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Anup Basu

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25 papers

2 author rows

AAAI Conference 2026 Conference Paper

StyleFM: Frequency Manipulation Empowered by Recursive Attention on Diffusion Models for Arbitrary Style Transfer

Yingnan Ma
Zhenye Liu
Siying Liu
Anup Basu

Given the remarkable performance of diffusion models in image generation, recent research has been exploring their adaptation to style transfer. However, current diffusion-based approaches encounter persistent challenges, such as style distortions and the reliance on textual prompts for content preservation. To address these limitations, we introduce StyleFM, a novel training-free diffusion-based style transfer approach that incorporates optimization strategies into both the frequency and temporal domains. The proposed method provides two core innovations: (1) Tripartite Frequency Manipulation: To more precisely tailor frequency manipulation, StyleFM introduces a tripartite frequency design with a buffer band accounting for the overlap of content and style representations. In addition, StyleFM designs a frequency superposition editing method to achieve frequency enhancement. (2) Recursive Attention: StyleFM proposes the recursive attention strategy within the diffusion process, which facilitates the progressive and consistent injection of style information throughout the temporal process without reliance on text guidance. Experiments demonstrate that StyleFM outperforms state-of-the-art methods. It effectively preserves content fidelity while achieving sufficient style embedding.

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EAAI Journal 2025 Journal Article

Block information strategy for multi-modal remote sensing image registration

Yameng Hong
Chengcai Leng
Beihua Liu
Jinye Peng
Irene Cheng
Anup Basu

Registration of multi-modal remote sensing image pairs (MRSI) is challenging given the distinct imaging mechanisms of multi-modal data sources, which lead to substantial geometric and radiometric distortions and inaccuracies in correspondences. To tackle this issue, we propose a novel approach that integrates local image information into feature representations through the design of local regions and the extraction of local information. The latter comprises of two key components: rank-based feature redistribution and residual information extraction utilizing a pyramid-like structure of local patches. This enhanced feature representation technique, termed Reinforced Local Information of LSS (RLILSS), embeds local information to improve the performance of the Local Self-Similarity (LSS)-based framework for MRSI registration. RLILSS strengthens feature characterization across various regions and addresses the limitations of supplementary information. This enables more reliable correspondences between images. Experimental results show that the proposed method achieves higher accuracy and better registration across diverse multi-modal datasets. Detailed analyses confirm its superiority over state-of-the-art methods in both accuracy and robustness. This approach holds significant potential for applications in automatic geographic registration and disaster area reconstruction.

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EAAI Journal 2025 Journal Article

Dual graph-regularized low-rank representation for hyperspectral image denoising

Chengcai Leng
Mingpei Tang
Zhao Pei
Jinye Peng
Anup Basu

Hyperspectral images have a wide range of applications in many fields. However, when hyperspectral images are captured by spectrometers, there is inevitably considerable noise, which affects subsequent research. In recent years, many hyperspectral image denoising methods based on low-rank representations have been proposed. Artificial intelligence denoising methods are also popular. However, the research on multi noise denoising is rarely mentioned, and most literatures only focus on one noise in hyperspectral images. Thus, we propose a denoising model for hyperspectral image based on dual graph-regularized low-rank representation, which can not only reduce multiple types of noise simultaneously, but also preserves details of the original image. In particular, this is the first time that the dual low-rank representation and dual graph regularizations are used on hyperspectral images. We solve this method using the linearized alternating direction method with adaptive penalty. Finally, we conduct experiments on simulated and real data sets to verify the effectiveness of our method. The experimental results show that our method can not only effectively remove a variety of mixed noises, but also well retain the details of the image.

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EAAI Journal 2025 Journal Article

Orthogonal Diversity Nonnegative Matrix Factorization for multi-view clustering

Xinling Zhang
Chengcai Leng
Jinye Peng
Irene Cheng
Anup Basu

In the context of rapid development of artificial intelligence, how to extract valuable information from complex multidimensional data has become a core research problem. Multi-view clustering methods based on non-negative matrix factorization (NMF) are widely used in multi-view data analysis, but still face many challenges in practical applications. Current multi-view clustering methods usually solve the problem of diversity among viewpoints by orthogonalization of view representations. However, they fail to fully utilize the rich features of each viewpoint because data from different viewpoints may be interrelated. In addition, existing methods fail to fully consider the orthogonality between base matrices while emphasizing the diversity of view representations. For this reason, this paper proposes a new orthogonal diversity non-negative matrix factorization method (ODNMF). First, ODNMF explores the orthogonality of the representations of sample pairs between different viewpoints. This approach preserves the characteristics of each perspective and enhances the diversity of data representations. Second, ODNMF orthogonalizes the basis matrix of each viewpoint to reduce redundant features and enhance data interpretability and representation. Finally, ODNMF introduces graph regularization for each view to reveal the intrinsic geometric and structural information of features. Experimental results show that ODNMF significantly outperforms existing state-of-the-art algorithms on seven datasets.

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EAAI Journal 2024 Journal Article

Bayesian non-negative matrix factorization with Student’s t-distribution for outlier removal and data clustering

Ruixue Yuan
Chengcai Leng
Shuang Zhang
Jinye Peng
Anup Basu

Non-negative Matrix Factorization (NMF) is an effective way to solve the redundancy of non-negative high-dimensional data. Most of the traditional probability-based NMF methods use Gaussian distribution to model the differences between the matrices before and after decomposition. However, the Gaussian distribution is strongly affected by outliers, and it may not fit all datasets accurately when there are no outliers in the data. In this article, we propose a novel Bayesian NMF with the Student’s t-distribution, i. e. , TNMF. specifically, in order to reduce the impact of outliers on the algorithm, we use the Student’s t-distribution to fit the data points instead of the Gaussian distribution. In addition, it is possible to adjust the Degree of Freedom (DF) to make the Student’s t-distribution more flexible than the Gaussian distribution to fit data points when there are no outliers. Next, we combine the Automatic Relevance Determination (ARD) prior in our algorithm to simplify the model and allow for better performance of the algorithm. Finally, the article used 10 datasets to design two kinds of experiments, outlier removal and data clustering. The outlier removal results of this proposed algorithm are significantly better than the other methods, and it performs better in clustering compared to the other methods in the majority of cases.

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EAAI Journal 2024 Journal Article

Feature matching based on Gaussian kernel convolution and minimum relative motion

Kun Wang
Chengcai Leng
Huaiping Yan
Jinye Peng
Zhao Pei
Anup Basu

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results.

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EAAI Journal 2024 Journal Article

Incremental semi-supervised graph learning NMF with block-diagonal

Xue Lv
Chengcai Leng
Jinye Peng
Zhao Pei
Irene Cheng
Anup Basu

Non-negative matrix factorization (NMF), as a good data dimensionality reduction method, is widely used in the field of image recognition. Incremental non-negative matrix factorization (INMF) as an improvement solves the problem of inefficiency caused by repeated running of data samples during online learning. However, in the traditional incremental non-negative matrix factorization algorithm, the newly added training samples do not contain label information. Some samples may be marked in both the initial sample and the new training sample in the real application scenario. In order to make full use of the label information carried by the dataset, in this paper, we propose a semi-supervised non-negative matrix factorization model for batch incremental data, incremental semi-supervised graph learning NMF with block diagonal (ISGDNMF). The model is divided into three cases according to the degree of label-carrying for the new batch data: all label-carrying, no label-carrying, and partial label-carrying. The label information is also used to add a diagonal structure to the coefficient matrix, which makes it possible to have stronger discriminatory ability and to distinguish different classes of images more easily. And graph regularization information is added in order to maintain the spatial-geometric structure of the data. Experiments on six image datasets show that this algorithm has superior performance relative to the other seven NMF-based algorithms.

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EAAI Journal 2023 Journal Article

β -divergence NMF with biorthogonal regularization for data representation

Ruixue Yuan
Chengcai Leng
Bing Li
Anup Basu

Non-Negative Matrix Factorization (NMF) has become a commonly used method for data representation. Orthogonal NMF improves the clustering performance by adding orthogonal constraints to the decomposed matrices. The existing orthogonal NMF methods typically use Euclidean distance to measure the difference between before and after factorization for convenience and simplicity. However, limitations of the Euclidean distance can lead to inflexibilities. In addition, failure to consider orthogonality of the decomposed features and sparsity of the data representation can also lead to degraded performance of the algorithm. In order to overcome the above shortcomings, we propose a novel β -divergence-based NMF with biorthogonal regularization (BO- β NMF). Our BO- β NMF method uses generalized β -divergence instead of Euclidean distance to measure the similarity between matrices, and selects an appropriate β for each type of data to obtain a more flexible way of measuring similarity. In addition, we also incorporate biorthogonal constraints into the minimized objective function, which ensures both orthogonality of the decomposed features and sparsity of the data representation. Furthermore, we use trace rather than Euclidean distance to measure the orthogonality of the decomposed matrices, which reduces execution time. Finally, clustering experiments on image datasets show that the overall clustering effect of BO- β NMF is better than state-of-the-art methods.

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YNICL Journal 2015 Journal Article

Stochastic process for white matter injury detection in preterm neonates

Irene Cheng
Steven P. Miller
Emma G. Duerden
Kaiyu Sun
Vann Chau
Elysia Adams
Kenneth J. Poskitt
Helen M. Branson

Preterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will also enable clinicians to implement and evaluate new methods to improve outcomes. While severe white matter injury (WMI) is associated with adverse developmental outcome, more subtle injuries are difficult to identify and the association with later impairments remains unknown. Thus, our goal was to develop an automated method for detection and visualization of brain abnormalities in MR images acquired in very preterm born neonates. We have developed a technique to detect WMI in T1-weighted images acquired in 177 very preterm born infants (24-32 weeks gestation). Our approach uses a stochastic process that estimates the likelihood of intensity variations in nearby pixels; with small variations being more likely than large variations. We first detect the boundaries between normal and injured regions of the white matter. Following this we use a measure of pixel similarity to identify WMI regions. Our algorithm is able to detect WMI in all of the images in the ground truth dataset with some false positives in situations where the white matter region is not segmented accurately.

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IROS Conference 2005 Conference Paper

Visual gesture recognition for ground air traffic control using the Radon transform

Meghna Singh
Mrinal Mandal 0001
Anup Basu

Human gesture recognition is an active topic of vision research which has applications in diverse fields such as collaborative virtual environments and robot teleoperation. We propose a novel method for the recognition of hand gestures, used by air marshals for steering aircraft on the runway, using the Radon transform. Various aspects of the algorithm, including acquisition, segmentation, labeling and recognition using the parametric Radon transform are addressed in this paper. A binary skeleton representation of the human subject is computed. The Radon transform is used to generate maxima corresponding to specific orientations of the skeletal representation. Feature vectors are extracted from the transform space by computing the normalized cumulative projections of the Radon transform on the angle axis. K-means clustering is then applied to recognize static gestures from the extracted features. This technique has the potential to provide information about the exact orientation of gesture segments and can find use in ground control of unmanned air vehicles. Experiments with image data corresponding to the various ground air traffic control gestures used in directing aircrafts, highlight the potential application of this approach.