- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 Eleventh International Conference on Contemporary Computing (IC3) - Noida, India (2018.8.2-2018.8.4)] 2018 Eleventh International Conference on Contemporary Computing (IC3) - cKGSA Based Fuzzy Clustering Method for Image Segmentation of RGB-D Images
摘要： With the introduction of low-cost depth image sensors, reliable image segmentation within RGB-D images is an ambitious goal of computer vision. However, in a cluttered scene, image segmentation has become a challenging problem. This paper presents a novel RGB-D image segmentation method, chaotic kbest gravitational search algorithm based fuzzy clustering (cKGSA-FC). First, the proposed method performs fuzzy clustering using cKGSA on different parameters and feature subsets to obtain multiple optimal clusters. Next, the proposed method combines the multiple clusters through the segmentation by aggregating superpixels (SAS) method on different combinations to generate the final segmentation result. The proposed method is evaluated on the standard RGB-D indoor image dataset namely; NYU depth v2 (NYUD2) and compared with the results obtained by performing fuzzy clustering through three existing clustering methods namely; gravitational search algorithm, fuzzy c-means, and kmeans. The evaluation of the results is done in terms of qualitative and quantitative. Experimental results confirm that the segmentation quality of the proposed method is superior than the compared methods.
关键词： cKGSA,Image segmentation,Meta-heuristic,RGB-D
更新于2025-09-09 09:28:46
-
[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - A Fractional Active Contour Model for Medical Image Segmentation
摘要： Consideration of both traditional local and global information segmentation remains a image challenging task. Some hybrid methods have shown promise in handling this challenge. In this paper, a new hybrid method is presented, which incorporates image gradient, local information and global information into a framework. The energy or level-set function fractional order fractional order gradient magnitude, and differentiation, local difference Chan-Vese model, which has been shown to be effective and efficient in modeling the local information. The presented new model can also enhance low frequency information, which is clinically desired. Experiments on synthetic images as well as real images were performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method. The dice similarity coefficient merit was employed as the comparative quantitative measures and showed a noticeable gain over a current hybrid method.
关键词： Fractional order differentiation,Level set,Active contour model,Image segmentation
更新于2025-09-09 09:28:46
-
Possibilistic Clustering Algorithm Incorporating Grey-Level Histogram and Spatial Information for Image Segmentation
摘要： Image segmentation is a process of segmenting an image into non-intersecting regions containing homogeneous pixels that are inhomogeneous with those in other adjacent regions. In this paper, a possibilistic clustering algorithm incorporating grey-level histogram and spatial information (PCA_HS) for image segmentation is proposed. The grey-level histogram speeds up the algorithm and the spatial information enhances its robustness to noise and outliers. To assess the proposed algorithm, four widely used validity indexes are computed and discussed. As the experimental quantitative and qualitative results on real images with and without noise show, PCA_HS can preserve the homogeneity and integrality of the regions and hence is more effective and efficient than traditional PCA.
关键词： grey-level histogram,Image segmentation,possibilistic clustering,spatial information
更新于2025-09-09 09:28:46
-
Perceptual hashing for SAR image segmentation
摘要： Image segmentation is an important step in the implementation of the interpretation of synthetic aperture radar (SAR) image due to speckle. This article proposes a SAR image segmentation method based on perceptual hashing. The new algorithm is divided into two phases. The first phase is to obtain initial regions with multi-thresholding based on histogram after reducing the speckle noise. The initial regions are used as input data. And the next phase is to merge regions according to the similarity between regions. In this phase, to segment SAR image effectively, the proposed hashing algorithm is used to obtain hash value and similarity between regions, which preserve the texture features of SAR images. In addition, we can obtain a smooth segmentation result by reducing the redundant information with principal component analysis. Furthermore, morphological methods are used to eliminate the uneven background in the segmentation results. These improvements make our algorithm more effective to segment the images with high speed. The experimental results of four real and one synthetic SAR images verify the efficiency of our algorithm.
