Article
Engineering, Biomedical
Meghana Karri, Chandra Sekhara Rao Annavarapu, Saurav Mallik, Zhongming Zhao, U. Rajendra Acharya
Summary: This paper presents a highly efficient cervical screening method based on cell image analysis and proposes a new framework for accurate classification of cervical cells. The proposed method consists of three phases: segmentation, nucleus localization, and classification. Experimental results show that the proposed approach is more effective than existing techniques.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2022)
Article
Chemistry, Analytical
Wysterlanya K. P. Barros, Leonardo A. Dias, Marcelo A. C. Fernandes
Summary: This study implements the Otsu automatic image thresholding algorithm on FPGA for real-time processing of high-resolution images. By optimizing processing time through parallelization, the proposed hardware achieved a high speedup compared to similar works.
Article
Engineering, Biomedical
Marco J. Del Moral-Argumedo, Carlos A. Ochoa-Zezzati, Ruben Posada-Gomez, Alberto A. Aguilar-Lasserre
Summary: This paper presents a method for multi-class cell segmentation, achieving good performance by utilizing state-of-the-art classification architectures. The approach accurately assesses cervical cell lesions and improves healthcare for patients.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Ecology
Jaya G. Brindha, E. S. Gopi
Summary: An hierarchical approach based on Kernel Linear Discriminant Analysis (KLDA) and Gaussian process regression is proposed in this paper for automating the segmentation process of leaf images. Experimental results demonstrate the potential of the proposed method in automating the leaf segmentation process.
ECOLOGICAL INFORMATICS
(2021)
Article
Engineering, Biomedical
Santhiya Thanaraj, Arun Balodi, R. S. Anand, Anurag Rawat
Summary: Cardiovascular disease (CVD) is a prevalent disease with high mortality rate. Early diagnosis is crucial for the prognosis of CVD. This research proposes an automatic delineation technique to quantify the regurgitant jet area and evaluate the severity of mitral regurgitation (MR) quickly and accurately.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Environmental Sciences
Shaomei Chen, Zhaofu Li, Tingli Ji, Haiyan Zhao, Xiaosan Jiang, Xiang Gao, Jianjun Pan, Wenmin Zhang
Summary: This study developed a new index (NDRI) and an adaptive thresholding method (THAT) to identify and extract rapeseed, which performed well in the experiment in Jiangsu province.
Article
Oncology
G. Jignesh Chowdary, G. Suganya, M. Premalatha, Pratheepan Yogarajah
Summary: Pap smear is a primary examination for diagnosis of cervical cancer, but manual analysis of slides is time-consuming and tedious. Automated segmentation and classification of cervical nuclei improves diagnostic efficiency. The proposed methodology includes three models and achieved promising performance in segmenting and classifying cervical cytopathology cell images.
TECHNOLOGY IN CANCER RESEARCH & TREATMENT
(2023)
Article
Health Care Sciences & Services
Chaoyue Chen, Yisong Cheng, Jianfeng Xu, Ting Zhang, Xin Shu, Wei Huang, Yu Hua, Yang Zhang, Yuen Teng, Lei Zhang, Jianguo Xu
Summary: This study developed a deep-learning-based system for preoperative grading of meningioma, which showed good performance in tumor detection, segmentation, reconstruction, and grading prediction. The system exhibited robustness in both internal and external validation datasets.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Engineering, Electrical & Electronic
Pallavi Kulkarni, Deepa Madathil
Summary: The proposed method introduces an optimization algorithm based on wavelet decomposition for segmentation, aiming to find the optimal threshold value through evaluating contrast properties and using a nonlinear derivative-free optimizing algorithm. By formulating the optimization task as a maximization problem, the method achieves precise segmentation of the left ventricle and extraction of its contour through iterative processes and user input, demonstrating a higher accuracy of 94% compared to ground truth labels.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2021)
Article
Chemistry, Analytical
Xia Huang, Shunyi Zheng, Li Gui
Summary: This study presents a novel approach for automatic plant measurement using a handheld 3D laser scanner, which can automatically estimate plant morphological traits through 3D model reconstruction and leaf sample extraction. Experimental results demonstrate the method's effectiveness in different canopy-occluded plants, showing high measurement efficiency and low time cost.
Article
Engineering, Electrical & Electronic
Qifan Tu, Dawei Li, Qian Xie, Li Dai, Jun Wang
Summary: In this paper, a novel framework including a carriage-attention module, a scrap detection module, and a scrap grading module is proposed to address the challenges in scrap steel grading. Experimental results demonstrate that the method achieves high accuracy and fast inference speed in scrap steel grading.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Forestry
Peng Wu, Maodong Cai, Xiaomei Yi, Guoying Wang, Lufeng Mo, Musenge Chola, Chilekwa Kapapa
Summary: This paper proposes a deep learning approach based on an enhanced version of DeepLabV3+ to accurately and efficiently detect common diseases in maple leaves. By utilizing image annotation and data enhancement techniques, the maple leaf spot dataset is constructed. The experimental results show that our improved algorithm achieves high accuracy in mean intersection over union (MIoU) and mean pixel accuracy (MPA) for disease detection.
Article
Agriculture, Multidisciplinary
Teng Miao, Chao Zhu, Tongyu Xu, Tao Yang, Na Li, Yuncheng Zhou, Hanbing Deng
Summary: The study proposes an automatic segmentation method for maize plants from 3D point clouds, achieving high precision and recall rates. It enables accurate measurement of phenotypic traits and skeleton extraction. This approach is crucial for further maize research and applications, offering benefits like genotype-to-phenotype studies and geometric reconstruction.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Chemistry, Analytical
Haitao Li, Gengchen Wu, Shutian Tao, Hao Yin, Kaijie Qi, Shaoling Zhang, Wei Guo, Seishi Ninomiya, Yue Mu
Summary: This paper proposes an automatic branch-leaf segmentation pipeline based on lidar point cloud for nondestructive and accurate measurements of leaf phenotypic parameters. The proposed method establishes a 3D model and uses the PointNet++ model for segmentation, achieving efficient and convenient measurements compared to manual methods.
Article
Chemistry, Multidisciplinary
Debora N. Diniz, Mariana T. Rezende, Andrea G. C. Bianchi, Claudia M. Carneiro, Daniela M. Ushizima, Fatima N. S. de Medeiros, Marcone J. F. Souza
Summary: This study proposed a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. By investigating eight traditional machine learning methods, a hierarchical classification methodology was developed for computer-aided screening of cell lesions, showing Random Forest as the best classifier. The results suggest that hierarchical classification outperforms other methods such as decision trees, k-NN, and the Ridge methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Murat Genctav, Asli Genctav, Sibel Tari
JOURNAL OF MATHEMATICAL IMAGING AND VISION
(2016)
Article
Geochemistry & Geophysics
H. Gokhan Akcay, Selim Aksoy
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2016)
Article
Geochemistry & Geophysics
Gencer Sumbul, Ramazan Gokberk Cinbis, Selim Aksoy
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2018)
Article
Computer Science, Interdisciplinary Applications
Caner Mercan, Selim Aksoy, Ezgi Mercan, Linda G. Shapiro, Donald L. Weaver, Joann G. Elmore
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2018)
Article
Computer Science, Artificial Intelligence
Bads Gecer, Selim Aksoy, Ezgi Mercan, Linda G. Shapiro, Donald L. Weaver, Joann G. Elmore
PATTERN RECOGNITION
(2018)
Article
Computer Science, Software Engineering
Ahmet Oguz Akyuz, Asli Genctav
COMPUTERS & GRAPHICS-UK
(2013)
Editorial Material
Engineering, Electrical & Electronic
Nicolas H. Younan, Selim Aksoy, Roger L. King
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2012)
Article
Geochemistry & Geophysics
Caglar Ari, Selim Aksoy
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2014)
Article
Computer Science, Artificial Intelligence
Asli Genctav, Sibel Tari
JOURNAL OF MATHEMATICAL IMAGING AND VISION
(2019)
Article
Geochemistry & Geophysics
Gencer Sumbul, Ramazan Gokberk Cinbis, Selim Aksoy
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2019)
Article
Imaging Science & Photographic Technology
Asli Genctav, Sibel Tari
JOURNAL OF IMAGING
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Huseyin Gokhan Akcay, Selim Aksoy
2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Ezgi Mercan, Selim Aksoy, Linda G. Shapiro, Donald L. Weaver, Tad Brunye, Joann G. Elmore
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2014)
Proceedings Paper
Engineering, Electrical & Electronic
H. Gokhan Akcay, Selim Aksoy
2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
(2013)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)