Simple Linear Iterative Clustering Segmentation Research Articles

Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

ABSTRACT Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

A Robust SLIC Based Approach for Segmentation Using Canny Edge Detector

An accurate image segmentation in noisy environment is complex and challenging. Unlike existing state-of-the-art methods that use superpixels for successful segmentation, we propose a new approach for noise-robust SLIC (Simple Linear Iterative Clustering) segmentation that incorporates a Canny edge detector. By leveraging Canny edge information, the proposed method modifies the pixel intensity distance measurement to overcome boundary adherence challenge. Furthermore, we adopt a selective approach to update cluster centers, focusing on pixels that contribute less to the noise. Extensive experiments on synthetic noisy images demonstrate the effectiveness of our approach. It significantly improves SLIC's performance in noisy image segmentation and boundary adherence, making it a promising technique for vision processing tasks.

Zero watermarking scheme for privacy protection in e-Health care

E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.

A Novel Multi-Scale Feature Fusion-Based 3SCNet for Building Crack Detection

Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LBP can improve the performance of the CNN (Convolution Neural Network). The achieved performance of the model can be used to develop a real-time crack detection system.

Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.

In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation.

GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and unmanned aerial system (UAS) imagery to track the spread of diseases, identify diseased regions within cornfields, and notify users with actionable information remains a research gap. Therefore, in this study, high-resolution, UAS-acquired, real-time kinematic (RTK) geotagged, RGB imagery at an altitude of 12 m above ground level (AGL) was used to develop the Geo Disease Location System (GeoDLS), a deep learning-based system for tracking diseased regions in corn fields. UAS images (resolution 8192 × 5460 pixels) were acquired in cornfields located at Purdue University’s Agronomy Center for Research and Education (ACRE), using a DJI Matrice 300 RTK UAS mounted with a 45-megapixel DJI Zenmuse P1 camera during corn stages V14 to R4. A dataset of 5076 images was created by splitting the UAS-acquired images using tile and simple linear iterative clustering (SLIC) segmentation. For tile segmentation, the images were split into tiles of sizes 250 × 250 pixels, 500 × 500 pixels, and 1000 × 1000 pixels, resulting in 1804, 1112, and 570 image tiles, respectively. For SLIC segmentation, 865 and 725 superpixel images were obtained using compactness (m) values of 5 and 10, respectively. Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. DenseNet169 identified diseased, healthy, and background regions with the highest testing accuracy of 100.00% when trained on images of tile size 1000 × 1000 pixels. Using a sliding window approach, the trained DenseNet169 model was then used to calculate the percentage of diseased regions present within each UAS image. Finally, the RTK geolocation information for each image was used to update users with the location of diseased regions with an accuracy of within 2 cm through a web application, a smartphone application, and email notifications. The GeoDLS could be a potential tool for an automated disease management system to track the spread of crop diseases, identify diseased regions, and provide actionable information to the users.

Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

Plant diseases lead to severe losses in crop yield worldwide. The conventional approach for diagnosing diseases relies on manual scouting. In recent years, advances in convolutional neural networks have motivated the use of deep learning-based computer vision for automatically identifying plant diseases. Although image classification techniques are commonly used for analyzing agricultural data, their use for accurately identifying diseased regions corresponding to different disease types on individual plant leaves is limited. In this study, Simple Linear Iterative Clustering (SLIC) segmentation was used on corn leaf images from the PlantVillage and CD&S datasets to create super-pixels, a cluster of pixels representing a region of interest on a corn leaf. The VGG16, ResNet50, DenseNet121, Xception, and InceptionV3, pre-trained deep learning models were utilized to identify diseased regions corresponding to five super-pixel classes (healthy, northern leaf blight (NLB), gray leaf spot (GLS), common rust, and background) for the PlantVillage dataset and four super-pixel classes (NLB, GLS, northern leaf spot, and background) for the CD&S dataset, on corn leaves. After setting the spatial proximity value (sigma) for SLIC segmentation to five, a total of 100 models were trained by using different numbers of segments per image (five and fifteen) in the SLIC algorithm for both datasets and choosing training: testing split ratios of 90:10, 80:20, 70:30, 60:40, and 50:50 for each of the five models. The highest overall testing accuracy of 97.77% was observed after training the DenseNet121 model to identify super-pixels created from the CD&S dataset, belonging to the four classes created using a sigma value of five, five segments per image, and an 80:20 training: testing split ratio. Web and mobile applications were developed to identify diseased regions on corn leaves using the best deep learning model as the classifier. The results suggest that SLIC segmentation on corn leaf images helps accurately identify diseased regions. This research demonstrates the potential of image-based scouting as an efficient alternative to manual scouting for disease monitoring.

A Method for Segmentation of Transformer Oil Level Region Based on Infrared Image

With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.

