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An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface

INFRARED PHYSICS & TECHNOLOGY 2014年 67卷 266-272页

作者： Xu, Changhang Xie, Jing Chen, Guoming Huang, Weiping China Univ Petr East China Coll Mech & Elect Engn Dept Safety Sci & Engn Qingdao 266580 Shandong Peoples R China Ocean Univ China Coll Engn Qingdao 266071 Peoples R China

Infrared thermography has been used increasingly as an effective non-destructive technique to detect cracks on metal surface. Due to many factors, infrared thermal image has low definition compared to visible image. The contrasts between cracks and sound areas in different thermal image frames of a specimen vary greatly with the recorded time. An accurate detection can only be obtained by glancing over the whole thermal video, which is a laborious work. Moreover, experience of the operator has a great important influence on the accuracy of detection result. In this paper, an infrared thermal image processing framework based on superpixel algorithm is proposed to accomplish crack detection automatically. Two popular superpixel algorithms are compared and one of them is selected to generate superpixels in this application. Combined features of superpixels were selected from both the raw gray level image and the high-pass filtered image. Fuzzy c-means clustering is used to cluster superpixels in order to segment infrared thermal image. Experimental results show that the proposed framework can recognize cracks on metal surface through infrared thermal image automatically. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Infrared thermography Defect detection superpixel algorithm Surface crack Image segmentation

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Optical Flow with Non-local Weight and Fractional Order Regularization: A Variational Model with superpixel algorithm for Various Application Oriented Spectrum

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-31页

作者： Singh, Bhavana Kumar, Pushpendra Maulana Azad Natl Inst Technol Dept Math Bioinformat & Comp Applicat Bhopal 462003 Madhya Pradesh India

The motion estimation has gained significant prominence in the field of computer vision due to its wide range of applications. In general, motion estimation is performed in terms of optical flow, which is represented by a vector plot and color maps. The objective of the work is to combine the global and local optical flow, and provide the dense and discontinuity preserving robust optical flow in various application-oriented spectra. A non-local weighted fractional order variational optical flow (NLW-FOOF) model is presented using the Marchaud fractional derivative and the superpixel algorithm. The global nature of the model provides the information about the overall motion present in the scene, while the local nature focuses on the individual motion of each object within the image frame. Thus, the fusion of local-global model along with superpixel algorithm yield the dense flow field, and the Marchaud fractional derivative deals with the textures and edge discontinuities effectively and also provides a significant robustness against outliers. An ablation study is conducted to show the significance of each component of the proposed model. The variational functional is minimized using Euler-Lagrange equations. The Marchaud derivative is numerically discretized through Gr & uuml;nwald-Letnikov derivative method. The resulting system of equations is solved using an efficient iteration technique. The experiments are performed on heterogeneous datasets, and results are evaluated both qualitatively and quantitatively. The performance of the proposed model is shown by comparing it with existing models. The robustness of the NLW-FOOF model is elaborated by estimating optical flow under four different noises.

关键词： Marchaud fractional derivative Non-local weight Optical flow superpixel algorithm Variational technique

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Recognition and segmentation of complex texture images based on superpixel algorithm and deep learning

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COMPUTATIONAL MATERIALS SCIENCE 2022年第0期209卷

作者： Han, Yuexing Yang, Shen Chen, Qiaochuan Shanghai Univ Sch Comp Engn & Sci 99 Shangda Rd Shanghai 200444 Peoples R China Zhejiang Lab Hangzhou 311100 Zhejiang Peoples R China

Recognizing and segmenting complex texture images such as materials is of great significance to industrial design and production. Due to the lack of sufficient training samples and fuzzy boundaries in material images, it is difficult to segment material images by using deep learning methods. In material images, the pixels of each phase have a high degree of similarity, so if partial pixels' features in each phase are learned, the whole phase can be recognized. In this paper, we propose a method based on deep learning for recognizing and segmenting material images with complex textures. Firstly, the simple linear iterative cluster(SLIC) algorithm is used to obtain different numbers of superpixels which are a group of pixels with similar texture features. Then we extract the largest inscribed rectangular block in each superpixel. Next, put these rectangular blocks into the classical convolutional neural network(CNN)-DenseNet to recognize them. To retain the key texture features and reduce redundant information, we increase the receptive field in the key layers of DenseNet. In addition, due to the uneven distribution of phases in the material images, we improve focal loss to fit the material image. We make extensive comparative and ablation experiments to confirm the effectiveness of our method.

