Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

/pdf/content-based-image-retrieval-at-the-end-of-the-early-years-q45iy9ejai.pdf

Content-based image retrieval at the end of the early years

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Thank you very much for reading the oxford handbook of event related potential components. Maybe you have knowledge that, people have look numerous times for their chosen readings like this the oxford handbook of event related potential components, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their desktop computer.

The Oxford Handbook Of Event Related Potential Components

Eye tracking has a long history in medical and psychological research as a tool for recording and studying human visual behavior. Real-time gaze-based text entry can also be a powerful means of communication and control for people with physical disabilities. Following recent technological advances and the advent of affordable eye trackers, there is a growing interest in pervasive attention-aware systems and interfaces that have the potential to revolutionize mainstream human-technology interaction. In this chapter, we provide an introduction to the state-of-the art in eye tracking technology and gaze estimation. We discuss challenges involved in using a perceptual organ, the eye, as an input modality. Examples of real life applications are reviewed, together with design solutions derived from research results. We also discuss how to match the user requirements and key features of different eye tracking systems to find the best system for each task and application.

Eye Tracking and Eye-Based Human–Computer Interaction

Augmented Reality (AR) gains increased attention as a means to provide assistance for different human activities. Hereby the suitability of AR does not only depend on the respective task, but also to a high degree on the respective device. In a standardized assembly task, we tested AR-based in-situ assistance against conventional pictorial instructions using a smartphone, Microsoft HoloLens and Epson Moverio BT-200 smart glasses as well as paper-based instructions. Participants solved the task fastest using the paper instructions, but made less errors with AR assistance on the Microsoft HoloLens smart glasses than with any other system. Methodically we propose operational definitions of time segments and other optimizations for standardized benchmarking of AR assembly instructions.

/pdf/comparing-conventional-and-augmented-reality-instructions-un7sc0j5to.pdf

Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks

In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.

Hierarchical color image region segmentation for content-based image retrieval system

Research in sports, dance and rehabilitation has shown that basic action concepts (BACs) are fundamental building blocks of mental action representations. BACs are based on chunked body postures related to common functions for realizing action goals. In this paper, we outline issues in research methodology and an experimental method, the structural dimensional analysis of mental representation (SDA-M), to assess action-relevant representational structures that reflect the organization of BACs. The SDA-M reveals a strong relationship between cognitive representation and performance if complex actions are performed. We show how the SDA-M can improve motor imagery training and how it contributes to our understanding of coaching processes. The SDA-M capitalizes on the objective measurement of individual mental movement representations before training and the integration of these results into the motor imagery training. Such motor imagery training based on mental representations (MTMR) has been applied successfully in professional sports such as golf, volleyball, gymnastics, windsurfing, and recently in the rehabilitation of patients who have suffered a stroke.

/pdf/mental-representation-and-motor-imagery-training-hmqd7rqxhj.pdf

Mental representation and motor imagery training.

Using eye tracking for the investigation of visual attention has become increasingly popular during the last few decades. Nevertheless, only a small number of eye tracking studies have employed 3D displays, although such displays would closely resemble our natural visual environment. Besides higher cost and effort for the experimental setup, the main reason for the avoidance of 3D displays is the problem of computing a subject's current 3D gaze position based on the measured binocular gaze angles. The geometrical approaches to this problem that have been studied so far involved substantial error in the measurement of 3D gaze trajectories. In order to tackle this problem, we developed an anaglyph-based 3D calibration procedure and used a well-suited type of artificial neural network—a parametrized self-organizing map (PSOM)—to estimate the 3D gaze point from a subject's binocular eye-position data. We report an experiment in which the accuracy of the PSOM gaze-point estimation is compared to a geometrical ...

/pdf/a-neural-network-for-3d-gaze-recording-with-binocular-eye-1d1uhrngur.pdf

A neural network for 3D gaze recording with binocular eye trackers

Two core elements for the coordination of different actions in sport are tactical information and knowledge about tactical situations. The current study describes two experiments to learn about the memory structure and the cognitive processing of tactical information. Experiment 1 investigated the storage and structuring of team-specific tactics in humans’ long-term memory with regard to different expertise levels. Experiment 2 investigated tactical decision-making skills and the corresponding gaze behavior, in presenting participants the identical match situations in a reaction time task. The results showed that more experienced soccer players, in contrast to less experienced soccer players, possess a functionally organized cognitive representation of team-specific tactics in soccer. Moreover, the more experienced soccer players reacted faster in tactical decisions, because they needed less fixations of similar duration as compared to less experienced soccer players. Combined, these experiments offer evidence that a functionally organized memory structure leads to a reaction time and a perceptual advantage in tactical decision-making in soccer. The discussion emphasizes theoretical and applied implications of the current results of the study.

/pdf/cognitive-representations-and-cognitive-processing-of-team-3ozkt538dt.pdf

Kai Essig

Papers

Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks

Hierarchical color image region segmentation for content-based image retrieval system

Mental representation and motor imagery training.

A neural network for 3D gaze recording with binocular eye trackers

Cognitive representations and cognitive processing of team-specific tactics in soccer.