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Evaluation of feature sensitivity to training data inaccuracy in detection of retinal lesions

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Evaluation of feature sensitivity to training data inaccurac...

Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Lauri Laaksonen Antti Hannuksela Ela Claridge Pauli Fält Markku Hauta-Kasari Hannu Uusitalo Lasse Lensu Machine Vision and Pattern Recognition Laboratory Lappeenranta university of Technology Lappeenranta Finland School of Computer Science The University of Birmingham United Kingdom School of Computing University of Eastern Finland Finland Department of Ophthalmology University of Tampere Finland TAUH Eye Center Tampere University Hospital Finland

computer aided diagnostic and segmentation tools have become increasingly important in reducing the workload of medical experts performing diagnosis, monitoring and documentation of various eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. Supervised methods have been developed for the segmentation and detection of lesions, and the reported performance has been good. The supervised methods, however, need representative data to properly train the classifier. Inaccuracies in the ground truth may have a significant impact on the performance of a supervised method as the training data are not representative. In this study, a quantitative evaluation of the sensitivity of different image features, including colour, texture, edge and higher-level features, to inaccuracy in the ground truth on exudates is presented. A mean decrease of approx. 20% in sensitivity and 13% in specificity was observed when using the most inaccurate training data.

关键词： Training data Sensitivity Retina Image color analysis Image segmentation Feature extraction Lesions

Pose focus transformer meet inter-part relation

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Expert Systems with Applications 2024年 240卷

作者： Luo, Yanmin Lin, Hongwei Huang, Wenlin Wang, Youjie Du, Jixiang Guo, Jing-Ming College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 China

Human pose estimation in crowded scenes is a challenging task. Due to overlap and occlusion, it is difficult to infer pose clues from individual keypoints. We proposed PFFormer, a new transformer-based approach that treats pose estimation as a hierarchical set prediction problem that first focuses on human windows and coarsely predicts whole-body poses globally within them. In PFFormer, we designed a Windows Clustering Transformer (WCT), which reorganizes the image windows by filtering the attentive windows and fusing the inattentive ones, allowing the transformer to focus on the important regions while reducing the interference from the complex background, followed by compensating for the loss of information with a global transformer. Then we partition the learned body pose into a set of structural parts and perform the Inter-Part Relation Module (IPRM) to capture the correlation between multiple parts. These full-body poses and component features are refined at a finer level through the Part-to-Joint Decoder (PJD). Extensive experiments show that PFFormer performs favorably against its counterpart on challenging datasets, including COCO2017, CrowdPose, and OChuman datasets. The performance of crowded scenes, in particular, demonstrates the robustness of the proposed methods to deal with occlusion. © 2023 Elsevier Ltd

关键词： Information filtering

Comparison of appearance-based and geometry-based bubble detectors

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8671卷 610-617页

作者： Strokina, Nataliya Juránek, Roman Eerola, Tuomas Lensu, Lasse Zemčik, Pavel Kälviäinen, Heikki Tampere University of Technology Department of Signal Processing P.O. Box 527 Tampere33101 Finland Brno University of Technology Department of Computer Graphics and Multimedia Brno Czech Republic Lappeenranta University of Technology Machine Vision and Pattern Recognition Laboratory P.O. Box 20 Lappeenranta53851 Finland

Bubble detection is a complicated tasks since varying lighting conditions changes considerably the appearance of bubbles in liquid. The two common techniques to detect circular objects such as bubbles, the geometry-based and appearance-based approaches, have their advantages and weaknesses. The geometry-based methods often fail to detect small blob-like bubbles that do not match the used geometrical model, and appearance-based approaches are vulnerable to appearance changes caused by, e.g., illumination. In this paper, we compare a geometry-based concentric circular arrangements (CCA) and appearance-based sliding window methods as well as their combinations in terms of bubble detection, gas volume computation, and size distribution estimation. The best bubble detection performance was achieved with the sliding window method whereas the most precise volume estimate was produced by the CCA method. The combination of the two approaches gave only a minor advantage compared to the base methods. © Springer International Publishing Switzerland 2014.

关键词： Geometry

HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images

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arXiv 2021年

作者： Yuan, Zhimin Ma, Xiaofen Yi, Jiajin Luo, Zhengrong Peng, Jialin College of Computer Science and Technology Huaqiao University Xiamen361021 China Department of Medical Imaging Guangdong Second Provincial General Hospital Guangzhou510317 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

Background and objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. Methods: In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. Results: Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules. Conclusions: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentati

关键词： Convolution

NORPPA: NOvel Ringed seal re-identification by Pelage pattern Aggregation

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arXiv 2022年

作者： Nepovinnykh, Ekaterina Chelak, Ilia Eerola, Tuomas Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering School of Engineering Science Lappeenranta-Lahti University of Technology Lut P.O.Box 20 Lappeenranta53851 Finland Department of Computer Science Faculty of Science University of Helsinki P.O. Box 4 Helsinki00100 Finland

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal monitoring and conservation and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on a new challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches. © 2022, CC BY-NC-ND.