关键词： multi-thresholding,perceptual hashing,region merging,principal component analysis,SAR image segmentation
更新于2025-09-09 09:28:46
-
[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
摘要： This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
关键词： deep-learning,Landsat 8,FCN,image segmentation,U-Net,remote sensing,CNN,Cloud detection
更新于2025-09-09 09:28:46
-
Comparison of level set models in image segmentation
摘要： Image segmentation is one of the most important tasks in modern imaging applications, which leads to shape reconstruction, volume estimation, object detection and classification. One of the most popular active segmentation models is level set models which are used extensively as an important category of modern image segmentation technique with many different available models to tackle different image applications. Level sets are designed to overcome the topology problems during the evolution of curves in their process of segmentation while the previous algorithms cannot deal with this problem effectively. As a result, there is often considerable investigation into the performance of several level set models for a given segmentation problem. It would therefore be helpful to know the characteristics of a range of level set models before applying to a given segmentation problem. In this study, the authors review a range of level set models and their application to image segmentation work and explain in detail their properties for practical use.
关键词： topology problems,curve evolution,level set models,active segmentation,image segmentation
更新于2025-09-04 15:30:14
-
[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Peekaboo-Where are the Objects? Structure Adjusting Superpixels
摘要： This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd’s algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in the image, and thus provides for more meaningful superpixels in the same linear runtime as standard SLIC. The proposed method is evaluated against state-of-the-art techniques and improved boundary adherence and undersegmentation error are observed, whilst still remaining among the fastest algorithms which are tested.
关键词： Image texture analysis,Image segmentation,Clustering algorithms
更新于2025-09-04 15:30:14
-
Image Judgment Auxiliary System for Table Tennis Umpiring under Low Light Conditions
摘要： In table tennis competitions, the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. It is often di?cult to provide enough light when using high-speed photography and this leads to underexposure. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can ?nd the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.
关键词： Table tennis,low light source,automatic tracking,hue-saturation-value (HSV) image segmentation
更新于2025-09-04 15:30:14
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation
摘要： Semantic image segmentation, which assigns labels in pixel level, plays a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning. However, one central problem of these methods is that deep convolutional neural network gives little consideration to the correlation among pixels. To handle this issue, in this paper, we propose a novel deep neural network named RelationNet, which utilizes CNN and RNN to aggregate context information. Besides, a spatial correlation loss is applied to train RelationNet to align features of spatial pixels belonging to same category. Importantly, since it is expensive to obtain pixel-wise annotations, we exploit a new training method to combine the coarsely and finely labeled data. Experiments show the detailed improvements of each proposal. Experimental results demonstrate the effectiveness of our proposed method to the problem of semantic image segmentation, which obtains state-of-the-art performance on the Cityscapes benchmark and Pascal Context dataset.
关键词： Spatial correlation loss,CNN,Semantic image segmentation,RNN,Deep learning,RelationNet
更新于2025-09-04 15:30:14
-
[IEEE 2018 IEEE 27th Asian Test Symposium (ATS) - Hefei, China (2018.10.15-2018.10.18)] 2018 IEEE 27th Asian Test Symposium (ATS) - Test Diagnosis of Digital Microfluidic Biochips Using Image Segmentation
摘要： Digital micro?uidic biochip has been developed as a promising alternative to the traditional approach of benchtop laboratory tests. Dependability is an important biochemical attribute for micro?uidic biochips that are used for safety-critical applications, such as point-of-care health assessment, air-quality monitoring, and food-safety testing. Therefore, the robust of?ine and online test techniques are required after manufacturing and during bioassay operations. In this work, we are presenting an image segmentation based testing methodology to detect the catastrophic faults and to locate the faulty cells. The design-for diagnosability scheme is proposed, and it is shown that faults can be located and tolerated by providing alternative paths in biochips. Moreover this testing method also facilitates the testing of a biochip with other bioassay operations running concurrently.
关键词： fault models,CCD,Biochip,electrowetting,image segmentation,test time
更新于2025-09-04 15:30:14