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The existing infrared image processing technology mainly relies on the traditional segmentation algorithm, which is not only inefficient, but also has problems such as blurred edges, poor segmentation accuracy, and insufficient extraction of key power equipment features for the infrared image defect segmentation of power equipment. A CS_DeeplabV3+ network for the accurate segmentation of the infrared image defect segmentation of power equipment is designed for the situation of leakage and false detection after segmentation by traditional algorithms. The ASPP module is improved in the encoder part to enable the network to obtain a denser pixel sampling, an improved attention mechanism is introduced to enhance the sensitivity and accuracy of the network for feature extraction, and a semantic segmentation feature enhancement module—the structured feature enhancement module (SFEM)—is introduced in the decoder part to enhance the feature processing to improve the segmentation accuracy. The CS_DeeplabV3+ network is validated using the dataset, and the experimental comparison proves that the improved model has finer contours compared with other models for segmenting infrared images of power equipment defects, and MPA is improved by 5.6% and MIOU is improved by 7.3% compared with the DeeplabV3+ network. [ABSTRACT FROM AUTHOR]

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel variational model based on fuzzy region competition and statistical image variation modeling for image segmentation. In the energy functional of the proposed model, each region is characterized by the pixel-level color feature and region-level spatial/frequency information extracted from various image domains, which are modeled by the windowed bit-plane-dependence probability models. To efficiently minimize the energy functional, we apply an alternating minimization procedure with the use of Chambolle's fast duality projection algorithm, where the closed-form solutions of the energy functional are obtained. Our method gives soft segmentation result via the fuzzy membership function, and moreover, the use of multi-domain statistical region characterization provides additional information that can enhance the segmentation accuracy. Experimental results indicate that the proposed method has a superior performance and outperforms the current state-of-the-art superpixel-based and deep-learning-based approaches. [ABSTRACT FROM AUTHOR]

Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis. [ABSTRACT FROM AUTHOR]

Superpixel segmentation for polarimetric synthetic aperture radar (PolSAR) images plays a key role in remote-sensing tasks, such as ship detection and land-cover classification. However, the existing methods cannot directly generate multi-scale superpixels in a hierarchical style and they will take a long time when multi-scale segmentation is executed separately. In this article, we propose an effective and accurate hierarchical superpixel segmentation method, by introducing a minimum spanning tree (MST) algorithm called the Boruvka algorithm. To accurately measure the difference between neighboring pixels, we obtain the scattering mechanism information derived from the model-based refined 5-component decomposition (RFCD) and construct a comprehensive dissimilarity measure. In addition, the edge strength map and homogeneity measurement are considered to make use of the structural and spatial distribution information in the PolSAR image. On this basis, we can generate superpixels using the distance metric along with the MST framework. The proposed method can maintain good segmentation accuracy at multiple scales, and it generates superpixels in real time. According to the experimental results on the ESAR and AIRSAR datasets, our method is faster than the current state-of-the-art algorithms and preserves somewhat more image details in different segmentation scales. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) image segmentation is considered to be a major challenge for object-oriented remote sensing applications that have been extensively studied in the past. In this paper, we propose a fast and efficient framework for multiscale and multifeatured hierarchical image segmentation (MMHS). First, the HSR image pixels were clustered into a small number of superpixels using a simple linear iterative clustering algorithm (SLIC) on modern graphic processing units (GPUs), and then a region adjacency graph (RAG) and nearest neighbors graph (NNG) were constructed based on adjacent superpixels. At the same time, the RAG and NNG successfully integrated spectral information, texture information, and structural information from a small number of superpixels to enhance its expressiveness. Finally, a multiscale hierarchical grouping algorithm was implemented to merge these superpixels using local-mutual best region merging (LMM). We compared the experiments with three state-of-the-art segmentation algorithms, i.e., the watershed transform segmentation (WTS) method, the mean shift (MS) method, the multiresolution segmentation (MRS) method integrated in commercial software, eCognition9, on New York HSR image datasets, and the ISPRS Potsdam dataset. Computationally, our algorithm was dozens of times faster than the others, and it also had the best segmentation effect through visual assessment. The supervised and unsupervised evaluation results further proved the superiority of the MMHS algorithm. [ABSTRACT FROM AUTHOR]

A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a particle-filter-based superpixel (PFS) segmentation method that extends the original tracking problem as a region clustering problem. The basic idea is to approximate superpixel centers by multiple particles to obtain high intra-region similarity. Specifically, we firstly use a density cluster to initialize single-group particles and introduce the association rule for mining other initial candidate particles. In propagation, particles are transferred to neighboring local regions by a moving step aiming to update local candidate particles with a lower energy cost. We evaluate all particles on the basis of their cluster similarity and estimate the largest particles as the final superpixel centers. The proposed method can locate cluster centers in diverse feature space, which alleviates the risk of a local optimum and produces better segmentation performance. Experimental results on the Berkeley segmentation 500 dataset (BSD500) demonstrate that our method outperforms seven state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments. In this work, we propose a novel scheme for supervoxel segmentation to address the problem of new and moving objects, where the segmentation is performed on every two consecutive frames and thus each internal frame has two valid superpixel segmentations. This scheme provides coarse-grained parallel ability, and subsequent algorithms can validate their result using two segmentations that will further improve robustness. To implement this scheme, a voxel-related Gaussian mixture model (GMM) is proposed, in which each supervoxel is assumed to be distributed in a local region and represented by two Gaussian distributions that share the same color parameters to capture temporal consistency. Our algorithm has a lower complexity with respect to frame size than the traditional GMM. According to our experiments, it also outperforms the state-of-the-art in accuracy. [ABSTRACT FROM AUTHOR]

Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically choose parameters of a segmentation algorithm are meant to make the process objective and reproducible. One of those approaches, and perhaps themost frequently used unsupervised parameter optimization method in the context of GEOBIA is called the objective function, also known as Global Score. Unfortunately, the method exhibits a hitherto widely neglected, yet severe source of instability, which makes quality rankings inconsistent. We demonstrate the issue in detail and propose a modification of the Global Score to mitigate the problem. This hopefully serves as a starting point to spark further development of the popular approach. [ABSTRACT FROM AUTHOR]

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods. [ABSTRACT FROM AUTHOR]