SLIC Superpixel Segmentation Research Articles

Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation.

Introduction: Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons. Method: PDB-SLIC combines the SLIC superpixel segmentation algorithm with DBSCAN clustering and underwent rigorous testing using an extensive dataset of T2-weighted mid-sagittal MR images from 4,258 patients across ten hospitals in China. The efficacy of PDB-SLIC was compared against other algorithms and networks in terms of superpixel segmentation quality and vertebral body segmentation accuracy. Validation included a comparative analysis of manual and automated measurements of cervical sagittal parameters and scrutiny of PDB-SLIC's measurement stability across diverse hospital settings and MR scanning machines. Result: PDB-SLIC outperforms other algorithms in vertebral body segmentation quality, with high accuracy, recall, and Jaccard index. Minimal error deviation was observed compared to manual measurements, with correlation coefficients exceeding 95%. PDB-SLIC demonstrated commendable performance in processing cervical spine T2-weighted MR images from various hospital settings, MRI machines, and patient demographics. Discussion: The PDB-SLIC algorithm emerges as an accurate, objective, and efficient tool for evaluating cervical spine sagittal balance, providing valuable assistance to spinal surgeons in preoperative assessment, surgical strategy formulation, and prognostic inference. Additionally, it facilitates comprehensive measurement of sagittal balance parameters across diverse patient cohorts, contributing to the establishment of normative standards for cervical spine MR imaging.

Grid Sample Based Temporal Iteration for Fully Pipelined 1-ms SLIC Superpixel Segmentation System

Grid Sample Based Temporal Iteration for Fully Pipelined 1-ms SLIC Superpixel Segmentation System

Stereo matching algorithm based on improved census transform and minimum spanning tree cost aggregation

Existing stereo matching algorithms suffer from issues such as susceptibility to distortion, weak noise resistance, and a high rate of mismatches in regions with weak textures and discontinuous disparities. To address these challenges, this paper proposes a stereo matching algorithm based on an improved census transform and minimum spanning tree (MST) cost aggregation. In the cost calculation phase, we employ a Gaussian-weighted transformation window and incorporate gradient and edge information to perform weighted fusion of the results. In the cost aggregation process, we introduce a collaborative adaptive window. Each pixel acquires information from the support window of the guided filter (GF) and other pixels within the MST. Furthermore, we integrate the SLIC superpixel segmentation algorithm into MST construction. These two components work synergistically to assign appropriate adaptive weights to pixels, facilitating coordinated cost volume aggregation. Different optimization methods are applied to address mismatched points of various types in post-disparity processing.Performance evaluation using the Middlebury dataset and KITTI dataset demonstrates that our proposed algorithm not only enhances matching accuracy in regions with discontinuous disparities and weak textures but also exhibits significantly improved robustness to interference. Additionally, the resulting disparity map displays smoother edges.

Harvesting Route Detection and Crop Height Estimation Methods for Lodged Farmland Based on AdaBoost

Addressing the challenge of the current harvester route detection method’s reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester’s operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations.

A Strategy of Parallel SLIC Superpixels for Handling Large-Scale Images over Apache Spark

Superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories—even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implemented faster relative to the repeat calculation strategy, especially for limited calculation resources. Therefore, the proposed strategy is more suitable for superpixel segmentation algorithms. In addition, this research accumulates experience for expanding the abundant sequential algorithms to the distributed environment and provides more solutions for large-scale image processing demands.

SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image

SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image

A New Clustering Algorithm Toward Building Segmentation From Aerial Images by Utilizing RGB‐Component Differences

AbstractFor the image segmentation task, deep features‐based deep learning methods have strict data requirements on the quality and quantity, which limits their application in irregular scenes, such as the villages subjected to geological hazards in the complex mountains. However, traditional algorithms with shallow and middle features are unable to utilize the complex information of unmanned aerial vehicle (UAV) remote sensing images effectively. To address this issue, we proposed a new image segmentation algorithm by using the RGB component difference clustering (RGBCDC). In this method, after dimensionality reduction of RGB color space from three dimensions to two dimensions, the distance between similar objects would be diminished and the distance between disturbance areas would be enlarged. Two different UAV data sets (3,000 in total) have been used to compare the proposed algorithm with the traditional segmentation algorithms and the deep learning‐based methods. Results show that the average pixel accuracy is 13.11%, 11.86%, 13.84%, 8.00%, and 9.86% higher than Octree Quantization clustering algorithm, Region Growing algorithm, Watershed algorithm, K‐Means algorithm, and SLIC superpixel segmentation algorithm, respectively. When facing insufficient samples, the new method performed better than deep learning algorithms, such as Deeplabv3, PSPnet, UNET, and UISBB. In general, the proposed algorithm for building segmentation shows a better potential for use in uncommon situations, especially for rapid emergency rescue after serious mountain hazards.

Multi-Source Image Registration Based on Slic Superpixel Segmentation and Autoencoder

A new Harris-AE feature matching algorithm based on image edge information combined with SLIC superpixel segmentation method is proposed to achieve high-precision registration of visible and infrared images. First, perform edge enhancement and histogram equalization processing on visible light and infrared images, superpixel segmentation technology is used to eliminate sub-images with low information entropy, and then by constructing a multi-scale Gaussian pyramid to detect the Harris corners of the image edge information, using autoencoder neural network to generate feature descriptors corresponding to feature points, and match the feature points of two images through the fast nearest-neighbor algorithm with bidirectional matching strategy. Finally, the RANSAC algorithm is used to purify the matching points, and the spatial geometric change parameters between the two images are estimated by the least-squares solution to complete the registration of visible light and infrared images. Experiments have proved that this algorithm improves the accuracy of matching while shortening the registration time.

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

Using high-resolution satellite image to detect change has been a hotspot in the field of remote sensing for a long time series. The change detection method combining feature extraction and machine learning could extract the change information effectively, but the manual sample selection is a huge workload for a wide range remote sensing images, and it is also difficult to ensure the accuracy of the pre-detection sample using a single difference image. Therefore, in this paper, a new method for change detection has been put forward based on multi-feature fusion of D-S evidence theory. In this approach, the texture difference image has calculated by structural similarity, because the difference image based on structural similarity plays a great role in change detection, which was verified in experiments. The difference images based on texture features and traditional spectral features are fused by D-S evidence theory, and texture features and spectral features have been fully utilized. Setting rules to select samples with high confidence based on pixels, and SLIC super-pixel segmentation has applied in order to improve further the credibility of the sample. Finally, the samples selected by SLIC segmentation optimization are sent to the classifier training to obtain the final result. The experimental results show that texture features play a very important role in the change detection of high-resolution remote sensing images, and D-S evidence theory could effectively fuse spectral texture features to improve the accuracy of change detection. The proposed method has high accuracy and good performance in change detection.

Dense Disparity Computing Method Based on Mesh Aggregation and Snake Optimization for Stereo Vision

In order to generate continuous and dense disparity images, a stereo matching method based on mesh aggregation and Snake optimization is proposed in this article. First, the reference pixels are obtained, so as to improve the suppression effect of the brightness difference in Census transform and improve the accuracy of initial matching cost calculation. Second, the image is divided by SLIC super pixel segmentation method, and the neighborhood pixels are searched according to the mesh search in the region, and the matching cost of these pixels are aggregated together according to the corresponding weight to complete cost aggregation of the pixels to be matched. Third, the Snake algorithm is used in optimizing the boundary of the disparity region. Eight classes of images on the Middlebury platform are selected as the test images, and the four algorithms on the Middlebury platform are selected as reference algorithms to carry out the experimental research. The experimental results show that proportion to bad pixels is low and disparity is continuous and dense on the disparity image calculated by the algorithm proposed in this article. Performance of the proposed method is close to LocalExp algorithm which is the best on the Middlebury platform, and the proposed method can be better applied in the stereo vision.

