• Title/Summary/Keyword: Pixel clustering

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A Method of Color Image Segmentation Based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) Using Compactness of Superpixels and Texture Information (슈퍼픽셀의 밀집도 및 텍스처정보를 이용한 DBSCAN기반 칼라영상분할)

  • Lee, Jeonghwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.11 no.4
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    • pp.89-97
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    • 2015
  • In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

A new Clustering Algorithm for the Scanned Infrared Image of the Rosette Seeker (로젯 탐색기의 적외선 주사 영상을 위한 새로운 클러스터링 알고리즘)

  • Jahng, Surng-Gabb;Hong, Hyun-Ki;Doo, Kyung-Su;Oh, Jeong-Su;Choi, Jong-Soo;Seo, Dong-Sun
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.37 no.2
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    • pp.1-14
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    • 2000
  • The rosette-scan seeker, mounted on the infrared guided missile, is a device that tracks the target It can acquire the 2D image of the target by scanning a space about target in rosette pattern with a single detector Since the detected image is changed according to the position of the object in the field of view and the number of the object is not fixed, the unsupervised methods are employed in clustering it The conventional ISODATA method clusters the objects by using the distance between the seed points and pixels So, the clustering result varies in accordance with the shape of the object or the values of the merging and splitting parameters In this paper, we propose an Array Linkage Clustering Algorithm (ALCA) as a new clustering algorithm improving the conventional method The ALCA has no need for the initial seed points and the merging and splitting parameters since it clusters the object using the connectivity of the array number of the memory stored the pixel Therefore, the ALCA can cluster the object regardless of its shape With the clustering results using the conventional method and the proposed one, we confirm that our method is better than the conventional one in terms of the clustering performance We simulate the rosette scanning infrared seeker (RSIS) using the proposed ALCA as an infrared counter countermeasure The simulation results show that the RSIS using our method is better than the conventional one in terms of the tracking performance.

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Parallel Processing of k-Means Clustering Algorithm for Unsupervised Classification of Large Satellite Images: A Hybrid Method Using Multicores and a PC-Cluster (대용량 위성영상의 무감독 분류를 위한 k-Means Clustering 알고리즘의 병렬처리: 다중코어와 PC-Cluster를 이용한 Hybrid 방식)

  • Han, Soohee;Song, Jeong Heon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.6
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    • pp.445-452
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    • 2019
  • In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

Building Change Detection Using Deep Learning for Remote Sensing Images

  • Wang, Chang;Han, Shijing;Zhang, Wen;Miao, Shufeng
    • Journal of Information Processing Systems
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    • v.18 no.4
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    • pp.587-598
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    • 2022
  • To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

Improving Clustering-Based Background Modeling Techniques Using Markov Random Fields (클러스터링과 마르코프 랜덤 필드를 이용한 배경 모델링 기법 제안)

  • Hahn, Hee-Il;Park, Soo-Bin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.1
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    • pp.157-165
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    • 2011
  • It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

Unsupervised Single Moving Object Detection Based on Coarse-to-Fine Segmentation

  • Zhu, Xiaozhou;Song, Xin;Chen, Xiaoqian;Lu, Huimin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.6
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    • pp.2669-2688
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    • 2016
  • An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.

Object-Based Image Search Using Color and Texture Homogeneous Regions (유사한 색상과 질감영역을 이용한 객체기반 영상검색)

  • 유헌우;장동식;서광규
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.6
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    • pp.455-461
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    • 2002
  • Object-based image retrieval method is addressed. A new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and texture features are extracted from each pixel in the image. These features we used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terns of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In retrieval case, two comparing schemes are proposed. Comparing between one query object and multi objects of a database image and comparing between multi query objects and multi objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into database.

The Binarization of Text Regions in Natural Scene Images, based on Stroke Width Estimation (자연 영상에서 획 너비 추정 기반 텍스트 영역 이진화)

  • Zhang, Chengdong;Kim, Jung Hwan;Lee, Guee Sang
    • Smart Media Journal
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    • v.1 no.4
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    • pp.27-34
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    • 2012
  • In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

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An Automatic Cut Detection Algorithm Using Median Filter And Neural Network ITC-CSCC'2000

  • Jun, Seung-Chul;Park, Sung-Han
    • Proceedings of the IEEK Conference
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    • 2000.07b
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    • pp.1049-1052
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    • 2000
  • In this paper, an efficient method to find cut in the MPEG stream data is proposed. For this purpose, histogram difference and pixel difference is considered as a noise signal. The signal is then filtered out by a median filter to make the frame difference larger. The frame difference obtained in this way is classified into cut frame and non-cut frame by the 2-means clustering without using any threshold value. To improve the classification ratio, a back-propagation neural network is constructed, where outputs of 2-means clustering are used as the inputs of the network. The simulation results demonstrate the performance of the proposed methods.

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Lip Shape Model and Lip Localization using Shape Clustering (형태 군집화를 이용한 입술 형태 모델과 입술 추출)

  • 장경식
    • Journal of Korea Multimedia Society
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    • v.6 no.6
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    • pp.1000-1007
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    • 2003
  • In this paper, we propose an efficient method for locating lip. The lip shape is represented as a set of points based on Point Distribution Model. We use the Isodata clustering algorithm to find clusters for all training data. For each cluster, a lip shape model is calculated using principle component analysis. For all training data, a lip boundary model is calculated based on the pixel values around the lip boundary. To decide whether a recognition result is correct, we use a cost function based on the lip boundary model. Because of using different models according to the lip shapes, our method can localize correctly the flu far from the mean shape. The experiments have been performed for many images, and show correct recognition rate of 92%.

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