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Open AccessArticle

Target Tracking with Adaptive Morphological Correlation and Neural Predictive Modeling

by Victor H. Diaz-Ramirez and Leopoldo N. Gaxiola-Sanchez

Appl. Sci. 2025, 15(21), 11406; https://doi.org/10.3390/app152111406 - 24 Oct 2025

Cited by 1 | Viewed by 746

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Image Processing)

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18 pages, 6310 KB

Open AccessArticle

Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT

by Qing Chen, Sheng Zheng, Xing Wu and Tao Liu

Appl. Sci. 2022, 12(21), 10807; https://doi.org/10.3390/app122110807 - 25 Oct 2022

Cited by 5 | Viewed by 2362

Abstract

In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDFSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram). Full article

(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)

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21 pages, 33351 KB

Open AccessArticle

Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration

by Yidan Teng, Ye Zhang, Chunli Ti and Junping Zhang

Remote Sens. 2018, 10(4), 592; https://doi.org/10.3390/rs10040592 - 11 Apr 2018

Cited by 3 | Viewed by 4925

Abstract

Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new fusion method is proposed to improve the fusion performance by taking further advantage of the distribution characteristics of ground objects. First, we put forward a local adaptive sparse unmixing based fusion (LASUF) algorithm, in which the sparsity of the abundance matrices is appended as the constraint to the optimization fusion, considering the limited categories of ground objects in a specific range and the local correlation of their distribution. Then, to correct the possible original subpixel misregistrations or those introduced by the fusion procedures, a subpixel calibration method based on optimal matching adaptive morphology filtering (OM-AMF) is designed. Experiments on various datasets captured by different sensors demonstrate that the proposed fusion algorithm surpasses other typical fusion techniques in both spatial and spectral domains. The proposed method effectively preserves the spectral composition features of the isolated ground objects within a small area. In addition, the OM-AMF postprocessing is able to spatially correct the fusion results at a subpixel level and preserve the spectral features simultaneously. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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