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Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 206卷

作者： Li, Si Ling, Zhihao Zhu, Kunpeng Wuhan Univ Sci & Technol Precis Mfg Inst Wuhan 430081 Hubei Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Res Ctr Adv Mfg Technol Changzhou 213164 Jiangsu Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China

It is an effective means to improve the machining quality of product by monitoring the tool wear conditions in micro milling. However, due to the small size and high speed of the micro milling cutter, it is difficult to capture the clear image of tool wear by CCD camera. Therefore, a double dictionary sparse coding super-resolution (SR) method is proposed to improve the resolution of micro milling tool wear images in this work. Based on the traditional single dictionary, this work proposes the residual dictionary to reconstruct the residual image of the tool, achieving the double dictionary coefficient encoding reconstruction. In order to extract local features of low resolution (LR) tool wear images, the Gobor filter banks are designed, and then the PCA method is used to reduce dimension of extracted features. The dictionary learning based on a single high resolution (HR) image is realized, which reduces the dependence of super-resolution algorithm on large datasets. The effectiveness of Gabor filter and double dictionary for restoration tool wear image details is verified by experiments. Compared with proposed method, traditional interpolation method, sparse coding super resolution model [32] and super resolution convolutional neural network model [26], the PSNR mean value of those methods are 43.62, 42.19, 43.20, 43.21, respectively, which shows that the proposed method has excellent performance in superresolution reconstruction of micro milling tool images for tool wear monitoring.

关键词： Super resolution sparse coding Double dictionary Tool wear monitoring Micro milling

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Tensor sparse coding for Positive Definite Matrices

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2014年第3期36卷 592-605页

作者： Sivalingam, Ravishankar Boley, Daniel Morellas, Vassilios Papanikolopoulos, Nikolaos Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

In recent years, there has been extensive research on sparse representation of vector-valued signals. In the matrix case, the data points are merely vectorized and treated as vectors thereafter (for example, image patches). However, this approach cannot be used for all matrices, as it may destroy the inherent structure of the data. Symmetric positive definite (SPD) matrices constitute one such class of signals, where their implicit structure of positive eigenvalues is lost upon vectorization. This paper proposes a novel sparse coding technique for positive definite matrices, which respects the structure of the Riemannian manifold and preserves the positivity of their eigenvalues, without resorting to vectorization. Synthetic and real-world computer vision experiments with region covariance descriptors demonstrate the need for and the applicability of the new sparse coding model. This work serves to bridge the gap between the sparse modeling paradigm and the space of positive definite matrices.

关键词： sparse coding positive definite matrices region covariance descriptors computer vision optimization

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Meet JEANIE: A Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年第9期132卷 4091-4122页

作者： Wang, Lei Liu, Jun Zheng, Liang Gedeon, Tom Koniusz, Piotr CSIRO Data61 Canberra Australia Singapore Univ Technol & Design Singapore Singapore Curtin Univ Perth Australia Australian Natl Univ ANU Sch Comp Canberra Australia

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequences whose camera and subjects' poses can be easily manipulated in 3D. We evaluate JEANIE on skeletal Few-shot Action Recognition (FSAR), where matching well temporal blocks (temporal chunks that make up a sequence) of support-query sequence pairs (by factoring out nuisance variations) is essential due to limited samples of novel classes. Given a query sequence, we create its several views by simulating several camera locations. For a support sequence, we match it with view-simulated query sequences, as in the popular Dynamic Time Warping (DTW). Specifically, each support temporal block can be matched to the query temporal block with the same or adjacent (next) temporal index, and adjacent camera views to achieve joint local temporal-viewpoint warping. JEANIE selects the smallest distance among matching paths with different temporal-viewpoint warping patterns, an advantage over DTW which only performs temporal alignment. We also propose an unsupervised FSAR akin to clustering of sequences with JEANIE as a distance measure. JEANIE achieves state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II on supervised and unsupervised FSAR, and their meta-learning inspired fusion.

关键词： Dynamic time warping Few-shot action recognition Unsupervised Supervised Fusion MAML sparse coding Dictionary learning Soft assignment Skeletons

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Independent Subspace Analysis to Rotate Multidimensional Space for Visualization of Color Symbolism

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IEEE ACCESS 2024年 12卷 102670-102687页

作者： Hanada, Mitsuhiko Future Univ Hakodate Dept Complex & Intelligent Syst Hakodate Hokkaido 0418655 Japan

Ordinary independent component analysis cannot suitably handle the spherically symmetrical distributed subspaces that reflect the hue relations. Therefore, a method based on independent subspace analysis or multidimensional independent component analysis is presented for rotating a configuration in which color symbolism is visualized. Assuming two-dimensional spherically symmetric distributions for some pairs of dimensions, the proposed method aligns the axes into mutually independent spherically symmetrical spaces and sparse dimensions. The L2-norm of the data points in the spherically symmetric subspace is assumed to follow a Weibull distribution, and the distributions in the sparse dimensions are assumed to follow a generalized normal distribution. To process the heterogeneous dimensions/dimension pairs and to rotate an unwhitened configuration, the scaling parameters of dimensions/dimension pairs are estimated in addition to the orthonormal matrix by fully maximizing the likelihood function to rotate the space representing color symbolism. Two applications for this method are presented, whereby the configurations of color-emotion associations and color-shape associations obtained by correspondence analysis are rotated by the proposed method. The characteristics of these associations are clearly shown in the obtained configurations. The proposed method provides potentially advantageous options for rotating a configuration for the visualization of color symbolism.

