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International Workshop on Frontiers in Handwriting recognition

作者： Purnendu Banerjee Ujjwal Bhattacharya Bidyut B. Chaudhuri Society for Natural Language Technology Research Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Automatic recognition of handwritten texts in video lectures has important applications. In video lectures, the presenter usually writes on white / colored board. The video camera often captures the writing board along with certain other objects possibly including the presenter itself. recognition of handwritten texts from such a video frame requires prior detection of the region of texts in the frame. In this article, we present our recent study of text localization in such video lecture frames. Here, we use Scale Invariant Feature Transform (SIFT) descriptors densely over the entire region of the frame. The descriptors are located on a regular grid of 5 pixels following the usual practice and considered a uniform patch size of 60 × 60 pixels as its support on the basis of an empirical study. This SIFT descriptor at each location (grid point) is fed as a 128-dimensional input feature vector to a Multilayer Perceptron (MLP) network which gives response for each grid point as either text or non-text. Depending on certain aggregate response at each pixel we localize text regions in the input video frame. Next, we employ K-means clustering to detect the text components present in the localized region of the video frame. Finally, two simple rules are applied to decide certain possible detected text components as noise. We obtained encouraging simulation results of this approach on a variety of video lecture frames.

关键词： Handwriting recognition Text recognition Training Databases Image color analysis Cameras Noise

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Handbook of Face recognition 1

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1000年

作者： Stan Z. Li Anil K. Jain

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Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography

Unsupervised detection of Small Hyperreflective Features in ...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Reimann, Marcel Won, Jungeun Takahashi, Hiroyuki Yaghy, Antonio Hwang, Yunchan Ploner, Stefan Lin, Junhong Girgis, Jessica Lam, Kenneth Chen, Siyu Waheed, Nadia K. Maier, Andreas Fujimoto, James G. Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany New England Eye Center Tufts Medical Center United States

ISBN: (纸本)9783658416560

Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a need for automatic detection arises. Labeled datasets are often not publicly available and there are usually large variations in scan protocols and scanner types. Thus, this work focuses on an unsupervised approach that is based on local peak-detection and random walker segmentation to detect small features on each B-scan of the volume. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Optical tomography

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A High Semantic Representation for Abnormal Events Detection in Crowded Scenes

A High Semantic Representation for Abnormal Events Detection...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Ye Tao Ye Jin Peng Liu Pattern Recognition and Intelligent System Research Center School of Computer Science and TechnologyHarbin Institute of Technology

Many crowd abnormal motion detection methods in video surveillance have been proposed in resent ***,most of them are based on low semantic features,such gray value,velocity and ***,low semantic features contain weak discriminative information of the *** addition,these methods often ignore important information in time and space *** this work,a high semantic representation is *** feature analysis（SFA） is adopted to provide high semantic ***,a random walk model,which takes into account the spatio-temporal information,is used to detect the abnormal motions in *** conduct extensive experiments on two datasets to demonstrate the effectiveness of proposed *** results suggest that our method outperforms the state-of-the-art methods.

关键词： Video surveillance Crowd analysis Abnormal events Slow feature analysis

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Generalized stochastic tube model: Tracking 3D blood vessels in MR images 12

Generalized stochastic tube model: Tracking 3D blood vessels...

引用

12th IAPR International Conference on pattern recognition - Conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Huang, Qian Stockman, George C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

ISBN: (纸本)0818662700

This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed Generalized Tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Blood vessels

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Leveraging Acoustic Images for Effective Self-supervised Audio Representation Learning 1

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16th European Conference on computer vision, ECCV 2020

作者： Sanguineti, Valentina Morerio, Pietro Pozzetti, Niccolò Greco, Danilo Cristani, Marco Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy University of Genova Genoa Italy Huawei Technologies Ltd. Ireland Research Center Dublin Ireland University of Verona Verona Italy

ISBN: (数字)9783030585426

ISBN: (纸本)9783030585419

In this paper, we propose the use of a new modality characterized by a richer information content, namely acoustic images, for the sake of audio-visual scene understanding. Each pixel in such images is characterized by a spectral signature, associated to a specific direction in space and obtained by processing the audio signals coming from an array of microphones. By coupling such array with a video camera, we obtain spatio-temporal alignment of acoustic images and video frames. This constitutes a powerful source of self-supervision, which can be exploited in the learning pipeline we are proposing, without resorting to expensive data annotations. However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised learning scheme, into more powerful audio and visual feature representations. The learnt feature representations can then be employed for downstream tasks such as classification and cross-modal retrieval, without the need of a microphone array. To prove that, we introduce a novel multimodal dataset consisting in RGB videos, raw audio signals and acoustic images, aligned in space and synchronized in time. Experimental results demonstrate the validity of our hypothesis and the effectiveness of the proposed pipeline, also when tested for tasks and datasets different from those used for training. © 2020, Springer Nature Switzerland AG.

关键词： Supervised learning

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

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The 1994 1st IEEE International Conference on Image Processing

作者： Huang, Qian Stockman, G.C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown. © 1994 IEEE.

关键词： Blood vessels

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Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation

Box-driven Class-wise Region Masking and Filling Rate Guided...

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Chunfeng Song Yan Huang Wanli Ouyang Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The University of Sydney SenseTime Computer Vision Research Group

ISBN: (纸本)9781728132945

Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN based models severely rely on the amounts of pixel-level annotations which are expensive and time-consuming. To address this problem, it is a good choice to learn to segment with weak supervision from bounding boxes. How to make full use of the class-level and region-level supervisions from bounding boxes is the critical challenge for the weakly supervised learning task. In this paper, we first introduce a box-driven class-wise masking model (BCM) to remove irrelevant regions of each class. Moreover, based on the pixel-level segment proposal generated from the bounding box supervision, we could calculate the mean filling rates of each class to serve as an important prior cue, then we propose a filling rate guided adaptive loss (FR-Loss) to help the model ignore the wrongly labeled pixels in proposals. Unlike previous methods directly training models with the fixed individual segment proposals, our method can adjust the model learning with global statistical information. Thus it can help reduce the negative impacts from wrongly labeled proposals. We evaluate the proposed method on the challenging PASCAL VOC 2012 benchmark and compare with other methods. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results.

关键词： bounding boxes filling rate Masking Semantics Supervision Regulated industries

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A New Method for Handwritten Scene Text Detection in Video

A New Method for Handwritten Scene Text Detection in Video

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International Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara Anjan Dutta Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision Center Universitat Authnòma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K = 2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising.

关键词： Pixel Image edge detection Image color analysis Graphics Clustering algorithms Image resolution Measurement

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Non-deterministic behavior of ranking-based metrics when evaluating embeddings

arXiv

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arXiv 2018年

作者： Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universitat Erlangen-Nurnberg

Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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