关键词： Material image segmentation superpixel algorithm DenseNet Focal loss

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Automatic stomata recognition and measurement based on improved YOLO deep learning model and entropy rate superpixel algorithm

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ECOLOGICAL INFORMATICS 2022年 68卷 101521-101521页

作者： Zhang, Fan Ren, Fangtao Li, Jieping Zhang, Xinhong Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Sch Life Sci Kaifeng 475004 Peoples R China Henan Univ Sch Software Kaifeng 475004 Peoples R China

The traditional methods of analyzing stomatal traits are mostly manual observation and measurement. These methods are time-consuming, labor-intensive, and inefficient. Some methods have been proposed for the automatic recognition and counting of stomata, however most of those methods could not complete the automatic measurement of stomata parameters at the same time. Some non-deep learning methods could automatically measure the parameters of stomata, but they could not complete the automatic recognition and detection of stomata. In this paper, a deep learning-based method was proposed for automatically identifying, counting and measuring stomata of maize (Zea mays L.) leaves at the same time. An improved YOLO (You Only Look Once) deep learning model was proposed to identify stomata of maize leaves automatically, and an entropy rate superpixel algorithm was used for the accurate measurement of stomatal parameters. According to the characteristics of the stomata images data set, the network structure of YOLOv5 was modified, which greatly reduced the training time without affecting the recognition performance. The predictor in YOLO deep learning model was optimized, which reduced the false detection rate. At the same time, the 16-fold and 32-fold down-sampling layers were simplified according to the characteristics of stomatal objects, which improved the recognition efficiency. Experimental results showed that the recognition precision of the improved YOLO deep learning model reached 95.3% on the maize leaves stomatal data set, and the average accuracy of parameter measurement reached 90%. The proposed method could fully automatically complete the recognition, counting and measurement of stomata of plants, which can help agricultural scientists and botanists to conduct large-scale researches of stomatal morphology, structure and physiology, as well as the researches combined with genetic analysis or molecular-level analysis.

关键词： Stomata Deep learning YOLO superpixel algorithm

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Adaptive superpixel-Guided Non-Homogeneous Image Dehazing

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 591-595页

作者： Zhang, Hao Lu, Ping Qi, Te Xu, Yan Zeng, Tieyong Chinese Univ Hong Kong Dept Math Hong Kong Peoples R China ZTE Corp Shenzhen 518057 Peoples R China

Image dehazing is regarded as a fundamental image processing task with a major impact on higher-level imaging tasks. Many existing haze removal methods are designed for homogeneous haze, but in real-world cases, the haze is normally non-homogeneous. superpixels, which segment an image into a set of closely spaced regions, can be employed in real-world scenarios to deal with non-homogeneous haze. In our paper, an adaptive non-homogeneous image dehazing approach that utilizes the superpixel-guided algorithm is designed to segment different hazy regions. Considering that both ambient light and transmission map estimation have a significant impact on the results, our research focuses on the development of a variational dehazing model that takes into account non-uniform ambient light and non-uniform transmission maps to address varying levels of haze. A series of numerical results illustrate the superiority and efficacy of our method.

关键词： Scattering Signal processing algorithms Mathematical models Atmospheric modeling Image segmentation Optimization Feature extraction Numerical models Lighting Estimation Image dehazing non-homogeneous superpixel algorithm

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Saliency Detection via Manifold Ranking on Multi-Layer Graph

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IEEE ACCESS 2024年 12卷 6615-6627页

作者： Wang, Suwei Ning, Yang Li, Xuemei Zhang, Caiming Shandong Univ Sch Software Jinan 250101 Shandong Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China

Saliency detection is increasingly a crucial task in the computer vision area. In previous graph-based saliency detection, superpixels are usually regarded as the primary processing units to enhance computational efficiency. Nevertheless, most methods do not take into account the potential impact of errors in superpixel segmentation, which may result in incorrect saliency values. To address this issue, we propose a novel approach that leverages the diversity of superpixel algorithms and constructs a multi-layer graph. Specifically, we segment the input image into multiple sets by different superpixel algorithms. Through connections within and connections between these superpixel sets, we can mitigate the errors caused by individual algorithms through collaborative solutions. In addition to spatial proximity, we also consider feature similarity in the process of graph construction. Connecting superpixels that are similar in feature space can force them to obtain consistent saliency values, thus addressing challenges brought by the scattered spatial distribution and the uneven internal appearance of salient objects. Additionally, we use the two-stage manifold ranking to compute the saliency value of each superpixel, which includes a background-based ranking and a foreground-based ranking. Finally, we employ a mean-field-based propagation method to refine the saliency map iteratively and achieve smoother results. To evaluate the performance of our approach, we compare our work with multiple advanced methods in four datasets quantitatively and qualitatively.