关键词： Image color analysis Color Kurtosis Gaussian distribution Encoding Shape Principal component analysis Independent subspace analysis independent component analysis correspondence analysis sparse coding color symbolism color-emotion association color-shape association hue

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Minimax Concave Penalty Regression for Superresolution Image Reconstruction

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 2999-3007页

作者： Liao, Xingran Wei, Xuekai Zhou, Mingliang City Univ Hong Kong Comp Sci Dept Hong Kong Peoples R China Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China

Fast and robust superresolution image reconstruction techniques can be beneficial in improving the safety and reliability of various consumer electronics applications. The least absolute shrinkage and selection operator (LASSO) penalty is widely used in sparse coding-based superresolution image reconstruction (SCSR) tasks. However, the performance of the previously developed models is constrained by bias generated by the LASSO penalty. Meanwhile, no efficient and fast computing algorithms are available for unbiased l(0) regression, and this situation restricts the practical application of l(0) -based SCSR methods. To address bias and efficiency problems, we propose a model called minimax concave penalty-based superresolution (MCPSR). First, we introduce a minimax concave penalty (MCP) into the SCSR task to eliminate bias. Second, we design a convergent, efficient algorithm for solving the MCPSR model and present a strict convergence analysis. Numerical experiments show that this model and the designed supporting algorithm can produce reconstructed images with richer textures at a fast computing speed. Moreover, MCPSR even shows robustness in the superresolution reconstruction of noisy images compared with other SCSR methods and has two flexible parameters to control the smoothness of the final reconstruction results.

关键词： Image reconstruction Superresolution Computational modeling Task analysis Dictionaries Numerical models Feature extraction Image superresolution sparse coding minimax concave penalty regression fast computing method

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Sparsity-guided Discriminative Feature Encoding for Robust Keypoint Detection

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第3期20卷 1-22页

作者： Xie, Yurui Guan, Ling Ryerson Multimedia Res Lab 350 Victoria St Toronto ON M5B 2K3 Canada Chengdu Univ Informat Technol Chengdu 610225 Peoples R China

Existing handcrafted keypoint detectors typically focus on designing specific local structures manually while ignoring whether they have enough flexibility to explore diverse visual patterns in an image. Despite the advancement of learning-based approaches in the past few years, most of them still rely on the availability of the outputs of handcrafted detectors as a part of training. In fact, such dependence limits their ability to discover various visual information. Recently, semi-handcrafted methods based on sparse coding have emerged as a promising paradigm to alleviate the above issue. However, the visual relationships between feature points have not been considered in the encoding stage, which may weaken the discriminative capability of feature representations for keypoint recognition. To tackle this problem, we propose a novel sparsity-guided discriminative feature representation (SDFR) method that attempts to explore the intrinsic correlations of keypoint candidates, thus ensuring the validity of characterizing distinctive and diverse structural information. Specifically, we first incorporate an affinity constraint into the feature representation objective, which jointly encodes all the patches in an image while highlighting the similarities and differences between them. Meanwhile, a smoother sparsity regularization with the Frobenius norm is leveraged to further preserve the similarity relationships of patch representations. Due to the differentiable property of this sparsity, SDFR is computationally feasible and effective for representing dense patches. Finally, we treat the SDFR model as multiple optimization sub-problems and introduce an iterative solver. During comprehensive evaluations on five challenging benchmarks, the proposed method achieves favorable performances compared with the state of the art in the literature.

关键词： Keypoint feature representation visual dictionary sparse coding

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Cross-Scatter sparse Dictionary Pair Learning for Cross-Domain Classification

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 371-384页

作者： Jiang, Lin Wu, Jigang Zhao, Shuping Li, Jiaxing Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Dongguan South China Design Innovat Inst Dongguan 523781 Peoples R China Guangzhou Univ Sch Artificial Intelligence Guangzhou 510006 Peoples R China

In cross-domain recognition tasks, the divergent distributions of data acquired from various domains degrade the effectiveness of knowledge transfer. Additionally, in practice, cross-domain data also contain a massive amount of redundant information, usually disturbing the training processes of cross-domain classifiers. Seeking to address these issues and obtain efficient domain-invariant knowledge, this paper proposes a novel cross-domain classification method, named cross-scatter sparse dictionary pair learning (CSSDL). Firstly, a pair of dictionaries is learned in a common subspace, in which the marginal distribution divergence between the cross-domain data is mitigated, and domain-invariant information can be efficiently extracted. Then, a cross-scatter discriminant term is proposed to decrease the distance between cross-domain data belonging to the same class. As such, this term guarantees that the data derived from same class can be aligned and that the conditional distribution divergence is mitigated. In addition, a flexible label regression method is introduced to match the feature representation and label information in the label space. Thereafter, a discriminative and transferable feature representation can be obtained. Moreover, two sparse constraints are introduced to maintain the sparse characteristics of the feature representation. Extensive experimental results obtained on public datasets demonstrate the effectiveness of the proposed CSSDL approach.

