Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering

Makoveichuk

et al. 2022

EEJET

The object of this study is the process of segmentation of images acquired from space optoelectronic surveillance systems. The method to segment images from space optoelectronic surveillance systems based on the Sine-Cosine algorithm involves determining the threshold level; unlike the known ones, the following is carried out in it: – preliminary selection of red-green-blue color space brightness channels in the original image; – calculation of the maximum distance of movement of agents in the image in each brightness channel; – calculation of the values that determine the movement of agents in the image in each brightness channel; – determining the position of agents in the image using trigonometric functions of the sine and cosine in each brightness channel. An experimental study into segmenting images acquired from space optoelectronic surveillance systems based on the Sine-Cosine algorithm was carried out. It was found that the improved method of image segmentation based on the Sine-Cosine algorithm makes it possible to segment the images. In this case, objects of interest, snow-covered objects of interest, background objects, and undefined areas of the image (anomalous areas) are identified. The quality of image segmentation was assessed using the Sine-Cosine algorithm-based method. It was found that the improved segmentation method based on the Sine-Cosine algorithm reduces the segmentation error of the first kind by an average of 21 % and the segmentation error of the first kind by an average of 17 %. Methods of image segmentation can be implemented in software and hardware systems that process images acquired from space optoelectronic surveillance systems. Further studies may involve comparing the quality of segmentation by the method based on the Sine-Cosine algorithm with segmentation methods based on evolutionary algorithms (for example, genetic ones).

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm

Makoveichuk

et al. 2022

EEJET

“…In the field of image segmentation, it is a common method to cluster the pixel points with similar color, brightness or texture into the same super-pixel block by using graph theory and clustering theory. At present, normalized cut (NCuts) [10], weighted k-means [11], simple linear iterative clustering (SLIC) [12] and other algorithms are commonly used. However, these algorithms have high computational complexity, they ignore the relationship between local and global images, and fail to make full use of small image feature information.…”

Section: Lsc Super-pixel Segmentationmentioning

confidence: 99%

Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging

Yu¹,

Yu²

2022

Journal of Compurters

<p>The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid development. Infrared image of power equipment contains a lot of noise, and the edge is fuzzy. In this paper, we propose a new infrared image segmentation method for power equipment by using linear spectral clustering and maximal similarity-based region merging under complex backgrounds. In this method, the linear spectral clustering algorithm (LSC) is used to segment the image into super-pixels, and the pixels with similar color and distance are clustered to the same center. The calculated OTSU threshold based on the global image is used to pre-label the background of each super-pixel block. The maximum similarity-based region merging algorithm (MSRM) is utilized to merge the super-pixel blocks. Meanwhile, it obtains the target equipment, the over-segmentation and under-segmentation rates are reduced effectively. Finally, the mathematical morphology algorithm is used to post-process the image. Experimental results show that, compared with other algorithms, this new method can obtain more accurate and complete target equipment under complex background. </p> <p> </p>

“…Finally, receive the clustering and segmentation result of the image. So as to confirm the segmentation capability of the AMR-WT-WTWPSC algorithm, we use two superpixel segmentation methods [43], [44] and the other three novel subspace learning methods to form seven comparison algorithms, namely SLIC-LRR, AMR-WT-LRR, SLIC-JMKSC, AMR-WT-JMKSC, SLIC-LRMKC, AMR-WT-LRMKC, and SLIC-WTSPSC. Then, perform image segmentation experiments with the AMR-WT-WTSPSC method on the BSDS500 dataset [45], The dataset contains a total of 500 natural images, such as: people, animals, plants, buildings and landscapes, etc.…”

Section: B Segmentation Experiments Of Amr-wt-wtspscmentioning

confidence: 99%

Low-Rank Subspace Learning of Multikernel Based on Weighted Truncated Nuclear Norm for Image Segmentation

Wang

et al. 2022

IEEE Access

Previous natural image segmentation algorithms through subspace learning method have over-segmentation issues in the pre-segmentation process, which will compromise the edge information, and the subspace learning model cannot effectively utilize the nonlinear structure in the image data and has weak resistance to multiple noises. To address these problems, a multi-kernel subspace learning method based on weight truncated Schatten-p norm for image segmentation is designed in this paper. First, the original natural image pre-processing operation, which is conducting adaptive morphological reconstruction watershed transformation on the image, then the original pixels are aggregated to form a superpixel image, of which the obtained superpixel block would retain more comprehensive local information; Secondly, perform feature extraction for each superpixel block, and stack the obtained feature vectors into the desired feature matrix; Then, it is input into the weighted truncated Schatten-p low-rank multi-kernel subspace learning model to obtain a similarity matrix with cluster structure on the diagonal; Finally, the similarity matrix is used as the adjacency matrix in the spectral clustering model, and the final feature data clustering and image segmentation results are obtained by the optimization solution. The final experimental results demonstrate that contrasts to existing clustering models, the proposed method accomplishes the best clustering property on two public datasets; Compared with seven segmentation algorithms on the BSDS500 dataset, and achieved the best segmentation effect on two evaluation metrics.