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Deep Vesselness Measure from Scale-Space analysis of Hessian Matrix Eigenvalues 9th

Deep Vesselness Measure from Scale-Space Analysis of Hessian...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Araujo, Ricardo J. Cardoso, Jaime S. Oliveira, Helder P. INESC TEC Porto Portugal Univ Porto Fac Ciencias Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783030313210;9783030313203

The enhancement of tubular structures such as vessels in medical images has been addressed in the past, aiming for easier extraction and or visualization of such structures by professionals. Some literature methodologies propose vesselness measures whose design is motivated by local properties of vascular networks and how these influence the eigenvalues of the Hessian matrix. However, past work fails to combine properly the scale-space and neighborhood information, thus leading to the proposal of suboptimal vesselness measures. In this paper, we show that a shallow convolutional neural network is able to learn more optimal embedding spaces from the eigenvalue analysis at different scales, thus leading to a stronger vessel enhancement. Additionally, we also show that such a system maintains one of the biggest advantages of Hessian-based vesselness measures, which is the robustness to data with varying statistics.

关键词： Blood vessel enhancement Computer vision Deep learning

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CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

CascadeTabNet: An approach for end to end table detection an...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Devashish Prasad Ayan Gadpal Kshitij Kapadni Manish Visave Kavita Sultanpure Pune Institute of Computer Technology India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and Table-Bank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and Table-Bank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://***/DevashishPrasad/CascadeTabNet.

关键词： Task analysis image segmentation Semantics Head Predictive models Analytical models image recognition

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Negative Emotion recognition using Deep Learning for Thai Language 5

Negative Emotion Recognition using Deep Learning for Thai La...

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5th Joint International conference on Digital Arts, Media and Technology with 3rd ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Hnoohom, Narit University of Phayao Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center North Bangkok Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Nakorn Pathom Thailand

ISBN: (数字)9781728163987

ISBN: (纸本)9781728163987

Over the last decade, speech emotion recognition (SER) has become an interesting and challenging topic in the human behavior analysis research field. The objective of this area of research is to classify the emotional states of people based on the speech patterns of humans. Currently, the focus of this research field is on the identification of the effectiveness of automatic classifiers that can enhance the efficiency of the classification in practical applications, particularly those used in telecommunication services. Negative emotions, such as sadness, anger, disgust, and fear, can provide a significant amount of beneficial data to both the user of the quality assurance platform and the customer. This paper examines the complicated task involving recognition of negative emotions in human speech data by employing a deep learning technique. Four open emotional speech datasets are used in this research in order to identify a deep learning classifier that provides good efficiency for use with negative emotion speech data. Furthermore, the classifier with the best performance was also tested with a Thai language dataset. Based on the experimental results, the one-dimensional convolution neural network was determined to be the classifier that has the most outstanding performance level for tasks involving negative emotion recognition in the Thai language with a level of accuracy at 96.60%. © 2020 IEEE.

关键词： Emotion recognition

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Identification of flood potential in Rawa Danau, Banten Province based morphometry and satellite image analysis 3

Identification of flood potential in Rawa Danau, Banten Prov...

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3rd Life and Environmental Sciences Academics Forum, LEAF 2019

作者： Haidiati, F.C. Syahputra, R. Indra, T.L. Universitas Indonesia Depok16424 Indonesia

The Quaternary Volcanic Rawa Danau, Banten Province has a unique characteristic. Their deformed morphology reflects those unique characteristics. A physical characteristic of deformed morphology is able to describe the morphometry parameters of a subwatershed. It also can be analyzed using the Digital Elevation Model (DEM) obtained from satellite imagery. The lineament of geological structure can be observed from satellite images through their colour, shape, texture, and pattern. Subwatershed characteristic data can be used to calculate the flood discharge using morphometry. Some morphometry parameters can be used are river dimension, an azimuth of river segment, an azimuth of terrain lineament, river length, drainage density, and bifurcation ratio. This research can be used to identify flood potential at Rawa Danau-Banten Province. The result of the research shows that the value of Rb is less than 3, it means all of the subwatershed has deformed. At the Cidano, Ci Kaduan, Ci Bulakbaru, Ci Tawing, Ci Cangkepan, Ci Pasauran, Ci Sangkuy, Ci Carung value of Rb increased so it indicates that the deformation is quite substantial and also potentially flood. © Published under licence by IOP Publishing Ltd.

关键词： Floods

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Towards Automatic Rat's Gait analysis Under Suboptimal Illumination Conditions 9th

Towards Automatic Rat's Gait Analysis Under Suboptimal Illum...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Adonias, Ana F. Ferreira-Gomes, Joana Alonso, Raquel Neto, Fani Cardoso, Jaime S. Univ Porto Fac Engn Porto Portugal Tecnol & Ciencia Inst Engn Sistemas & Computadores Porto Portugal Univ Porto Inst Invest & Inovacao Saude Porto Portugal Univ Porto IBMC Inst Biol Mol & Celular Porto Portugal Univ Porto Fac Med Dept Biomed Unidade Biol Expt Porto Portugal

ISBN: (纸本)9783030313210;9783030313203

Rat's gait analysis plays an important role in the assessment of the impact of certain drugs on the treatment of osteoarthritis. Since movement-evoked pain is an early characteristic of this degenerative joint disease, the affected animal modifies its behavior to protect the injured joint from load while walking, altering its gait's parameters, which can be detected through a video analysis. Because commercially available video-based gait systems still present many limitations, researchers often choose to develop a customized system for the acquisition of the videos and analyze them manually, a laborious and time-consuming task prone to high user variability. Therefore, and bearing in mind the recent advances in machine learning and computer vision fields, as well as their presence in many tracking and recognition applications, this work is driven by the need to find a solution to automate the detection and quantification of the animal's gait changes making it an easier, faster, simpler and more robust task. Thus, a comparison between different methodologies to detect and segment the animal under degraded luminance conditions is presented in this paper as well as an algorithm to detect, segment and classify the animal's paws.

关键词： Computer vision Gait analysis Rat Osteoarthritis

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Convolutional neural network-based classification of histopathological images affected by data imbalance 1

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3rd Workshop on Face and Facial Expression recognition from Real World Videos, FFER 2018, and 2nd International Workshop on Deep Learning for pattern recognition, DLPR 2018, held at the 24th International conference on pattern recognition, ICPR 2018

作者： Koziarski, Michal Kwolek, Bogdan Cyganek, Boguslaw Department of Electronics AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland

ISBN: (数字)9783030121778

ISBN: (纸本)9783030121761

In this paper we experimentally evaluated the impact of data imbalance on the convolutional neural networks performance in the histopathological image recognition task. We conducted our analysis on the Breast Cancer Histopathological Database. We considered four phenomena associated with data imbalance: how does it affect classification performance, what strategies of preventing imbalance are suitable for histopathological data, how presence of imbalance affects the value of new observations, and whether sampling training data from a balanced distribution during data acquisition is beneficial if test data will remain imbalanced. The most important findings of our experimental analysis are the following: while high imbalance significantly affects the performance, for some of the metrics small imbalance. Sampling training data from a balanced distribution had a decremental effect, and we achieved a better performance applying a dedicated strategy of dealing with imbalance. Finally, not all of the traditional strategies of dealing with imbalance translate well to the histopathological image recognition setting. © Springer Nature Switzerland AG 2019.

关键词： image classification

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Going Back to Basics on Volumetric Segmentation of the Lungs in CT: A Fully image Processing Based Technique 9th

Going Back to Basics on Volumetric Segmentation of the Lungs...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Oliveira, Ana Catarina Domingues, Ines Duarte, Hugo Santos, Joao Abreu, Pedro H. Univ Coimbra Coimbra Portugal Portuguese Inst Oncol Porto IPO Porto Res Ctr Med Phys Radiobiol & Radiat Protect Grp Porto Portugal IPO Porto Nucl Med Dept Porto Portugal Univ Porto IPO Porto Inst Ciencias Biomed Abel Salazar Med Phys Dept Porto Portugal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9783030313210;9783030313203

Radiotherapy planning is a crucial task in cancer patients' management. This task is, however, very time consuming and prone to a high intra and inter subject variance and human errors. In this way, the present line of work aims at developing a tool to help the specialists in this task. The developed tool will consider the delimitation of anatomical regions of interest, since it is crucial to identify the organs at risk and minimize the exposure of these organs to the radiation. This paper, in particular, presents a lung segmentation algorithm, based on image processing techniques, such as intensity projection and region growing, for Computed Tomography volumes. Our pipeline consists in first separating two halves of the volume to isolate each lung. Then, three techniques for seed placement are developed. Finally, a traditional region growing algorithm has been changed in order to automatically derive the value of the threshold parameter. The results obtained for the three different techniques for seed placement were, respectively, 74%, 74% and 92% of DICE with the Iterative Region Growing algorithm. Although the presented results have as use case the Hodgkin Lymphoma, we believe that the developed method is generalizable to any other pathology.