关键词： Animals

FDDH: Fast discriminative discrete hashing for large-scale cross-modal retrieval

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arXiv 2021年

作者： Liu, Xin Wang, Xingzhi Cheung, Yiu-Ming Department of Computer Science Huaqiao University Xiamen Key Laboratory of Computer Vision and Pattern Recognition Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong

Cross-modal hashing, favored for its effectiveness and efficiency, has received wide attention to facilitating efficient retrieval across different modalities. Nevertheless, most existing methods do not sufficiently exploit the discriminative power of semantic information when learning the hash codes, while often involving time-consuming training procedure for handling the large-scale dataset. To tackle these issues, we formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data so as to minimize the quantization loss of mapping such data to hamming space, and propose an efficient Fast Discriminative Discrete Hashing (FDDH) approach for large-scale cross-modal retrieval. More specifically, FDDH introduces an orthogonal basis to regress the targeted hash codes of training examples to their corresponding semantic labels, and utilizes ϵ-dragging technique to provide provable large semantic margins. Accordingly, the discriminative power of semantic information can be explicitly captured and maximized. Moreover, an orthogonal transformation scheme is further proposed to map the nonlinear embedding data into the semantic subspace, which can well guarantee the semantic consistency between the data feature and its semantic representation. Consequently, an efficient closedform solution is derived for discriminative hash code learning, which is very computationally efficient. In addition, an effective and stable online learning strategy is presented for optimizing modality-specific projection functions, featuring adaptivity to different training sizes and streaming data. The proposed FDDH approach theoretically approximates the bi-Lipschitz continuity, runs sufficiently fast, and also significantly improves the retrieval performance over the state-of-the-art methods. The source code is released at: https://***/starxliu/FDDH. © 2021, CC0.

关键词： Hash functions

Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition

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arXiv 2021年

作者： Zhou, Ling Mao, Qirong Huang, Xiaohua Zhang, Feifei Zhang, Zhihong School of Computer Science and Communication Engineering Jiangsu University ZhenjiangJiangsu212013 China School of Computer Engineering Nanjing Institute of Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Xiamen University Xiamen China Center for Machine Vision and Signal Analysis University of Oulu Finland

Micro-Expression recognition has become challenging, as it is extremely difficult to extract the subtle facial changes of micro-expressions. Recently, several approaches proposed several expression-shared features algorithms for micro-expression recognition. However, they do not reveal the specific discriminative characteristics, which lead to sub-optimal performance. This paper proposes a novel Feature Refinement (FR) with expression-specific feature learning and fusion for micro-expression recognition. It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features. FR consists of an expression proposal module with attention mechanism and a classification branch. First, an inception module is designed based on optical flow to obtain expression-shared features. Second, in order to extract salient and discriminative features for specific expression, expression-shared features are fed into an expression proposal module with attention factors and proposal loss. Last, in the classification branch, labels of categories are predicted by a fusion of the expression-specific features. Experiments on three publicly available databases validate the effectiveness of FR under different protocol. Results on public benchmarks demonstrate that our FR provides salient and discriminative information for micro-expression recognition. The results also show our FR achieves better or competitive performance with the existing state-of-the-art methods on micro-expression recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

EDEN: Deep feature distribution pooling for Saimaa ringed seals pattern matching

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arXiv 2021年

作者： Chelak, Ilia Nepovinnykh, Ekaterina Eerola, Tuomas Kälviäinen, Heikki Belykh, Igor Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Lappeenranta-Lahti University of Technology LUT School of Engineering Science Department of Computational Engineering Computer Vision and Pattern Recognition Laboratory P.O.Box 20 Lappeenranta53850 Finland

In this paper, pelage pattern matching is considered to solve the individual re-identification of the Saimaa ringed seals. Animal re-identification together with the access to large amount of image material through camera traps and crowd-sourcing provide novel possibilities for animal monitoring and conservation. We propose a novel feature pooling approach that allow aggregating the local pattern features to get a fixed size embedding vector that incorporate global features by taking into account the spatial distribution of features. This is obtained by eigen decomposition of covariances computed for probability mass functions representing feature maps. Embedding vectors can then be used to find the best match in the database of known individuals allowing animal re-identification. The results show that the proposed pooling method outperforms the existing methods on the challenging Saimaa ringed seal image data. © 2021, CC BY-NC-ND.

关键词： pattern matching

Real-time 3D human pose estimation without skeletal a priori structures

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Image and vision Computing 2023年 132卷

作者： Bai, Guihu Luo, Yanmin Pan, Xueliang Wang, Jia Guo, Jing-Ming College of Computer Science and Technology Huaqiao University 668 Jimei Avenue Jimei District Fujian Xiamen361021 China Key Laboratory for Computer Vision and Pattern Recognition Huaqiao University 668 Jimei Avenue Jimei District Fujian Xiamen361021 China The Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

This study is about real-time 2D-3D human pose estimation without using the a priori structure of the skeleton and with a low number of parameters for regression tasks. Current graph convolution-based 3D human pose tasks require structural knowledge of the skeleton, which limits the exploration of pose estimation for unknown structures. Inspired by tyre rotation and circular convolution, weights rotation is used to fully learn the potential connections between the human joints. We refer to this process as the weight cyclic sharing mechanism, a novel method for updating features. It does not require knowledge of the structure of the human skeleton and learns different constraints between joints with a low number of parameters. We propose an end-to-end weight circular sharing network (WCirSNet) based on the weight circular sharing mechanism. We propose a simple and efficient weighted residual block in this WCirSNet. The superiorities of the weight circular sharing mechanism and weighted residual block were verified by abundant ablation studies. Extensive evaluations on two challenging benchmark datasets (Human 3.6 M, MPI-INF-3DHP) show that the performance and generalization capabilities of our framework are superior to the results of many previously advanced methods. © 2023 Elsevier B.V.

关键词： Benchmarking