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Superpixel segmentation is a popular preprocessing tool in the field of image processing. Nevertheless, conventional planar superpixel generation algorithms are inadequately suited for segmenting symmetrical spherical images due to the distinctive geometric differences. In this paper, we present a novel superpixel algorithm termed context identity and contour intensity (CICI) that is specifically tailored for spherical scene segmentation. By defining a neighborhood range and regional context identity, we propose a symmetrical spherical seed-sampling method to optimize both the quantity and distribution of seeds, achieving evenly distributed seeds across the panoramic surface. Additionally, we integrate the contour prior to superpixel correlation measurements, which could significantly enhance boundary adherence across different scales. By implementing the two-fold optimizations on the non-iterative clustering framework, we achieve synergistic CICI to generate higher-quality superpixels. Extensive experiments on the public dataset confirm that our work outperforms the baselines and achieves comparable results with state-of-the-art superpixel algorithms in terms of several quantitative metrics. [ABSTRACT FROM AUTHOR]

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies. [ABSTRACT FROM AUTHOR]

In order to improve the security effect of image information, this paper studies the reversible information hiding of the digital images combined with the pixel combination algorithm and proposes an improved simulated annealing algorithm using the incremental calculation method of statistical functions. Moreover, according to the gray gradient information contained in the image, this paper proposes an automatic threshold determination algorithm suitable for the unimodal gray distribution images and uses this algorithm to complete the threshold determination and binary segmentation of all images. In addition, this paper optimizes the model by transforming the isolated pores inside the model and removing the isolated matrix area. Through comparative experiments, it can be seen that the reversible information hiding method for digital images based on the pixel combination algorithm proposed in this paper has a good image information confidentiality effect. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency and insufficient accuracy of threshold solution in multithreshold sculpture image segmentation, this paper proposes a threshold segmentation algorithm for sculpture images based on sparse decomposition. In this paper, sparse decomposition is introduced to optimize the model to reduce the impact of local noise on segmentation accuracy, and an energy functional based on pixel coconstraint is built to make up for the defect that pixels cannot retain local details. At the same time, the weighted sum of elite solution sets is used to determine Neighborhood centers increase communication between groups. Experiments show that compared with other algorithms, the above method has significant advantages in convergence efficiency and accuracy. [ABSTRACT FROM AUTHOR]

To address the vulnerability of the widely used block permutation and co-XOR (BPCX) encryption algorithm in reversible data-hiding in the encrypted domain (RDH-ED), which is susceptible to known-plaintext attacks (KPAs), and to enhance embedding capacity, we propose a novel technique of reversible data-hiding in encrypted images (RDH-EI). This method incorporates adaptive quadtree partitioning and most significant bit (MSB) prediction. To counteract KPAs, we introduce pixel modulation specifically targeting pixels within blocks of the same level during the encryption phase. During data embedding, we utilize tagging bits to indicate the state of the pixel blocks, capitalizing on pixel redundancy within those blocks to augment embedding capacity. Our experimental results demonstrate that our method not only achieves reversibility and separability but also significantly boosts embedding capacity and method security. Notably, the average embedding rate across the 10,000 images tested stands at 2.4731, surpassing previous methods by 0.2106 and 0.037, respectively. [ABSTRACT FROM AUTHOR]

With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time. [ABSTRACT FROM AUTHOR]

Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat + bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details. [ABSTRACT FROM AUTHOR]

Traditional saliency detection algorithms lack object semantic character, and the segmentation algorithms cannot highlight the saliency of the segmentation regions. In order to compensate for the defects of these two algorithms, the salient object segmentation model, which is a novel combination of two algorithms, is established in this paper. With the help of a priori knowledge of image boundary background traits, the K-means++ algorithm is used to cluster the pixels for each region; in line with the sensitivity of the human eye to color and with its attention mechanism, the joint probability distribution of the regional contrast ratio and spatial saliency is established. The selection of the salient area is based on the probabilities, for which the region boundary is taken as the initial curve, and the level-set algorithm is used to perform the salient object segmentation of the image. The curve convergence condition is established according to the confidence level for the segmented region, thus avoiding over-convergence of the segmentation curve. With this method, the salient region boundary is adjacent to the object contour, so the curve evolution time is shorter, and compared with the traditional Li algorithm, the proposed algorithm has higher segmentation evaluation scores, with the additional benefit of emphasizing the importance of the object. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results. [ABSTRACT FROM PUBLISHER]

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]

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial–temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods. [ABSTRACT FROM AUTHOR]

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set. [ABSTRACT FROM AUTHOR]