An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

Rapidly obtaining accurate dense disparity maps has been the focus of stereo matching research. At present, approaches that achieve superior disparity maps require a large amount of computation, which is not suitable for practical applications. To address this issue, this paper proposes an efficient local matching method based on an adaptive exponentially weighted moving average filter and simple linear iterative clustering segmentation algorithm. First, an effective matching cost is introduced to adaptively integrate absolute intensity difference with Census transform, which is robust against texture free and luminance variate. Following this, during the cost aggregation, the exponentially weighted moving average filter and the SLIC segmentation are combined to handle the problems of computing consumption and adaptive expansion of the cost aggregation window. Finally, the dense disparity map is obtained by a winner-takes-all approach and disparity refinement. To demonstrate its efficiency and validity, the method is quantitatively tested and compared to existing approaches on the Middlebury benchmark. The results show that it has a non-occlusion accuracy of 90.66% and an average runtime of 7.01 s on the 2014 Middlebury dataset. Compared with existing competitive methods, the proposed method achieves superior matching results with a lower time cost.

An Integrated Object and Machine Learning Approach for Tree Canopy Extraction from UAV Datasets

Unmanned aerial vehicles (UAV) have emerged as new platforms for acquiring ultra-high resolution images, which are challenging for extraction of features using conventional image processing approaches. Tree canopies are required to be constantly monitored for better planning and management. UAV is currently one way to survey canopies over a large area for precisely estimating their geometry. Conventional segmentation techniques are extensively used for image feature extraction. However, they lack in accuracy and require high computational processing when used for ultra-high resolution UAV datasets. These issues can be handled by superpixel segmentation algorithms which have good boundary adherence and are computationally efficient. Simple linear iterative clustering (SLIC) is a subset of superpixel segmentation technique which uses minimum tuning parameters making it most efficient. As the random forest is known for handling multiple attributes and robustness, it can be used for classifying and extracting features from segmented image generated using SLIC. The present study mainly focuses on the automation for extraction of tree canopies along with their object-based attributes from the UAV dataset. The data acquisition was carried out using Trimble UX5 fixed-wing UAV which was further orthorectified at a spatial resolution of 13 cm. The ortho-image was further segmented using SLIC algorithm. Canopy segments are then identified and classified using random forest, which is then merged into trees objects on the basis of their proximity. Accuracy assessment was then carried out for extracted tree canopies and was found that the aforesaid approach could achieve 93% similarity index. The current study highlights the potential of using SLIC segmentation and random forest classification method for tree canopy extraction from the ultra-high resolution ortho-image derived from UAV platforms.

Uniformity Evaluation of Temperature Field in an Oven Based on Image Processing

Non-uniform temperature distributions in ovens affect the quality of baked goods and raise concerns regarding food safety. Traditional research on oven performance focuses on the heating mechanisms in simulated ovens and does not involve quantitative analysis of baked goods. This study proposes a model for calculating the uniformity of baked goods based on image processing technology, to quantitatively assess the distribution uniformity of different baked states and digitally express the internal temperature field distribution in the oven. First, the image of the baked goods is captured using a digital camera. Then, it is preprocessed to obtain an image containing only the region showing the baked goods. Subsequently, the simple linear iterative clustering segmentation algorithm is employed to extract the baked states. Finally, a meshing model is applied to calculate the distribution variance of each baked state, and the evaluation index describing the uniformity of the baked goods image is obtained by normalizing the variance in the distribution. The simple linear iterative clustering segmentation algorithm expresses the color features of acquired baked goods images in the form of superpixels. By determining the distribution and proportion of different baked states, the proposed method can qualitatively and quantitatively reflect the spatial distribution of the temperature fields inside the oven corresponding to the baked goods image. This provides a strong basis for further evaluation of the heat distribution field inside the oven.

Mineral grains recognition using computer vision and machine learning

Identifying and counting individual mineral grains composing sand is an important component of many studies in environment, engineering, mineral exploration, ore processing and the foundation of geometallurgy. Typically, silt (32-128μm) and sand (128-1000μm) sized grains will be characterized under an optical microscope or a scanning electron microscope. In both cases, it is a tedious and costly process. Therefore, in this paper, we introduce an original computational approach in order to automate mineral grains recognition from numerical images obtained with a simple optical microscope. To the best of our knowledge, it is the first time that the current computer vision based on machine learning algorithms is tested for the automated recognition of such mineral grains. In more details, this work uses the simple linear iterative clustering segmentation to generate superpixels and many of them allow isolating sand grains, which is not possible with classical segmentation methods. Also, the approach has been tested using convolutional neural networks (CNNs). However, CNNs did not give as good results as the superpixels method. The superpixels are also exploited to extract features related to a sand grain. These image characteristics form the raw dataset. Prior to proceed with the classification, a data cleaning stage is necessary to get a usable dataset for machine learning algorithms. In addition, we present a comparison of performances of several algorithms. The overall obtained results are approximately 90% and demonstrate the concept of mineral recognition from a sample of sand grains provided by a numerical image.

Multimorphological Superpixel Model for Hyperspectral Image Classification

With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.

Improved SLIC imagine segmentation algorithm based on K-means

Dividing the image into superpixels contributes to further processing of the image. Simple linear iterative clustering (SLIC) algorithm achieves good segmentation result by clustering color and distance characteristics of pixels. However, finite superpixels easily cause under-segmentation. Therefore, the work corrects segmentation result of SLIC by k-means clustering method calculating similarity based on weighted Euclidean distance. After that, the under-segmentation superpixel blocks are conducted with k-means clustering based on binary classification. Result shows that the corrected SLIC segmentation has better visual effect and index.