DNN Based Moth Search Optimization for Video Forgery Detection

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1-Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

SLIC segmentation method for full‐polarised remote‐sensing image

The rapid development of remote-sensing data acquisition technology means that the resolution of remote-sensing image has been continuously improved, resulting in the large scale of remote-sensing image data and the increase of redundant information, which restricts the image processing and analysis. However, super-pixel segmentation method mainly aimed at the general image segmentation algorithm, rarely for remote-sensing image. Therefore, here, the polarisation decomposition of full-polarised remote-sensing images is used to synthesise pseudo-colour images. Then, based on the region, the image segmentation algorithm based on the colour feature of SLIC super-pixel segmentation algorithm is used to make it have important application value in remote-sensing image target extraction. Here, the image of Danjiangkou reservoir obtained by high-resolution space-borne SAR is selected as the research object. Finally, the polarisation decomposition and segmentation of full-polarised remote-sensing images are realised, and the advantages of SLIC super-pixel algorithm are proved.

基于ORB和SLIC超像素分割的特征点匹配方法

基于ORB和SLIC超像素分割的特征点匹配方法

Correction: A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

[This corrects the article DOI: 10.1371/journal.pone.0192246.].

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES WITH LOCAL ITERATIVE CLUSTERING AND HETEROGENEOUS STATISTICAL MODEL

Abstract. Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel segmentation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed segmentation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Cellular matrix model for parallel combinatorial optimization algorithms in Euclidean plane

We propose a parallel computation model, called cellular matrix model (CMM), to address large-size Euclidean graph matching problems in the plane. The parallel computation takes place by partitioning the plane into a regular grid of cells, each cell being affected to a single processor. Each processor operates on local data, starting from its cell location and extending its search to the neighborhood cells in a spiral search way. In order to deal with large-size problems, memory size and processor number are fixed as O(N), where N is the problem size. Then one key point is that closest point searching in the plane is performed in O(1) expected time for uniform or bounded distribution, for each processor independently. We define a generic loop that models the parallel projection between graphs and their matching, as executed by the many cells at a given level of computation granularity. To illustrate its efficacy and versatility, we apply the CMM, on GPU platforms, to two problems in image processing: superpixel segmentation and stereo matching energy minimization. Firstly, we propose an extended version of the well-known SLIC superpixel segmentation algorithm, which we call SPASM algorithm, by using a parallel 2D self-organizing map instead of k-means algorithm. Secondly, we investigate the idea of distributed variable neighborhood search, and propose a parallel search heuristic, called distributed local search (DLS), for global energy minimization of stereo matching problem. We evaluate the approach with regards to the state-of-the-art graph cut and belief propagation algorithms. For each problem, we argue that the parallel GPU implementation provides new competitive quality/time trade-offs, with substantial acceleration factors as the problem size increases.

Multimodality semantic segmentation based on polarization and color images

Semantic segmentation gives a meaningful class label to every pixel in an image. It enables intelligent devices to understand the scene and has received sufficient attention during recent years. Traditional imaging systems always apply their methods on RGB, RGB-D or even RGB combined with geometric information. However, for outdoor applications, strong reflection or poor illumination appears to reduce the visualization of the real shape or texture of the objects, thus limiting the performance of semantic segmentation algorithms. To tackle this problem, this paper adopts polarization imaging as it can provide complementary information by describing some imperceptible light properties, which varies from different materials. For acceleration, SLIC superpixel segmentation is used to speed up the system. HOG and LBP features are extracted from both color and polarization images. After quantization using visual codebooks, Joint Boosting classifier is trained to label each pixel based on the quantized features. The proposed method was evaluated both on Day-set and Dusk-set. The experimental results show that using polarization setup can provide complementary information to improve the semantic segmentation accuracy. Especially, a large improvement on Dusk-set shows its capacity for intelligent vehicle applications under dark illumination condition.