WT (Wavelet Transform) is considered as landmark for image compression because it represents a signal in terms of functions which are localized both in frequency and time domain. Wavelet sub-band coding exploits the self-similarity of pixels in images and arranges resulting coefficients in different sub-bands. A much simpler and fully embedded codec algorithm SPIHT (Set Partitioning in Hierarchical Trees) is widely used for the compression of wavelet transformed images. It encodes the transformed coefficients depending upon their significance comparative to the given threshold. Statistical analysis reveals that the output bit-stream of SPIHT comprises of long trail of zeroes that can be further compressed, therefore SPIHT is not advocated to be used as sole mean of compression. In this paper, wavelet transformed images have been initially compressed by using SPIHT technique and to attain more compression, the output bit streams of SPIHT are then fed to entropy encoders; Huffman and Arithmetic encoders, for further de-correlation. The comparison of two concatenations has been carried out by evaluating few factors like Bit Saving Capability, PSNR (Peak Signal to Noise Ratio), Compression Ratio and Elapsed Time. The experimental results of these cascading demonstrate that SPIHT combined with Arithmetic coding yields better compression ratio as compared to SPIHT cascaded with Huffman coding. Whereas, SPIHT once combined with Huffman coding is proved to be comparatively efficient. [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]

The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained “in one shot” at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking. [ABSTRACT FROM PUBLISHER]

Classification of digital image content is mainly done by identifying low level image features such as corners and edges. The literature shows variety of algorithms for the identification of corner and non-corner pixels, important for objects' identification and image segmentation. However, all of these algorithms produce different results for same data and therefore, suitable for limited applications. This paper proposes a hybrid solution of combining complementary corner detection algorithms to improve image pixels' classification. This has been done by identifying the best detection algorithm for corner points with small and large angles and producing a hybrid algorithm by combining the latter two. Results have shown that Harris detector combined with Global and Local Curvature Points (GLC) improved the detection rate by 28% in synthetic images, but 50% in real images whereas, the combination of Shi's detection algorithm with GLC enhanced the detection rate by 25.9% in synthetic images and 123% in real images, showing a significant improvement. [ABSTRACT FROM AUTHOR]

In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, ). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the $$gPb-ucm$$ approach of Arbelaez et al. (TPAMI, ) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (Felzenszwalb et al., TPAMI, ). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. [ABSTRACT FROM AUTHOR]

Superpixel segmentation for PolSAR images can heavily decrease the number of primitives for subsequent interpretation while reducing the impact of speckle noise. However, traditional superpixel segmentation methods for PolSAR images only focus on the boundary adherence, the significance of superpixel segmentation will be lost when the accuracy is improved at the expense of computation efficiency. To solve this problem, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization and edge refinement. First, the PolSAR image is initialized as hexagonal distribution, where the complexity of searching pixels for relabelling in the local regions can be reduced by 30% theoretically. Second, all pixels in the PolSAR image are initialized as unstable pixels based on the hexagonal superpixels, which can boost the segmentation performance in the heterogeneous regions and effectively maintain all the potential edge pixels. Third, the revised Wishart distance and the spatial distance are integrated as a distance measure to relabel all unstable pixels. Finally, the postprocessing procedure based on a dissimilarity measure is applied to generate the final superpixels. Extensive experiments conducted on both the simulated and real-world PolSAR images demonstrate the superiority and effectiveness of our proposed algorithm in terms of computation efficiency and segmentation accuracy, compared to three other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction. [ABSTRACT FROM AUTHOR]

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation. [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]

We present a novel superpixel generation algorithm based on a new definition of geodesic distance, called bilateral geodesic distance. In contrast to the traditional geodesic distance, the new bilateral geodesic distance of two pixels considers the distance between their positions as well as their color difference. Superpixel generation is essentially a problem of clustering image pixels with respect to a set of properly selected seeds. We first use an adaptive hexagonal subdivision method to determine the initial seed-based image gradient. Then, we use the bilateral geodesic distance to measure the similarity between the pixels and the seeds. We apply an improved fast marching method to generate superpixels’ contour regions with the expansion velocities dependent on a new gradient formulation that depends on the seeds’ properties. The experimental results indicate that our algorithm is not only much faster than the structure-based method, which uses conventional geodesic distance, but also outperforms the existing methods in terms of region compactness and region boundary regularity. [ABSTRACT FROM AUTHOR]

• We present PSCT new data-driven sampling method based clustering algorithms. • PSCT output can be used for image segmentation and image reconstruction. • Image reconstructed with PSCT show similar performance than original images in our tests. In this paper, we describe Pixel Sampling Clustering Technique (PSCT), a data-driven sampling procedure used to reduce pixel sets. We view the pixels in an image as a high redundancy 3D space. We also refer to this space as our color model. Our method aims to retain a relevant sample of the data so it can act as a new smaller, hence more efficient, color model. PSCT uses a pair of fast density-based clustering algorithms in tandem. First, it applies Birch and then DBSCAN to keep the most densely represented colors. We cluster the resulting color model and use the labels to segment images. We also complement the sampling method with a refinement algorithm intended to improve color representation. In our paper, we show how to reconstruct images using our reduced color model. We also show that reconstructed images have enough information to perform image related learning tasks with almost the same accuracy than the original images but with only a small fraction of the data. We test our sampling method in three image related supervised and unsupervised tasks and compare them with state-of-the-art methods. For our experiments, we use two image datasets: MIT's Vision Texture Dataset and Berkeley's BSD500. [ABSTRACT FROM AUTHOR]

