A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.

AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors

Brain cancer is the premier reason for cancer deaths all over the world.The diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is ineffectual.Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful.Therefore,the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules.The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.In the beginning stage,an anisotropic diffusion filters(ADF)method with un-sharp intensification masking is utilized to eliminate the noise discernment in images.In the following stage,within the improved nodule image sequence,a Superpixel Segmentation Based Iterative Clustering(SSBIC)algorithm is proposed for irregular brain tissue prediction.Subsequently,the brain nodule samples are captured using deep learning methods:Advanced Grey Wolf Optimization(AGWO)with ONN(AGWO-ONN)and Advanced GWO with CNN-based(AGWOCNN).The proposed technique indicates that the sensitivity is increased and the calculation time is decreased.Consequently,the proposed methodology manifests that the advanced Computer-Assisted Diagnosis(CAD)system has outstanding potential for automatic brain tumor diagnosis.The average segmentation time of the nodule slice order is 1.06s,and 97%of AGWO-ONN and 97.6%of AGWO-CNN achieve the best classification reliability.

Monocular depth ordering with occlusion edges extraction and local depth inference

In this paper, a method to infer global depth ordering for monocular images is presented. Firstly a distance metric is defined with color, compactness, entropy and edge features to estimate the difference between pixels and seeds, which can ensure the superpixels to obtain more accurate object contours. To correctly infer local depth relationship, a weighting descriptor is designed that combines edge, T-junction and saliency features to avoid wrong local inference caused by a single feature. Based on the weighting descriptor, a global inference strategy is presented,which not only can promote the performance of global depth ordering, but also can infer the depth relationships correctly between two non-adjacent regions. The simulation results on the BSDS500 dataset, Cornell dataset and NYU 2 dataset demonstrate the effectiveness of the approach.

A raster-based typification method for multiscale visualization of building features considering distribution patterns

In map multiscale visualization,typification is the process of replacing original objects,such as buildings,using a smaller number of objects while maintaining initial geometrical and distribution characteristics.During the past few decades,many vector-based methods for building typification have been developed,whereas raster-based methods have received less attention.In this paper,a new method for the typification of buildings with different distribution patterns called superpixel building typification(SUBT)is developed based on raster data.Using this method,buildings with different distribution patterns,such as linear,grid and irregular patterns,are first grouped by image connected component detection and superpixel analysis.Then,the new positions for building typification are determined by superpixel resegmentation.Finally,a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel.To test the proposed SUBT method,buildings from both cities and countrysides in China are applied to perform typification.The experimental results show that the proposed SUBT method can realize typification for buildings with linear,grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings.