Superpixel Segmentation Based on Square-Wise Asymmetric Partition and Structural Approximation

Cluster-based fine-to-coarse superpixel segmentation

We propose an improved superpixel segmentation algorithm based on visual saliency and color entropy for online color detection in printed products. This method addresses the issues of low accuracy and slow speed in detecting color deviations in print quality control. The improved superpixel segmentation algorithm consists of three main steps: Firstly, simulating human visual perception to obtain visually salient regions of the image, thereby achieving region-based superpixel segmentation. Secondly, adaptively determining the superpixel size within the salient regions using color information entropy. Finally, the superpixel segmentation method is optimized using hue angle distance based on chromaticity, ultimately achieving a region-based adaptive superpixel segmentation algorithm. Color detection of printed products compares the color mean values of post-printing images under the same superpixel labels, outputting labels with color deviations to identify areas of color differences. The experimental results show that the improved superpixel algorithm introduces color phase distance with better segmentation accuracy, and combines it with human visual perception to better reproduce the color information of printed materials. Using the method described in this article for printing color quality inspection can reduce data computation, quickly detect and mark color difference areas, and provide the degree of color deviation.

ABSTRACTHyperspectral imagery (HSI) classification is a rapidly growing and highly active research area in the field of hyperspectral community. The method that combines both spatial and spectral information for hyperspectral image classification has made a great advance. The focus of spectral-spatial classification is how to extract discriminating features and how to combine the spectral and spatial information effectively. In this paper, we propose a new spectral-spatial method to solve these two problems. The first part is a series of support vector machines (SVMs) that are cascaded to form the enhanced features, where the predicted information of the preceding layer can provide correction information for the subsequent layer, and then a new superpixel segmentation method is adopted to introduce multi-scale spatial information, which can generate multi-scale superpixels at one time and is as accurate as the state-of-the-art methods. The final label is determined by majority voting of different scales.

Effective shadow detection and shadow removal can improve the performance of fruit recognition in natural environments and provide technical support for agricultural intelligence. In this study, a superpixel segmentation method was used to divide an image into multiple small regions. Based on the superpixel segmentation results, the shadow regions and the shadowless regions of the orchard images under natural light were compared and studied. Seven shadow saliency features (SSF) were explored and analyzed for shadow detection. The SSF were used to enhance the shadow characteristics. Then, the genetic algorithm (GA) was used to optimize the parameters, and support vector machine recursive feature elimination (SVM-RFE) was used to determine the best feature combination for shadow detection. According to the best feature combination, the support vector machine (SVM) algorithm was used to determine whether each segment of the superpixel segmentation results belonged to the shadow region. Shadow removal was carried out on each detected shadow region, and a natural light image after shadow removal was obtained. Finally, the accuracy of shadow detection was tested. The experimental results showed that the average accuracy of the shadow detection algorithm in this study was 91.91%. As a result, the precision and recall for fruits recognition after shadow removal generally improved.

Non-overlapping classification of hyperspectral imagery with superpixel segmentation

Superpixel segmentation is essential to the rapid information extraction from synthetic aperture radar (SAR) imagery. In this letter, we propose a fast multiscale superpixel segmentation method based on the minimum spanning tree (MST), which can generate all scales of superpixels accurately in real time. Therefore, our method has the ability to segment SAR imagery with different scales efficiently and is meaningful for applications that require different levels of SAR image details. Experimental results on two real SAR images demonstrate that our proposed superpixel segmentation method can capture the image information of different levels, resulting in better hierarchical segmentation performance in comparison with other state-of-the-art methods.

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

Superpixel segmentation has been commonly used in various image processing tasks such as object detection and image classification. In this paper, we propose a novel superpixel segmentation method based on a new distance function and superpixel seed updating strategy for polarimetric synthetic aperture radar (PolSAR) images. We initialize superpixel seeds with an expected number of superpixels. Then, we iteratively cluster the pixels based on the distance function and update the superpixel seeds based on the updating strategy. When the termination condition is reaches, we stop the iteration and obtain the superpixels. The experimental results based on RADARSAT-2 data demonstrate that our method is effective and achieves a better tradeoff between boundary adherence and compactness.

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.