关键词： Saliency detection Manifolds Image edge detection Image segmentation Computational modeling Image color analysis Manifold ranking multi-layer graph superpixel algorithm saliency detection

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License Plate Detection and Character Segmentation Using Adaptive Binarization Based on superpixels under Illumination Change

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2017年第6期E100D卷 1384-1387页

作者： Kim, Daehun Ku, Bonhwa Han, David K. Ko, Hanseok Korea Univ Sch Elect Engn Seoul 02841 South Korea Off Naval Res Arlington VA 22217 USA

In this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

关键词： license plate detection license plate character segmentation binarization superpixel algorithm

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Divergence based SLIC

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ELECTRONICS LETTERS 2019年第14期55卷 783-+页

作者： Gupta, A. K. Seal, A. Khanna, P. PDPM IHTDM Jabalpur India

The success of cluster analysis for revealing natural grouping in a dataset depends heavily on the chosen dissimilarity measure. Recently, several attempts have been made to replace the popular Euclidean distance measure for dissimilarity with divergences that are known to disobey triangular inequality. In this Letter, a representative partitioning based superpixel algorithm called Simple Linear Iterative Clustering (SLIC) is experimented with a divergence measure. In particular, the Jeffery divergence is employed for dissimilarity computation between colour-image plane space vectors while performing cluster assignment. Despite being a non-metric, the Jeffery divergence based SLIC has shown better boundary adherence performance on BSDS500 dataset as compared to the conventional SLIC algorithm.

关键词： image colour analysis iterative methods pattern clustering image resolution divergence based SLIC cluster analysis triangular inequality representative partitioning superpixel algorithm Simple Linear Iterative Clustering divergence measure Jeffery divergence dissimilarity computation colour-image plane space vectors cluster assignment BSDS500 dataset dissimilarity measure

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superpixel GROUP-CORRELATION NETWORK FOR CO-SALIENCY DETECTION 29

SUPERPIXEL GROUP-CORRELATION NETWORK FOR CO-SALIENCY DETECTI...

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IEEE International Conference on Image Processing (ICIP)

作者： Lee, Minhyeok Park, Chaewon Cho, Suhwan Lee, Sangyoun Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Co-saliency detection is a task to segment the occurring salient objects in a group of images. The biggest challenges are distracting objects in the background and ambiguity between the foreground and background. To handle these issues, we propose a novel superpixel group-correlation network (SGCN) architecture that uses a superpixel algorithm to obtain various component features from a group of images and creates a group-correlation matrix to detect the common components of those images. In this way, non-common objects can be effectively excluded from consideration, enabling a clear distinction between foreground and background. Our method outperforms current state-of-the-art methods on three popular benchmark datasets for co-saliency detection, and our extensive experiments thoroughly validate our claimed contributions.

关键词： Co-saliency Detection superpixel algorithm

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superpixel Approach in High Resolution Histopathological Image Segmentation 25

Superpixel Approach in High Resolution Histopathological Ima...

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Albayrak, Abdulkadir Bilgin, Gokhan Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781509064946

Segmentation of cellular structures with high accuracy has a crucial importance for the detection of cancerous regions in histopathologic images. The proper segmentation of cellular structures is one of the most important issues to be considered when making a diagnosis by pathologists. In this study, the contribution of the superpixel method to the segmentation of high-resolution histopathologic images of renal cell carcinoma from the TCGA (The Cancer Genome Atlas) data set was investigated. The superpixel method performs clustering based on color similarities and spatial proximity of the pixels in histopathologic images. When the results are evaluated, it has been observed that the superpixel method has a positive contribution to both the segmentation success and the running time.

关键词： Histopathological images cell segmentation superpixel algorithm segmentation accuracy

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