关键词： Lung segmentation Computed Tomography (CT) 3D

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Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval

Two-stage Discriminative Re-ranking for Large-scale Landmark...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Shuhei Yokoo Kohei Ozaki Edgar Simo-Serra Satoshi Iizuka University of Tsukuba Preferred Networks Inc Waseda University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity. Our proposed re-ranking approach improves the results in two steps: in the sort-step, k-nearest neighbor search with soft-voting to sort the retrieved results based on their label similarity to the query images, and in the insert-step, we add additional samples from the dataset that were not retrieved by image-similarity. This approach allows overcoming the low visual diversity in retrieved images. In-depth experimental results show that the proposed approach significantly outperforms existing approaches on the challenging Google Landmarks Datasets. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge on Kaggle. Our code is publicly available here: https://***/lyakaap/Landmark2019-1st-and-3rd-Place-Solution.

关键词： image retrieval Indexes Training Task analysis Visualization Google Pipelines

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Data for image recognition Tasks: An Efficient Tool for Fine-Grained Annotations 8th

Data for Image Recognition Tasks: An Efficient Tool for Fine...

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8th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Filax, Marco Gonschorek, Tim Ortmeier, Frank Otto von Guericke Univ Chair Software Engn Magdeburg Germany

ISBN: (纸本)9789897583513

Using large datasets is essential for machine learning. In practice, training a machine learning algorithm requires hundreds of samples. Multiple off-the-shelf datasets from the scientific domain exist to benchmark new approaches. However, when machine learning algorithms transit to industry, e.g., for a particular image classification problem, hundreds of specific purpose images are collected and annotated in laborious manual work. In this paper, we present a novel system to decrease the effort of annotating those large image sets. Therefore, we generate 2D bounding boxes from minimal 3D annotations using the known location and orientation of the camera. We annotate a particular object of interest in 3D once and project these annotations on to every frame of a video stream. The proposed approach is designed to work with off-the-shelf hardware. We demonstrate its applicability with an example from the real world. We generated a more extensive dataset than available in other works for a particular industrial use case: fine-grained recognition of items within grocery stores. Further, we make our dataset available to the interested vision community consisting of over 60,000 images. Some images were taken under ideal conditions for training while others were taken with the proposed approach in the wild.

关键词： image and Video analysis Assistive Computer Vision Fine-Grained recognition Dataset

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Pencil Drawing of Microscopic images Through Edge Preserving Filtering 9th

Pencil Drawing of Microscopic Images Through Edge Preserving...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Singh, Harbinder Sanchez, Carlos Cristobal, Gabriel Bueno, Gloria Chandigarh Engn Coll Landran Mohali India Inst Opt CSIC Serrano 121 Madrid 28006 Spain Univ Castilla La Mancha VISILAB Ciudad Real Spain

ISBN: (纸本)9783030313210;9783030313203

Automatic diatom identification approaches have revealed remarkable abilities to tackle the challenges of water quality assessment and other environmental issues. Scientists often analyze the taxonomic characters of the target taxa for automatic identification. In this process the digital photographs, sketches or drawings are recorded to analyze the shape and size of the frustule, the arrangement of striae, the raphe endings, and the striae density. In this paper, we describe two new methods for producing drawings of different diatom species at any stage of their life cycle development that can also be useful for future reference and comparisons. We attempt to produce drawings of diatom species using Edge-preserving Multi-scale Decomposition (EMD). The edge preserving smoothing property of Weighted Least Squares (WLS) optimization framework is used to extract high-frequency details. The details extracted from two-scale decomposition are transformed to drawings which help in identifying possible striae patterns from diatom images. To analyze the salient local features preserved in the drawings, the Scale Invariant Feature Transform (SIFT) model is adopted for feature extraction. The generated drawings help to identify certain unique taxonomic and morphological features that are necessary for the identification of the diatoms. The new methods have been compared with two alternative pencil drawing techniques showing better performance for details preservation.

关键词： Edge-preserving filters Diatom identification Taxonomic Drawings Feature analysis

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