Sudden-onset natural disasters, such as destructive earthquakes, pose significant threats to human life and property. The use of high-resolution remote sensing (HRRS) images for automated assessment of building damage can rapidly and accurately provide spatial distribution information and statistical data on building damage, assisting in disaster response and relief efforts. However, the task is exceedingly challenging due to the diverse and intricate appearance of damaged buildings in HRRS images, coupled with interference from surrounding areas that exhibit certain damage characteristics as a result of the disaster. To overcome these issues, this article proposes a weakly supervised building damage assessment method based on scene change detection in pre- and post-disaster bitemporal images. This method fully leverages visual information of building boundaries and deeper semantic information of building scenes from pre-disaster images to guide the identification of building damage in post-disaster images. Specifically, the method first generates fine-grained subbuilding objects with detailed boundaries from pre-disaster images by combining semantic segmentation of buildings with superpixel segmentation. Then, bitemporal image scene blocks obtained using sub-building objects as clues are input into our proposed Siamese local-global visual transformer (SLgViT) network, enabling scene change detection guided by deep semantic information from pre-disaster images. Finally, the change detection results serve as the basis to depict pixel-level building damage in post-disaster images. The proposed SLgViT network is primarily composed of a specially designed local-global visual transformer (LgViT) module and a cross-Siamese interaction fusion (CSIF) module, both of which play a crucial role in the deep mining and integrated interaction of local and global semantic features from pre- and post-disaster images. It is noteworthy that our method operates in a weakly supervised manner. The training of the SLgViT network requires only scene patches centered around building objects from pre- and post-disaster bitemporal images, along with image-level annotations. Experiments conducted with satellite images from the 2010 Port-au-Prince, Haiti earthquake and unmanned aerial vehicle (UAV) images from the 2019 Changning, China earthquake have demonstrated the effectiveness and superior performance of the proposed method.

Solid mineral resources are the fundamental material basis for maintaining the sustainable development of human society. The international seabed area contains vast and potentially valuable mineral resources, and deep-sea polymetallic nodules are one of the important ocean mineral resources. Taking the Peru Basin in the eastern Pacific Ocean as an example, this study aims to identify and classify small-scale polymetallic nodules occurred in the deep sea. Improving the resolution of deep-sea hydroacoustic images by utilizing super-resolution reconstruction methods. On this basis, the superpixel segmentation method is applied to construct a deep-sea object sample enhancement model, and the multi-dimensional heterogeneous features of the seabed objects are deeply explored to achieve effective construction of training samples. Under the constraint of geological seabed samples, an accurate seabed polymetallic nodule recognition model was thus established to achieve intelligent classification of seabed minerals based on multi-source data (including bathymetric data, backscatter data, etc.). Ultimately, by utilizing the model's generalization ability, the recognition and classification of untrained samples can be achieved, thereby advancing the application of the proposed algorithm in large-scale deep-sea mineral resource exploration.

Road detection is a basic functionality for driver assistance system such as vehicle and pedestrian detection. The main challenge is to deal with the presence of different objects (pedestrians and vehicles), the continuously varying background, different imaging conditions (different viewpoints, varying weather conditions and changing illumination), different road types (color, shape) and different environments (off-road, urban, highways). Accordingly, we propose a novel road detection approach based on superpixels and anisotropic heat diffusion. The idea is to previously denoise and deblur road images using directional filters. And then to oversegment the enhanced image into small homogeneous regions which are called superpixels with the superpixel segmentation method. According to the thermodynamic energy diffusion theory, we cluster superpixels into several region blocks. Finally, the algorithm extracts the road region combining the prior knowledge with the largest and most coherent principle of cosegmentation. Quantitative and qualitative experiments show that the approach is robust to lighting variations, heavy traffic and shadows. Moreover, the proposed method provides the better performance when compared with several state-of-the-art methods.

Superpixel conditional generation adversarial network for CMR artifact correction

Saliency and co-saliency detection aim to distinguish conspicuous foreground objects from single and multiple images, thus are essential in many multimedia and vision applications. To achieve balanced efficiency and accuracy, most recent successful saliency detectors are based on superpixels. However, saliency detection with single-scale superpixel segmentation may fail in capturing intrinsic salient objects of complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank optimisation. Specifically, we first build a multiscale superpixel pyramid and derive the corresponding saliency map by multimodal saliency features and priors at each single scale. Then, we use joint low-rank analysis of multiscale saliency maps to obtain a more reliable and adaptively-fused saliency map, which properly takes all scales saliency into account. We further propose a GMM generative co-saliency prior to enable the above approach to detect co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed saliency and co-saliency detector over state-of-the-arts.

Superpixel segmentation, a preprocessing component, is widely used in computer vision tasks. Superpixel algorithm is supposed to generate superpixels with required boundary adherence, compactness as well as less computational complexity. However, most of the existing superpixel algorithms do not perform satisfactorily when it comes to compactness and boundary adherence, as they are a paradox in nature. In this paper, we propose a new superpixel segmentation method based on density peak (DP) clustering and modification of the original DP algorithm. The experimental results have been compared with state-of-the-art methods, both quantitatively and qualitatively, revealing the efficient outcomes that adhere to the object boundaries better than others.