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects. [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]

Background: Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules. Method: Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences. Results: Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences. [ABSTRACT FROM AUTHOR]

An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods. [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]

Segmentation is an important research area in image processing, which has been used to extract objects in images. A variety of algorithms have been proposed in this area. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a general formal framework to study the neutralities’ origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing algorithms. This paper proposed a novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images. We utilize the neutrosophic set in image processing field and define a new similarity function for clustering. At first, an image is represented in the neutrosophic set domain via three membership sets: T , I and F . Then, a neutrosophic similarity function (NSF) is defined and employed in the objective function of the clustering analysis. Finally, the new defined clustering algorithm classifies the pixels on the image into different groups. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method's performance. The experimental results demonstrate that the NSC method segment the images effectively and accurately. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision. [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]

Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method iterative deep unsupervised segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) a deep network estimates class assignments; 2) low-level image features from the deep network are clustered into superpixels; 3) superpixels are clustered into class assignments (which we call pseudo-labels) using $k$ -means; and 4) resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Because our design combines merits of classical superpixel methods with deep learning, practically we demonstrate a very significant benefit in terms of reduced selection bias, i.e., IDUS shows markedly improved robustness against the choice of training images. Finally, we also develop a semi-supervised (SS) extension of IDUS called Iterative Deep SS Segmentation (IDSS) and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery. [ABSTRACT FROM AUTHOR]

One of the newest important medical imaging modalities is optical coherence tomography (OCT) providing the possibility of taking pictures from optical scattering tissues such as retina. With the help of accurate verification and analysis of OCT images, it is possible to identify and treat irreversible retinal diseases like glaucoma. Therefore, to suggest novel high-performance segmentation methods is of significant importance. In this article, a novel algorithm is proposed for the segmentation of OCT B-scans. The proposed method uses the intensity information of pixels to find a distinguishing feature for boundary pixels which are located on the retinal layer boundaries. Also, a nonparametric mathematical model is provided and analyzed for the segmentation algorithm. Such a novel model is also capable of determining boundary pixels. The performance of the proposed algorithm is evaluated for both normal and age-related macular degeneration (AMD) cases. The comparisons are performed from complexity and accuracy points of view. We show that the proposed algorithm has an outstanding performance in terms of values of mean signed and unsigned errors for both cases. Also, the results of mathematical analysis and execution of the algorithm match with good approximation. [ABSTRACT FROM AUTHOR]

For hyperspectral unmixing, a multi-scale spatial regularization method based on a modified image segmentation algorithm to generate super-pixels is proposed in which the super-pixels are used to extract contextual information from spatial correlations and spectral similarity in hyperspectral images (HSIs). The unmixing problem is decomposed into two simple unmixing subproblems regarding the approximate super-pixels and the original pixels. The unmixing results of these two subproblems have spatial-correlation constraints. Introducing a novel regularization term to constrain the abundance matrix to promote the homogeneous abundances helps in making effective use of the spatial correlations and spectral similarity of the abundances from HSIs. Experimental results obtained from synthetic data demonstrate that the proposed algorithm yields an accuracy greater than other conventional methods. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of objects similarity measurement of traditional cultural pattern segmentation and adaptively determine the number of segmentations, we propose an adaptive segmentation algorithm based on a new superpixel Log-Euclidean Gaussian metric (SLEGM) in this paper. We first propose to use the SLEGM to effectively characterize superpixels for more accurate measurement of their similarity. Because the space of Gaussians sample covariance matrix distribution is not a linear space but a Riemannian manifold, we map this manifold via matrix logarithm into a linear space, which enables us to handle Gaussians with Euclidean operations. Under the SLEGM framework, we develop an improved spectral clustering algorithm that can adaptively determine the number of clusters to achieve the adaptive segmentation for the traditional culture pattern. Extensive evaluations on the Berkeley Segmentation Data Set (BSDS500) benchmark verify that our algorithm outperforms the state-of-the-art techniques of the same category under four evaluation metrics, achieving 74.3% F-measure, 13.52% under segmentation error, 83.4% boundary recall and 97.79% achievable segmentation accuracy. Further experiments on our challenging Traditional Cultural Pattern Database (TCPD) indicate the effectiveness of our algorithm for segmenting the complex patterns. • A new SLEGM is proposed to effectively characterize the superpixels. • A new algorithm for adaptively determining the number of clusters is designed. • An adaptive image segmentation algorithm based on SLEGM is realized. • The proposed algorithm outperforms state-of-the-art algorithms of the same category. [ABSTRACT FROM AUTHOR]

In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback–Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm. [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]