Martin Kampel

Bio: Martin Kampel is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Computer science & Video tracking. The author has an hindex of 23, co-authored 167 publications receiving 1988 citations. Previous affiliations of Martin Kampel include Bosch & University of Vienna.

Facial Expression Recognition using Convolutional Neural Networks: State of the Art.

Abstract: The ability to recognize facial expressions automatically enables novel applications in human-computer interaction and other areas. Consequently, there has been active research in this field, with several recent works utilizing Convolutional Neural Networks (CNNs) for feature extraction and inference. These works differ significantly in terms of CNN architectures and other factors. Based on the reported results alone, the performance impact of these factors is unclear. In this paper, we review the state of the art in image-based facial expression recognition using CNNs and highlight algorithmic differences and their performance impact. On this basis, we identify existing bottlenecks and consequently directions for advancing this research field. Furthermore, we demonstrate that overcoming one of these bottlenecks - the comparatively basic architectures of the CNNs utilized in this field - leads to a substantial performance increase. By forming an ensemble of modern deep CNNs, we obtain a FER2013 test accuracy of 75.2%, outperforming previous works without requiring auxiliary training data or face registration.

Introducing the use of depth data for fall detection

Abstract: Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.

3D MURALE: a multimedia system for archaeology

Abstract: This paper introduces the 3D Measurement and Virtual Reconstruction of Ancient Lost Worlds of Europe system (3D MURALE). It consists of a set of tools for recording, reconstructing, encoding, visualising and database searching/querying that operate on buildings, building parts, statues, statue parts, pottery, stratigraphy, terrain geometry and texture and material texture. The tools are loosely linked together by a common database on which they all have the facility to store and access data. The paper describes the overall architecture of the 3D MURALE system and then briefly describes the functionality of the tools provided by the project. The paper compares the multimedia studio architecture adopted in this project with other multimedia studio architectures.

An Empirical Evaluation of Ten Depth Cameras: Bias, Precision, Lateral Noise, Different Lighting Conditions and Materials, and Multiple Sensor Setups in Indoor Environments

Abstract: Over the last few years, novel color and depth sensors have pushed the boundaries of robot perception significantly. Today, several new depth-sensing products are replacing the earlier, well-examined red-green-blue-depth (RGBD) sensors, which have reached the end of their product life cycle and are no longer available. The properties of the new sensors have not yet been investigated, and it is unclear how they will compare to earlier RGBD sensors.

Profile-based Pottery Reconstruction

Abstract: A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric information describing an object, and merging geometric, texture and semantic data. We present a fully automated approach to pottery reconstruction based on the fragment profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. We demonstrate the method and give results on synthetic and real data.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Searching and mining trillions of time series subsequences under dynamic time warping

Abstract: Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

Information retrieval

Abstract: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …