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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

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Sleep-stage scoring in mice: The influence of data pre-processing on a system's performance

Sleep-stage scoring in mice: The influence of data pre-proce...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasiliki-Maria Katsageorgiou Glenda Lassi Valter Tucci Vittorio Murino Diego Sona Department of Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy Department of Neuroscience and Brain Technologies (NBT) Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy Neuroinformatics Laboratory (NILab) Fondazione Bruno Kessler (FBK) Trento Italy

Sleep-stage analysis in mice and rats has received growing attention in recent years, due to the fact that mice display electrical activity during sleep which has underlying similarities with that of human sleep. Both conventional manual and automatic sleep-wakefulness scoring are rule based tasks which use brain waves measured by Electroencephalogram (EEG) and activity detected by Electromyography (EMG) of skeletal muscles. Several works have been conducted trying to provide an automatic sleep-scoring system on the basis of machine learning methods. In this study we try to understand the reasons behind the complexity of this problem and we emphasize the importance of normalization procedure that leads to a better stage discrimination comparing different classification methods.

关键词： Sleep Electroencephalography Electromyography Mice Niobium Support vector machines

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An End-to-End Video Text Detector with Online Tracking

An End-to-End Video Text Detector with Online Tracking

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International Conference on Document Analysis and Recognition

作者： Hongyuan Yu Chengquan Zhang Xuan Li Junyu Han Errui Ding Liang Wang University of Chinese Academy of Sciences (UCAS) Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Department of Computer Vision Technology(VIS) Baidu Inc. Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods attempt to enhance the performance of video text detection by cooperating with video text tracking, but treat these two tasks separately. In this work, we propose an end-to-end video text detection model with online tracking to address these two challenges. Specifically, in the detection branch, we adopt ConvLSTM to capture spatial structure information and motion memory. In the tracking branch, we convert the tracking problem to text instance association, and an appearance-geometry descriptor with memory mechanism is proposed to generate robust representation of text instances. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Experiments on existing video text benchmarks including ICDAR2013 Video, Minetto and YVT demonstrate that the proposed method significantly outperforms state-of-the-art methods. Our method improves F-score by about 2% on all datasets and it can run realtime with 24.36 fps on TITAN Xp.

关键词： Feature extraction Geometry Task analysis Tracking Trajectory Proposals Loss measurement

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Masked Image Training for Generalizable Deep Image Denoising

Masked Image Training for Generalizable Deep Image Denoising

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Conference on computer vision and pattern Recognition (CVPR)

作者： Haoyu Chen Jinjin Gu Yihao Liu Salma Abdel Magid Chao Dong Qiong Wang Hanspeter Pfister Lei Zhu The Hong Kong University of Science and Technology (Guangzhou) Shanghai AI Lab The University of Sydney ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Harvard University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences The Hong Kong University of Science and Technology

When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method.

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

arXiv

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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

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New COLD Feature Based Handwriting Analysis for Enthnicity/Nationality Identification

New COLD Feature Based Handwriting Analysis for Enthnicity/N...

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International Workshop on Frontiers in Handwriting Recognition

作者： Sauradip Nag Palaiahnakote Shivakumara Yirui Wu Umapada Pal Tong Lu Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, uses tangent angle of the contour pixels in each row and the mean of intensity values of each row for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method.

关键词： Feature extraction Shape Image edge detection Writing Image segmentation Forensics Support vector machines

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A new unified method for detecting text from marathon runners and sports players in video

arXiv

引用

arXiv 2020年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Sydney Australia

Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper presents a new unified method for tackling the above challenges. The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions. Candidate regions are used for determining the number of temporal frame clusters obtained by K-means clustering on frame differences. This process in turn detects key frames. The proposed method explores Bayesian probability for skin portions using color values at both pixel and component levels of temporal frames, which provides fused images with skin components. Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences between them. We further propose adaptive pixels linking a deep learning model for text detection from torso regions. The proposed method is tested on our own dataset collected from marathon/sports video and three standard datasets, namely, RBNR, MMM and R-ID of marathon images, to evaluate the performance. In addition, the proposed method is also tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text datasets, to show the objectiveness of the proposed method. A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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computer vision Approaches for Automated Bee Counting Application

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IFAC-PapersOnLine 2024年第9期58卷 43-48页

作者： Simon Bilik Ilona Janakova Adam Ligocki Dominik Ficek Karel Horak Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic and Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.

关键词： Signal Processing Biomedical systems bee counting bee traffic monitoring computer vision deep classification object detection

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A feasibility study of on-board data compression for infrared cameras of space observatories

A feasibility study of on-board data compression for infrare...

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International Conference on pattern Recognition

作者： C. Reimers A.N. Belbachir H. Bischof R. Ottensamer D.A. Cesarsky H. Feuchtgruber F. Kerschbaum A. Poglitsch Institute of Astronomy University of Technology Vienna Vienna Austria Pattern Recognition and Image Procesing Group University of Technology Vienna Vienna Austria Institute of Computer Graphics and Vision Technical University of Graz Graz Austria Max-Planck Institute of Extraterrestrial Physics Garching Germany

In this paper, the feasibility of on-board data reduction/compression concept described in is evaluated for infrared images taken from space observatories. The method described in, which was initially designed and developed for the PACS (photodetector array camera and spectrometer) instrument, makes use of on-board integration to achieve higher compression ratio (CR) for applications with a modest telemetry rate. The evaluation of the reduction concept takes into account the visual performance (distortion) and the compression ratio. The distortion is assessed by calculating the reconstruction error using 4 metrics, namely, root mean square error (RMSE), signal-to-noise-ratio (SNR), peak-signal-to-noise-ratio (PSNR) and potential information loss (PIL). A quantitative evaluation of the on-board compression concept is performed on data from the infrared camera ISOCAM (infrared space observatory camera). We conclude with a short summary.

关键词： Data compression Cameras Observatories Image coding Infrared imaging Picture archiving and communication systems Photodetectors Spectroscopy Instruments Chromium

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MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration

arXiv

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arXiv 2024年

作者： Ding, Yanbo Zhuang, Shaobin Li, Kunchang Yue, Zhengrong Qiao, Yu Wang, Yali Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China

Despite recent advancements in text-to-image generation, most existing methods struggle to create images with multiple objects and complex spatial relationships in the 3D world. To tackle this limitation, we introduce a generic AI system, namely MUSES, for 3D-controllable image generation from user queries. Specifically, our MUSES develops a progressive workflow with three key components, including (1) Layout Manager for 2D-to-3D layout lifting, (2) Model Engineer for 3D object acquisition and calibration, (3) Image Artist for 3D-to-2D image rendering. By mimicking the collaboration of human professionals, this multi-modal agent pipeline facilitates the effective and automatic creation of images with 3D-controllable objects, through an explainable integration of top-down planning and bottom-up generation. Additionally, existing benchmarks lack detailed descriptions of complex Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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