关键词： Dictionary learning scatter learning sparse coding cross-domain classification Dictionary learning scatter learning sparse coding cross-domain classification

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Airplane detection based on rotation invariant and sparse coding in remote sensing images

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OPTIK 2014年第18期125卷 5327-5333页

作者： Liu, Liu Shi, Zhenwei Beihang Univ Sch Astronaut Image Proc Ctr Beijing 100191 Peoples R China

Airplane detection has been taking a great interest to researchers in the remote sensing filed. In this paper, we propose a new approach on feature extraction for airplane detection based on sparse coding in high resolution optical remote sensing images. However, direction of airplane in images brings difficulty on feature extraction. We focus on the airplane feature possessing rotation invariant that combined with sparse coding and radial gradient transform (RGT). sparse coding has achieved excellent performance on classification problem through a linear combination of bases. Unlike the traditional bases learning that uses patch descriptor, this paper develops the idea by using RGT descriptors that compute the gradient histogram on annulus round the center of sample after radial gradient transform. This set of RGT descriptors on annuli is invariant to rotation. Thus the learned bases lead to the obtained sparse representation invariant to rotation. We also analyze the pooling problem within three different methods and normalization. The proposed pooling with constraint condition generates the final sparse representation which is robust to rotation and detection. The experimental results show that the proposed method has the better performance over other methods and provides a promising way to airplane detection. (c) 2014 Elsevier GmbH. All rights reserved.

关键词： Airplane detection sparse coding Rotation invariant Radial gradient transform Constraint pooling

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Multiview Hessian discriminative sparse coding for image annotation

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COMPUTER VISION AND IMAGE UNDERSTANDING 2014年 118卷 50-60页

作者： Liu, Weifeng Tao, Dacheng Cheng, Jun Tang, Yuanyan China Univ Petr East China Coll Informat & Control Engn Qingdao Shandong Peoples R China Univ Technol Ctr Quantum Computat & Intelligent Syst Ultimo NSW 2007 Australia Univ Technol Fac Engn & Informat Technol Ultimo NSW 2007 Australia Chinese Acad Sci Shenzhen Key Lab CVPR Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China Univ Macau Fac Sci & Technol Taipa Macau Peoples R China

sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Image annotation Hessian Multiview sparse coding

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Deep Learing for sparse Domain Kalman Filtering With Applications on ECG Denoising and Motility Estimation

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第8期71卷 2321-2329页

作者： de Vries, I. R. de Jong, A. M. van der van der Jagt, M. B. van Laar, J. O. E. H. VullingsMember, R. Maxima Med Ctr Dept Obstet & Gynecol NL-5504 DB Eindhoven Noord Brabant Netherlands Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Noord Brabant Netherlands Eindhoven Univ Technol Dept Appl Phys & Sci Educ Eindhoven Netherlands Nemo Healthcare BV Veldhoven Netherlands

Objective: The reconstruction of an input based on a sparse combination of signals, known as sparse coding, has found widespread use in signal processing. In this work, the combination of sparse coding with Kalman filtering is explored and its potential is shown on two use-cases. Methods: This work extends the Iterative Shrinkage and Thresholding Algorithm with a Kalman filter in the sparse domain. The resulting method may be implemented as a deep unfolded neural network and may be applied to any signal which has a sparse representation and a known or assumed relation between consecutive measurements. This method is evaluated on the use cases of noise reduction in the electrocardiogram (ECG) and the estimation of object motility. Results: For ECG denoising, the proposed method achieved an improvement in Signal-to-Noise ratio of 18.6 dB, which is comparable to state-of-the-art. In motility estimation, a correlation of 0.84 with ground truth simulations was found. Conclusion: The proposed method was shown to have advantages over sparse coding and Kalman filtering alone. Due to the low complexity and high generalizability of the proposed method, the implementation of context-specific knowledge or an extension to other applications can be readily made. Significance: The presented Kalman-ISTA algorithm is a resource-efficient method combining the promise of both sparse coding and Kalman filtering, making it well-suited for various applications.

关键词： Kalman filters Encoding Filtering Electrocardiography Biomedical measurement sparse approximation Estimation Electrocardiogram ISTA Kalman filter motility estimation sparse coding

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