new superpixel generation method based on...
Fuzzy clustering methods are efficient tools for image segmentation. However, most of fuzzy clustering approaches are too sensitive to deal with the misclassification of pixels in image segmentation. In recent years, a variety of enhanced fuzzy clustering approaches have been proposed to obtain smoother results in noised image segmentation, but usually with less accurate edges in these results. To...
A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve image segmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then,...
Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images...
Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. For the curtain as the background contained texture structures and resulted in a heterogeneous texture in the sensed image, which...
A side-scan sonar image often contains a lot of noises and its resolution is very low, the edge of side-scan sonar image is blurred, so these characteristics make it difficult to segment objects. The current common segmentation algorithms generally require setting parameters manually, and these parameters are closely related with the images they collected. Consequently, it is difficult to achieve...
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