Multiscale Sparse Research Articles

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Multi-scale Sparse Network with Cross-Attention Mechanism for image-based butterflies fine-grained classification

Butterfly protection is critical for environmental protection, and butterfly classification study is an essential tool for doing so. We proposed a new fine-grained butterfly classification architecture to address the issues of duplicate information in some butterfly images and trouble identifying them due to their tiny inter-class variance. To begin, a Non-Local Mean Filtering and Multi-Scale Retinex-based method (NL-MSR) is employed to enhance the butterfly images in order to efficiently retain more detail information. Then, to accomplish fine-grained categorization of butterfly images, a Multi-scale Sparse Network with Cross-Attention Mechanism (CA-MSNet) is designed. In CA-MSNet, a Cross-Attention Mechanism module (CAM) that offers distinct weights in the horizontal and vertical directions based on two strategies is devised to successfully identify the spatial distribution of butterfly stripes and spots and suppress incorrect information. Then, to overcome the recognition problem of butterfly spots with small inter-class variance, a Multi-scale sparse module (MSS) with multi-scale receptive fields is constructed. Finally, a Depthwise Separable Convolution module is employed to mitigate the parameter rise induced by the MSS network. In order to validate the model’s feasibility and effectiveness in a complex environment, we compared it to existing methods, and our proposed method achieved an average recognition accuracy of 91.88%, with an F1 value of 92.15%, indicating that it has a good effect on the fine-grained classification of butterflies and can be applied to their recognition to realize their protection.

Multi-Scale Sparse Graph Convolutional Network For the Assessment of Parkinsonian Gait

Automated assessment of patients with Parkinson's disease (PD) is urgently required in clinical practice to improve the diagnostic efficiency and objectivity and to remotely monitor the motor disorder symptoms and general health of these patients, especially in view of the travel restrictions due to the recent coronavirus epidemic. Gait motor disorder is one of the critical manifestations of PD, and automated assessment of gait is vital to realize automated assessment of PD patients. To this end, we propose a novel two-stream spatial-temporal attention graph convolutional network (2s-ST-AGCN) for video assessment of PD gait motor disorder. Specifically, the skeleton sequence of human body is extracted from videos to construct spatial-temporal graphs of joints and bones, and a two-stream spatial-temporal graph convolutional network is then built to simultaneously model the static spatial information and dynamic temporal variations. The multi-scale spatial-temporal attention-aware mechanism is also designed to effectively extract the discriminative spatial-temporal features. The deep supervision strategy is then embedded to minimize classification errors, thereby guiding the weight update process of the hidden layer to promote significant discriminative features. Besides, two model-driven terms are integrated into this deep learning framework to strengthen multi-scale similarity in the deep supervision and realize sparsification of discriminative features. Extensive experiments on the clinical video dataset show that the proposed model exhibits good performance with an accuracy of 65.66% and an acceptable accuracy of 98.90%, which is much better than that of the existing sensor- and vision-based methods for Parkinsonian gait assessment. Thus, the proposed method is potentially useful for assessing PD gait motor disorder in clinical practice.

A dynamical appearance model based on multiscale sparse representation: segmentation of the left ventricle from 4D echocardiography.

The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by individual inherent data coherence. It employs multi-scale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary multiscale appearance information including intensity, multiscale local appearance, and dynamical shape predictions. It overcomes the limitations of database-driven statistical models and applies to a broader range of subjects. Results on 26 4D canine echocardiographic images acquired from both healthy and post-infarct subjects show that our method significantly improves segmentation accuracy and robustness compared to a conventional intensity model and our previous single-scale sparse representation method.

Learning Multiscale Sparse Representations for Image and Video Restoration

This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311–4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.