Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition最新文献

{"title":"Complexity of Representations in Deep Learning","authors":"T. Ho","doi":"10.48550/arXiv.2209.00525","DOIUrl":"https://doi.org/10.48550/arXiv.2209.00525","url":null,"abstract":"—Deep neural networks use multiple layers of functions to map an object represented by an input vector progressively to different representations, and with sufficient training, eventually to a single score for each class that is the output of the final decision function. Ideally, in this output space, the objects of different classes achieve maximum separation. Motivated by the need to better understand the inner working of a deep neural network, we analyze the effectiveness of the learned representations in separating the classes from a data complexity perspective. Using a simple complexity measure, a popular benchmarking task, and a well-known architecture design, we show how the data complexity evolves through the network, how it changes during training, and how it is impacted by the network design and the availability of training samples. We discuss the implications of the observations and the potentials for further studies.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"57 1","pages":"2657-2663"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83566819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraction of Ruler Markings For Estimating Physical Size of Oral Lesions.","authors":"Zhiyun Xue,&nbsp;Kelly Yu,&nbsp;Paul Pearlman,&nbsp;Tseng-Cheng Chen,&nbsp;Chun-Hung Hua,&nbsp;Chung Jan Kang,&nbsp;Chih-Yen Chien,&nbsp;Ming-Hsui Tsai,&nbsp;Cheng-Ping Wang,&nbsp;Anil Chaturvedi,&nbsp;Sameer Antani","doi":"10.1109/icpr56361.2022.9956251","DOIUrl":"https://doi.org/10.1109/icpr56361.2022.9956251","url":null,"abstract":"<p><p>Small ruler tapes are commonly placed on the surface of the human body as a simple and efficient reference for capturing on images the physical size of a lesion. In this paper, we describe our proposed approach for automatically extracting the measurement information from a ruler in oral cavity images which are taken during oral cancer screening and follow up. The images were taken during a study that aims to investigate the natural history of histologically defined oral cancer precursor lesions and identify epidemiologic factors and molecular markers associated with disease progression. Compared to similar work in the literature proposed for other applications where images are captured with greater consistency and in more controlled situations, we address additional challenges that our application faces in real world use and with analysis of retrospectively collected data. Our approach considers several conditions with respect to ruler style, ruler visibility completeness, and image quality. Further, we provide multiple ways of extracting ruler markings and measurement calculation based on specific conditions. We evaluated the proposed method on two datasets obtained from different sources and examined cross-dataset performance.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"2022 ","pages":"4241-4247"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728633/pdf/nihms-1840606.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10509307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TensorMixup Data Augmentation Method for Fully Automatic Brain Tumor Segmentation","authors":"Yu Wang, Ya-Liang Ji","doi":"10.1109/ICPR56361.2022.9956689","DOIUrl":"https://doi.org/10.1109/ICPR56361.2022.9956689","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"20 1 1","pages":"4615-4622"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77443821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline.","authors":"Beibin Li,&nbsp;Ezgi Mercan,&nbsp;Sachin Mehta,&nbsp;Stevan Knezevich,&nbsp;Corey W Arnold,&nbsp;Donald L Weaver,&nbsp;Joann G Elmore,&nbsp;Linda G Shapiro","doi":"10.1109/icpr48806.2021.9412824","DOIUrl":"https://doi.org/10.1109/icpr48806.2021.9412824","url":null,"abstract":"<p><p>In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"2020 ","pages":"8727-8734"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icpr48806.2021.9412824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10718778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dependently Coupled Principal Component Analysis for Bivariate Inversion Problems.","authors":"Navdeep Dahiya, Yifei Fan, Samuel Bignardi, Romeil Sandhu, Anthony Yezzi","doi":"10.1109/icpr48806.2021.9413305","DOIUrl":"10.1109/icpr48806.2021.9413305","url":null,"abstract":"<p><p>Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction in various problem domains, including data compression, image processing, visualization, exploratory data analysis, pattern recognition, time-series prediction, and machine learning. Often, data is presented in a correlated paired manner such that there exist observable and correlated unobservable measurements. Unfortunately, traditional PCA techniques generally fail to optimally capture the leverageable correlations between such paired data as it does not yield a maximally correlated basis between the observable and unobservable counterparts. This instead is the objective of Canonical Correlation Analysis (and the more general Partial Least Squares methods); however, such techniques are still symmetric in maximizing correlation (covariance for PLSR) over all choices of the basis for both datasets without differentiating between observable and unobservable variables (except for the regression phase of PLSR). Further, these methods deviate from PCA's formulation objective to minimize approximation error, seeking instead to maximize correlation or covariance. While these are sensible optimization objectives, they are not equivalent to error minimization. We therefore introduce a new method of leveraging PCA between paired datasets in a dependently coupled manner, which is optimal with respect to approximation error during training. We generate a dependently coupled paired basis for which we relax orthogonality constraints in decomposing unreliable unobservable measurements. In doing so, this allows us to optimally capture the variations of the observable data while conditionally minimizing the expected prediction error for the unobservable component. We show preliminary results that demonstrate improved learning of our proposed method compared to that of traditional techniques.