• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.4
• /
• pp.151-160
• /
• 2014

In this paper, a comparison of superpixel characteristics based on SLIC(simple linear iterative clustering) for several color feature spaces is presented. Computer vision applications have come to rely increasingly on superpixels in recent years. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. A superpixel is consist of pixels with similar features such as luminance, color, textures etc. Thus superpixels are more efficient than pixels in case of large scale image processing. Generally superpixel characteristics are described by uniformity, boundary precision and recall, compactness. However previous methods only generate superpixels a special color space but lack researches on superpixel characteristics. Therefore we present superpixel characteristics based on SLIC as known popular. In this paper, Lab, Luv, LCH, HSV, YIQ and RGB color feature spaces are used. Uniformity, compactness, boundary precision and recall are measured for comparing characteristics of superpixel. For computer simulation, Berkeley image database(BSD300) is used and Lab color space is superior to the others by the experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.915-917
• /
• 2011

In this paper, a comparison of superpixel characteristics for each color feature space. The superpixel is consist of several pixels with same features such as luminance, color, textures etc. The superpixel can be used on image processing and analysis with large image size to speed up the process. We compare the superpixel characteristics by means of compactness using Berkeley image database(BSD-300).

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.4
• /
• pp.89-97
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.649-651
• /
• 2012

In this paper, a method of segmenting color image using characteristics of superpixels is proposed. A superpixel is consist of several pixels with same features such as luminance, color, textures etc. The superpixel can be used for image processing and analysis with large scale image to get high speed processing. A color image can be transformed to $La^*b^*$ feature space having good characteristics, and the superpixels are grouped by clustering and gradient-based algorithm.

• Journal of Multimedia Information System
• /
• v.7 no.3
• /
• pp.197-204
• /
• 2020

Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of the images in the computer vision applications. It is common to generate superpixels of similar size and shape based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to control the sizes and shapes of generated superpixels, considering the contents of an image. The proposed method consists of two steps. The first step is to over-segment an image so that the boundary information of the image is well preserved. In the second step, generated superpixels are merged based on similarity to produce the target number of superpixels, where the shapes of superpixels are controlled by limiting the maximum size and the proposed roundness metric. Experimental results show that the proposed method preserves the boundaries of the objects in an image more accurately than the existing method.

• The Journal of Korean Institute of Information Technology
• /
• v.16 no.12
• /
• pp.85-92
• /
• 2018

When using complicated image processing algorithms, we use superpixels to reduce computational complexity. Superpixel segmentation is a method of grouping pixels having similar characteristics into one group. Since superpixel is used as a preprocessing of image processing, it should be generated quickly, and the edge components of the image should be well preserved. In this paper, we propose a method of generating superpixels with a small amount of computation while preserving edge components well. In the proposed method, superpixels of an image are generated by using the existing k-mean method, and similar superpixels among the generated superpixels are merged to make final superpixels. When merging superpixels, the similarity is calculated only for superpixels. Therefore, the amount of computation is maintained small. It is shown by experimental results that the superpixel images produced by the proposed method are conserving edge information of the original image better than those produced by the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1520-1527
• /
• 2019

Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of images in the field of computer vision. It is common to generate superpixels with a regular size and form based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to generate superpixels considering the characteristics of an image according to the application. The proposed method consists of two steps, and the first step is to oversegment an image so that the boundary information of the image is well preserved. In the second step, superpixels are merged based on similarity to produce the desired number of superpixels, where the form of superpixels are controlled by limiting the maximum size of superpixels. Experimental results show that the proposed method preserves the boundaries of an image more accurately than the existing method.

• Journal of Biomedical Engineering Research
• /
• v.44 no.3
• /
• pp.176-181
• /
• 2023

During cancer treatment, the patient's response to drugs appears differently at the cellular level. In this paper, an image-based cell phenotypic feature quantification and key feature selection method are presented to predict the response of patient-derived cancer cells to a specific drug. In order to analyze the viability characteristics of cancer cells, high-definition microscope images in which cell nuclei are fluorescently stained are used, and individual-level cell analysis is performed. To this end, first, image stitching is performed for analysis of the same environment in units of the well plates, and uneven brightness due to the effects of illumination is adjusted based on the histogram. In order to automatically segment only the cell nucleus region, which is the region of interest, from the improved image, a superpixel-based segmentation technique is applied using the fluorescence expression level and morphological information. After extracting 242 types of features from the image through the segmented cell region information, only the features related to cell viability are selected through the ReliefF algorithm. The proposed method can be applied to cell image-based phenotypic screening to determine a patient's response to a drug.

• Journal of Korea Multimedia Society
• /
• v.15 no.6
• /
• pp.761-769
• /
• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.