Linear Iterative Clustering Algorithm Research Articles

A graph learning approach for automatic subsurface defects segmentation in building façade using RGB and infrared images

Infrared thermal imaging technology provides a non-destructive and automated inspection solution for analyzing building facades. However, conventional methods that rely on detecting subsurface defects through abnormal heat loss often suffer from the limitation of misidentifying other materials present on the building facade. To address this challenge, this study proposes a graph learning approach for segmentation of subsurface defects by integrating both visual and thermal information. The proposed methodology encompasses several key steps. Firstly, the infrared image is partitioned into internally connected superpixels with similar characteristics using a linear iterative clustering algorithm. Subsequently, these superpixels are employed as nodes to construct a weighted undirected graph. The edges of the graph are defined based on the connectivity of superpixels in the image plane, while the weights are determined by the Wasserstein distance between each node. Furthermore, a multimodal graph reuse technique is introduced to construct a new graph based on an RGB image, thereby incorporating additional visual information. By defining the defect as tightly connected in the RGB graph but loosely connected in the infrared graph, the segmentation of defects involving multiple materials is formulated as a multi-graph N-cut problem. The feasibility and effectiveness of the proposed method are demonstrated through laboratory testing conducted on concrete specimens, as well as on-site monitoring carried out on a tiled building façade.

The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping

A breeder can select a visually appealing phenotype, whether for ornamentation or landscaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 > 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals.

An Adaptive Smoothness Parameter Strategy for Variational Optical Flow Model

The smoothness parameter is used to balance the weight of the data term and the smoothness term in variational optical flow model, which plays very significant role for the optical flow estimation, but existing methods fail to obtain the optimal smoothness parameters (OSP). In order to solve this problem, an adaptive smoothness parameter strategy is proposed. First, an amalgamated simple linear iterative cluster (SLIC) and local membership function (LMF) algorithm is used to segment the entire image into several superpixel regions. Then, image quality parameters (IQP) are calculated, respectively, for each superpixel region. Finally, a neural network model is applied to compute the smoothness parameter by these image quality parameters of each superpixel region. Experiments were done in three public datasets (Middlebury, MPI_Sintel, and KITTI) and our self-constructed outdoor dataset with the proposed method and other existing classical methods; the results show that our OSP method achieves higher accuracy than other smoothness parameter selection methods in all these four datasets. Combined with the dual fractional order variational optical flow model (DFOVOFM), the proposed model shows better performance than other models in scenes with illumination inhomogeneity and abnormity. The OSP method fills the blank of the research of adaptive smoothness parameter, pushing the development of the variational optical flow models.

Content-Based Video Retrieval Using Integration of Curvelet Transform and Simple Linear Iterative Clustering

Video is a rich information source containing both audio and visual information along with motion information embedded in it. Applications such as e-learning, live TV, video on demand, traffic monitoring, etc. need an efficient video retrieval strategy. Content-based video retrieval and superpixel segmentation are two diverse application areas of computer vision. In this work, we are presenting an algorithm for content-based video retrieval with help of Integration of Curvelet transform and Simple Linear Iterative Clustering (ICTSLIC) algorithm. Proposed algorithm consists of two steps: off line processing and online processing. In offline processing, keyframes of the database videos are extracted by employing features: Pearson Correlation Coefficient (PCC) and color moments (CM) and on the extracted keyframes superpixel generation algorithm ICTSLIC is applied. The superpixels generated by applying ICTSLIC on keyframes are used to represent database videos. On other side, in online processing, ICTSLIC superpixel segmentation is applied on query frame and the superpixels generated by segmentation are used to represent query frame. Then videos similar to query frame are retrieved through matching done by calculation of Euclidean distance between superpixels of query frame and database keyframes. Results of the proposed method are irrespective of query frame features such as camera motion, object’s pose, orientation and motion due to the incorporation of ICTSLIC superpixels as base feature for matching and retrieval purpose. The proposed method is tested on the dataset comprising of different categories of video clips such as animations, serials, personal interviews, news, movies and songs which is publicly available. For evaluation, the proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Experiments were conducted on the developed dataset and the performance is assessed with different parameters Precision, Recall, Jaccard Index, Accuracy and Specificity. The experimental results shown that the proposed method is performing better than the other state-of-art methods.