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330695/pdf/nihms-1676299.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39277423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directionally Paired Principal Component Analysis for Bivariate Estimation Problems.","authors":"Yifei Fan,&nbsp;Navdeep Dahiya,&nbsp;Samuel Bignardi,&nbsp;Romeil Sandhu,&nbsp;Anthony Yezzi","doi":"10.1109/icpr48806.2021.9412245","DOIUrl":"https://doi.org/10.1109/icpr48806.2021.9412245","url":null,"abstract":"<p><p>We propose Directionally Paired Principal Component Analysis (DP-PCA), a novel linear dimension-reduction model for estimating coupled yet partially observable variable sets. Unlike partial least squares methods (e.g., partial least squares regression and canonical correlation analysis) that maximize correlation/covariance between the two datasets, our DP-PCA directly minimizes, either conditionally or unconditionally, the reconstruction and prediction errors for the observable and unobservable part, respectively. We demonstrate the optimality of the proposed DP-PCA approach, we compare and evaluate relevant linear cross-decomposition methods with data reconstruction and prediction experiments on synthetic Gaussian data, multi-target regression datasets, and a single-channel image dataset. Results show that when only a single pair of bases is allowed, the conditional DP-PCA achieves the lowest reconstruction error on the observable part and the total variable sets as a whole; meanwhile, the unconditional DP-PCA reaches the lowest prediction errors on the unobservable part. When an extra budget is allowed for the observable part's PCA basis, one can reach an optimal solution using a combined method: standard PCA for the observable part and unconditional DP-PCA for the unobservable part.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icpr48806.2021.9412245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics.","authors":"Benjamin Szczapa,&nbsp;Mohamed Daoudi,&nbsp;Stefano Berretti,&nbsp;Pietro Pala,&nbsp;Alberto Del Bimbo,&nbsp;Zakia Hammal","doi":"10.1109/icpr48806.2021.9412292","DOIUrl":"https://doi.org/10.1109/icpr48806.2021.9412292","url":null,"abstract":"<p><p>We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Archive and was compared to the state-of-the-art on the same data. Using both 5-fold cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state-of-the-art methods.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icpr48806.2021.9412292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39520929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map.","authors":"Maruf Hossain Shuvo, Yasmin M Kassim, Filiz Bunyak, Olga V Glinskii, Leike Xie, Vladislav V Glinsky, Virginia H Huxley, Mahesh M Thakkar, Kannappan Palaniappan","doi":"10.1109/icpr48806.2021.9412122","DOIUrl":"10.1109/icpr48806.2021.9412122","url":null,"abstract":"<p><p>Characterizing the spatial relationship between blood vessel and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately captures as much of the vascular structures as possible. The red spectral channel captures small blood vessels and the green fluorescence channel images lymphatics structures in the intact dura mater attached to bone. The deep architecture Multi-Channel Fusion U-Net (MCFU-Net) combines multi-slice regression likelihood maps of thin linear structures using max pooling for each channel independently to estimate a slice-based focus selection map. We compare MCFU-Net with a widely used derivative-based multi-scale Hessian fusion method [8]. The multi-scale Hessian-based fusion produces dark-halos, non-homogeneous backgrounds and less detailed anatomical structures. Perception based no-reference image quality assessment metrics PIQUE, NIQE, and BRISQUE confirm the effectiveness of the proposed method.</p>","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":" ","pages":"4317-4323"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513773/pdf/nihms-1685783.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39519299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PA-FlowNet: Pose-Auxiliary Optical Flow Network for Spacecraft Relative Pose Estimation","authors":"Zhi-Yu Chen, Po-Heng Chen, Kuan-Wen Chen, Chen-Yu Chan","doi":"10.1109/ICPR48806.2021.9413201","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9413201","url":null,"abstract":"","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"37 1","pages":"9703-9710"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81380663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense Receptive Field for Object Detection","authors":"Yao Yongqiang, Dong Yuan, Huang Zesang, Bai Hongliang","doi":"10.1109/ICPR.2018.8546207","DOIUrl":"https://doi.org/10.1109/ICPR.2018.8546207","url":null,"abstract":"Current one-stage single-shot detectors such as DSSD and StairNet based on aggregating context information from multiple scales have shown promising accuracy. However, existing multi-scale context fusion techniques are insufficient for detecting objects of different scales. In this paper, we investigate how to detect different objects with different scales with respect to accuracy-vs-speed trade-off. We propose a novel single-shot based detector, called DRFNet which fuses feature maps with different sizes of the receptive field to boost the detection accuracy. Our final model DRFNet detector unifies comprehensive context information from various receptive fields effectively to enable it to detect objects in different sizes with higher accuracy. Experimental results on PASCAL VOC 2007 benchmark (79.6% mAP, 68 FPS) demonstrate that DRFNet is better than other state-of-the-art one-stage detectors similar to FPN. Code is released at https://github.com/yqyao/DRFNet.","PeriodicalId":74516,"journal":{"name":"Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition","volume":"15 1","pages":"1815-1820"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74603158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}