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2016 SAI Computing Conference, SAI 2016

作者： Lantsova, Ekaterina Voitiuk, Tatiana Zudilova, Tatiana Victorovna Kaarna, Arto Saint Petersburg State University of Information Technologies Mechanics and Optics ITMO Department of Infocommunication Technologies St.-Petersburg Russia Lappeenranta University of Technology School of Computational Engineering Machine Vision and Pattern Recognition Lab Lappeenranta Finland

ISBN: (纸本)9781467384605

This study demonstrates computational methods for the automatic detection and tracking of fish from video sequences. The research in this subject is very important especially in fish farming companies and for nature protection around the world. The process of automated control and counting of individual species of fish has a supportive contribution both in the nature conservation and in the food industry. The difficulties and the specific features of the problem have been studied in order to find a solution for the components of the automated fish tracking system. Typical methods in computer vision applications, such as background subtraction, Kalman filtering and Viola-Jones method were implemented for the motion detection, tracking, and estimation of fish parameters. Both the choice of the appropriate methods and results of the experiments strongly depend on the quality and the type of video sequences. The results we received from the practical experiments demonstrated that not all methods can produce good results for a real data set, whereas on a set of synthetic data they operate satisfactorily. These results suggest that the proposed approaches are adequate in estimating the annual amount of fish migrating through the fish pass. © 2016 IEEE.

关键词： Fish

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Image-based characterization of the pulp flows

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pattern recognition and Image Analysis 2016年第3期26卷 630-637页

作者： Sorokin, M. Strokina, N. Eerola, T. Lensu, L. Karttunen, K. Kalviainen, H. Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Computer Vision Group Department of Signal Processing Tampere University of Technology PO Box 527 TampereFI-33101 Finland Cemis-Oulu Unit of Measurement Technology Kajaani University Consortium University of Oulu PO Box 127 KajaaniFI-87400 Finland School of Information Technology Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor Darul Ehsan46150 Malaysia

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions. © 2016, Pleiades Publishing, Ltd.

关键词： pattern matching

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Reconstruction of retinal spectra from RGB data using a RBF network

Reconstruction of retinal spectra from RGB data using a RBF ...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Uyen Nguyen Lauri Laaksonen Hannu Uusitalo Lasse Lensu Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Lappeenranta Finland Department of Ophthalmology University of Tampere Finland Tauh Eye Center Tampere University Hospital Finland

ISBN: (纸本)9781467389112

In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. The dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. The experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy.

关键词： Retina Image reconstruction Image color analysis Quantization (signal) Imaging Image segmentation Radial basis function networks

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Deep learning for facial keypoints detection 10

Deep learning for facial keypoints detection

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10th International Conference on Computer vision Theory and Applications, VISAPP 2015

作者： Haavisto, Mikko Kaarna, Arto Lensu, Lasse Machine Vision and Pattern Recognition Laboratory Department of Mathematics and Physics Lappeenranta University of Technology Lappeenranta Finland

ISBN: (纸本)9789897580901

A new area of machine learning research called deep learning has moved machine learning closer to one of its original goals: artificial intelligence and feature learning. Originally the key idea of training deep networks was to pretrain models in completely unsupervised way and then fine-tune the parameters for the task at hand using supervised learning. In this study, deep learning is applied to a facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by a realvalued pair in the space of pixel coordinates. In the experiments, we pretrained a Deep Belief Network (DBN) and finally performed discriminative fine-tuning. We varied the depth and size of the network. We tested both deterministic and sampled hidden activations, and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than the publicly available benchmarks for the dataset.

关键词： Deep learning

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

Evaluation of feature sensitivity to training data inaccurac...

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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

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Amplifier Errors in Dry Bacteriorhodopsin Sensor Measurements

Amplifier Errors in Dry Bacteriorhodopsin Sensor Measurement...

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IEEE International Instrumentation and Measurement Technology Conference

作者： Joonas P. Talvitie Teemu Tukiainen Lasse Lensu Tommi J. Karkkainen Pertti Silventoinen Mikko Kuisma Department of Electrical Engineering Lappeenranta University of Technology Lappeenranta Finland Machine Vision and Pattern Recognition Laboratory Department of Mathematics and Physics Lappeenranta University of Technology Lappeenranta Finland

ISBN: (纸本)9781479961153

Bacteriorhodopsin is considered an important biomolecule for biochemical and technology-oriented studies. The modeling and functionality of bacteriorhodopsin sensors have been extensively studied, but the studies lack the analysis of the errors present in the measurements. Further, the impacts of these errors on photoelectric measurements are mostly unknown. In this paper, we discuss the errors caused by a transimpedance amplifier in the photocurrent measurements on a dry bacteriorhodopsin sensor. This information is especially useful when evaluating the reliability of photocurrent measurements.

关键词： Bacteriorhodopsins transimpedance amplifier Sensors Amplifiers Photocurrent

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Learning Generative Models of Object Parts from a Few Positive Examples

Learning Generative Models of Object Parts from a Few Positi...

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International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Department of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

A number of computer vision problems such as object detection, pose estimation, and face recognition utilise local parts to represent objects, which include the distinguished information of objects. In this work, we introduce a novel probabilistic framework which automatically learns class-specific object parts (landmarks) in generative-learning manner. Encouraged by the success in learning and detecting facial landmarks, we employ bio-inspired multi-resolution Gabor features in the proposed framework. Specifically, complex-valued Gabor filter responses are first transformed to landmark specific likelihoods using Gaussian Mixture Models (GMM), and then efficient response matrix shift operations provide detection over orientations and scales. We avoid the undesirable characteristic of generative learning, a large number of training instances, with the novel concept of randomised Gaussian mixture model. Extensive experiments with public benchmarking Caltech-101 and BioID datasets demonstrate the effectiveness of our proposed method for localising object landmarks.

关键词： Feature extraction Gaussian mixture model Face Training Motorcycles Estimation

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Density-Aware Part-Based Object Detection with Positive Examples

Density-Aware Part-Based Object Detection with Positive Exam...

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International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Deparment of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.

关键词： Training Probabilistic logic Estimation Face Feature extraction Object detection Detectors

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Comparison of appearance-based and geometry-based bubble detectors

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

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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

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