Automated Superpixel-borders-guided Deformation Image Registration for Adaptive Radiotherapy

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

ABSTRACT In this study, we propose a method for discriminating crops/weeds in upland rice fields using a commercial unmanned aerial vehicles (UAVs) and red-green-blue (RGB) cameras with the simple linear iterative clustering (SLIC) algorithm and random forest (RF) classifier. In the SLIC-RF algorithm, we evaluated different combinations of input features: three color spaces (RGB, hue-saturation-brightness [HSV], CIE-L*a*b), canopy height model (CHM), spatial texture (Texture) and four vegetation indices (VIs) (excess green [ExG], excess red [ExR], green-red vegetation index [GRVI] and color index of vegetation extraction [CIVE]). Among the color spaces, the HSV-based SLIC-RF model showed the best performance with the highest out-of-bag (OOB) accuracy (0.904). The classification accuracy was improved by the combination of HSV with CHM, Texture, ExG, or CIVE. The highest OOB accuracy (0.915) was obtained from the HSV+Texture combination. The greatest errors from the confusion matrix occurred in the classification between crops and weeds, while soil could be classified with a very high accuracy. These results suggest that with the SLIC-RF algorithm developed in this study, rice and weeds can be discriminated by consumer-grade UAV images with acceptable accuracy to meet the needs of site-specific weed management (SSWM) even in the early growth stages of small rice plants..

Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.

Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages.

Segmenting bioimage based filaments is a critical step in a wide range of applications, including neuron reconstruction and blood vessel tracing. To achieve an acceptable segmentation performance, most of the existing methods need to annotate amounts of filamentary images in the training stage. Hence, these methods have to face the common challenge that the annotation cost is usually high. To address this problem, we propose an interactive segmentation method to actively select a few super-pixels for annotation, which can alleviate the burden of annotators. Specifically, we first apply a Simple Linear Iterative Clustering (i.e., SLIC) algorithm to segment filamentary images into compact and consistent super-pixels, and then propose a novel batch-mode based active learning method to select the most representative and informative (i.e., BMRI) super-pixels for pixel-level annotation. We then use a bagging strategy to extract several sets of pixels from the annotated super-pixels, and further use them to build different Laplacian Regularized Gaussian Mixture Models (Lap-GMM) for pixel-level segmentation. Finally, we perform the classifier ensemble by combining multiple Lap-GMM models based on a majority voting strategy. We evaluate our method on three public available filamentary image datasets. Experimental results show that, to achieve comparable performance with the existing methods, the proposed algorithm can save 40 percent annotation efforts for experts.

AdaSLIC: adaptive supervoxel generation for volumetric medical images

In the last decade, supervoxels have become a useful mid-level representation of volumetric medical images such as MRIs and CT scans. Several methods were suggested to produce uniform supervoxels, yet little has been done to generate content-sensitive over-segmentations. This is particularly beneficial to 3D medical image analysis, where sizes of anatomical structures vary largely. In this paper, we propose AdaSLIC as an adaptive supervoxel generation technique that applies to volumetric medical images. In small structures, it generates tiny supervoxels to capture the details of the image. Meanwhile, it partitions large structures into bigger supervoxels, hence leading to a sparse description. The proposed technique is an extension of the Simple Linear Iterative Clustering (SLIC) algorithm. Rather than using a regular sampling to initiate supervoxel centers, a content-sensitive initialization is performed using a Poisson-disk sampling algorithm (PDS). It relies on a map of distances to the main image contours. The size of each supervoxel depends on the distance of its center to the closest image contour. We compare our algorithm to the SLIC algorithm as well as to an extension of the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise). Two datasets are used for this purpose: knee MRIs and cardiovascular magnetic resonance (CMR) images. We use different metrics to assess the quality of the generated over-segmentations. Experimental results show that our algorithm achieves comparable or better boundary adherence than the state of the art algorithms while producing compact and adaptive supervoxels.

Robust mean shift tracking based on refined appearance model and online update

In this paper, a robust mean shift tracking algorithm based on refined appearance model (RAM) and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model to improve tracking precision and design an online update strategy to adjust to the appearance variation. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model construction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to distinguish the foreground from background. A weighted vector is then obtained based on the classified foreground from background, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on some challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance. A further comprehensive experiment on OTB2013 demonstrates that the proposed tracking algorithm outperforms the state-of-the-art works in most cases.