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Hierarchical classification with reject option for live fish recognition

MACHINE VISION AND APPLICATIONS 2015年第1期26卷 89-102页

作者： Huang, Phoenix X. Boom, Bastiaan J. Fisher, Robert B. Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland Int Assoc Pattern Recognit Stockholm Sweden British Machine Vis Assoc Taunton Somerset England

A live fish recognition system is needed in application scenarios where manual annotation is too expensive, i.e. too many underwater videos. We present a novel balance-enforced optimized tree with reject option (BEOTR) for live fish recognition. It recognizes the top 15 common species of fish and detects new species in an unrestricted natural environment recorded by underwater cameras. The three main contributions of the paper are: (1) a novel hierarchical classification method suited for greatly unbalanced classes, (2) a novel classification-rejection method to clear up decisions and reject unknown classes, (3) an application of the classification method to free swimming fish. This system assists ecological surveillance research, e.g. fish population statistics in the open sea. BEOTR is automatically constructed based on inter-class similarities. Afterwards, trajectory voting is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. We apply a Gaussian mixture model and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. The proposed BEOTR-based hierarchical classification method achieves significant improvements compared to state-of-the-art techniques on a live fish image dataset of 24,150 manually labelled images from South Taiwan Sea.

关键词： BEOTR Live fish recognition Hierarchical classification Reject option GMM

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intELLIGENT SENSOR - PREFACE

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NEW GENERATION COMPUTING 1985年第4期3卷 339-340页

作者： SAKAI, T INT ASSOC PATTERN RECOGNIT QUEBEC CITYQUEBECCANADA KYOTO UNIV DEPT INFORMAT SCISAKYO KUKYOTO 606JAPAN

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A Mechanically Beam-Steered Phased Array Antenna for Power-Harvesting Applications

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IEEE ANTENNAS AND PROPAGATION MAGAZINE 2016年第3期58卷 58-64页

作者： Shao, Shuai Gudan, Ken Hull, Jonathan J. Ricoh Innovat Corp Menlo Pk CA USA Ricoh Innovat Corp Intelligent Network Syst Res Grp Menlo Pk CA USA Int Assoc Pattern Recognit Nagoya Aichi Japan

Beam-steered antennas are highly desirable for radio-frequency (RF)- power-harvesting applications. By changing the direction of its main beam, maximum power can be harvested without physically rotating the antenna. This is specifically favorable to power-harvesting applications where the harvesting unit needs to be placed on a planar surface, cannot be rotated, and the available volume is constrained. We present a 2 X 2 beam-steered phased array antenna for RF-power-harvesting applications. The designed antenna array achieves a continuous beam-steering angle of ?3? in the E-plane. Beam steering is realized by mechanically shifting the position of the feeding point. The antenna does not utilize any active elements; thereby, the maximum amount of power can be delivered into the harvesting circuitry. A small and wideband patch antenna element has also been designed and incorporated in the array, which reduces its overall size and enables wide scanning angle impedance matching. At the operating frequency of 2.4 GHz, the size of the array is 0.96 X 0.96 X 0.056 ?. The measured peak gain is 11.3 dBi, and bandwidth is 12.9%.

关键词： Antenna feeds Phased arrays Antenna measurements Radio frequency Beam steering

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Privacy as a Feature for Body-Worn Cameras

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IEEE SIGNAL PROCESSING MAGAZINE 2020年第4期37卷 148-+页

作者： Cross, Maria S. Cavallaro, Andrea QMUL Ctr Intelligent Sensing London England Queen Mary Univ London QMUL Multimedia Signal Proc London England Alan Turing Inst London England Int Assoc Pattern Recognit London England

Reports on the technology of body worn cameras (BWMs) and discusses the threat to privacy that this passive data collection creates, along with opportunities to mitigate this risk. Furthermore, we argue that the use case of BWCs at work will stimulate the development of solutions that prevent the collection of data that could infringe upon the privacy of the wearer. Finally, we discuss the desirable properties of privacy-enhancing technologies (PETs) for BWCs.

关键词： Cameras Wearable computing Privacy

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Visual Domain Adaptation

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IEEE SIGNAL PROCESSING MAGAZINE 2015年第3期32卷 53-69页

作者： Patel, Vishal M. Gopalan, Raghuraman Li, Ruonan Chellappa, Rama Harvard Univ Cambridge MA 02138 USA Univ Maryland UMD Engn College Pk MD USA Univ Maryland UMD Elect & Commun Engn Dept College Pk MD USA Int Assoc Pattern Recognit Adelaide SA Australia Opt Soc Amer Washington DC USA Amer Assoc Advancement Sci Cambridge MA USA Assoc Comp Machinery New York NY USA

In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field.

关键词： Semisupervised learning Classification algorithms pattern recognition Visualization Signal processing algorithms Computer vision Training data

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IEEE ACCESS SPECIAL SECTION EDITORIAL APPLYING FOUR D'S OF MACHINE LEARNING TO ADVANCE BIOMETRICS

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IEEE ACCESS 2015年 3卷 3083-3084页

作者： Vatsa, Mayank Singh, Richa Bowyer, Kevin Indraprastha Inst Informat Technol Delhi India Univ Notre Dame Comp Sci & Engn Notre Dame IN 46556 USA Univ Notre Dame Chair Dept Notre Dame IN 46556 USA Int Assoc Pattern Recognit Contribut Comp Vis Pat Venice Italy

With the availability of inexpensive biometric sensors, computing power, and memory, it is becoming increasingly clear that biometrics technology will have broader usage, and therefore broader scope of future research in addressing newer challenges and pushing the boundaries. If we perceive of biometrics as a fundamental technology with broad scientific and economic impact, then designing efficient algorithms and systems will require a multidisciplinary effort in signal/image processing, pattern recognition, machine learning, sensor design, embedded systems, and information fusion.

关键词： Special issues and sections Biometrics Biosensors Machine learning

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A Primer on Cluster Analysis

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IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 2018年第1期4卷 48-50页

作者： Hall, Lawrence O. Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Amer Assoc Advancement Sci Washington DC 20036 USA Int Assoc Pattern Recognit Washington DC 20036 USA

This book focuses on four widely used basic clustering methods suitable for most unlabeled data. The algorithms are k-means, fuzzy c-means (FCM), expectation maximization (EM), and a couple for relational clustering (sequential, agglomerative, hierarchical, and nonoverlapping). There is also a discussion of how to choose the right number of clusters for each approach. This includes internal and external validity measures for the clusters obtained by applying an algorithm. The book includes more than the four basic methods when variants and competitors are noted during discussions of the methods, and it dives into clustering approaches for big data. At the end of the chapters are historical notes, exercises, and additional examples of other algorithms. To whom is this book useful? If you have read this far, it is likely you. It is especially valuable for someone just starting to use clustering who wants background information on major underlying approaches. There are many nicely illustrated examples of clustering and validation of clusters. It is also of interest to people who know about clustering and use/develop algorithms. The book has a deep, rigorous mathematical treatment of the subject and covers work in an area with which only few are completely familiar. The historical notes are likely to be informational to most. Finally, if you teach a course on clustering, this would serve as a great textbook. It includes a deep discussion of algorithms and their variations with exercises, then moves to the modern problem of clustering big-data sets. It also shows how to evaluate the resulting partition (set of clusters). To whom is this book useful? If you have read this far, it is likely you. It is especially valuable for someone just starting to use clustering who wants background information on major underlying approaches. There are many nicely illustrated examples of clustering and validation of clusters. It is also of interest to people who know about clustering and

关键词： Book reviews Clustering algorithms Cybernetics Algorithm design and analysis Fuzzy methods

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Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

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IEEE SIGNAL PROCESSING MAGAZINE 2020年第4期37卷 14-23页

作者： Modas, Apostolos Sanchez-Matilla, Ricardo Frossard, Pascal Cavallaro, Andrea Ecole Polytech Fed Lausanne Signal Proc Lab 4 Lausanne Switzerland Queen Mary Univ London Ctr Intelligent Sensing London England Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab 4 Lausanne Switzerland IBM TJ Watson Res Ctr Yorktown Hts NY USA Alan Turing Inst London England Int Assoc Pattern Recognit Cedar Knolls NJ USA

Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1]. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations and can even be adversarial perturbations (APs).

关键词： Sensors Perturbation methods Jamming Global Positioning System Acoustics Laser radar

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Kinect Sensor Gesture and Activity recognition New applications for consumer cognitive systems

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IEEE CONSUMER ELECTRONICS MAGAZINE 2018年第1期7卷 88-94页

作者： Gavrilova, Marina L. Wang, Yingxu Ahmed, Faisal Paul, Padma Polash Univ Calgary Biomet Secur & Pattern Recognit Calgary AB Canada Biomet Technol Lab Calgary AB Canada Spatial Anal & Res Computat Sci Lab Calgary AB Canada Int Inst Cognit Informat & Cognit Comp Calgary AB Canada Int Conf Informat & Comp Calgary AB Canada World Innovat Fdn Huddersfield W Yorkshire England Assoc Comp Machinery New York NY USA Islamic Univ Technol Gazipur Bangladesh Univ Oxford Oxford Computat Neurosci Lab Oxford England Brain Sci Fdn Providence RI USA

Cognitive consumer electronics (CE), the fastest-growing sector worldwide that is driven by machine intelligence and cognitive systems, is triggered and enabled by audio- and video-capturing devices, smart sensors, health- and fitness-monitoring devices, security and education electronics, and intelligent systems. Smart consumer sensors and cognitive systems are synergized through the internet of Things (IoT) for optimal information sharing, communication, real-time updates, data analytics, and enhanced support for decision making. Biometric-based devices, originally intended for large-scale applications in airports, border controls, disaster zones, or refugee migration zones, are enabling a wide range of applications in commercial and consumer sectors as standalone systems or with interconnected sensor networks. This article introduces the applications of Microsoft Kinect in cognitive systems for smart CE, and, using Kinect sensors, a human-behavior cognition technology is presented for gesture and activity recognition. As a novel front end of pervasive cognitive systems, the challenges and applications of a Kinect sensor-based system will be explored in CE, such as smart automobiles, health care, surveillance, and activity recognition.

关键词： Gait recognition Cognitive systems Real-time systems Computational modeling Activity recognition Consumer electronics Security

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Privacy-Aware Human Activity recognition From a Wearable Camera: Highlights From the IEEE Video And Image Processing Cup 2019 Student Competition

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IEEE SIGNAL PROCESSING MAGAZINE 2020年第3期37卷 168-172页

作者： Tadesse, Girmaw Abebe Bent, Oliver Marcenaro, Lucio Weldemariam, Komminist Cavallaro, Andrea IBM Res Africa Nairobi Kenya Univ Oxford Oxford England Machine Learning Res Grp Oxford England Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit Genoa Italy IEEE Signal Proc Soc Student Serv Comm Piscataway NJ USA Next Einstein Forum Kigali Rwanda Queen Mary Univ London QMUL Multimedia Signal Proc London England Alan Turing Inst London England QMUL Ctr Intelligent Sensing London England Int Assoc Pattern Recognit Cedar Knolls NJ USA

Presents highlights from the IEEE Video and Image Processing Cup 2019 Student Competition.

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