Object Categories

Finnish architect Pekka Harni runs a design and architecture practice in Helsinki together with industrial designer Yuka Takahashi. Their collaboration results in a variety of work, of which this book on the classification of objects is just one part. Based on a morphological-functional consideration of the properties of household objects, the study proposes to organise the forms of artefacts, determine the significance of their parts and explain the relationships between objects and the environment, thus describing their most important basic properties while exploring the realm of functional form.

This book covers a broad range of current research topics in category development. Its aim is to understand the perceptual and cognitive mechanisms that underlie category formation and how they change in developmental time. The chapters in this book are

This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.

An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

This book focuses on enabling mobile robots to recognize scenes in indoor environments, in order to allow them to determine which actions are appropriate at which points in time. In concrete terms, future robots will have to solve the classification problem represented by scene recognition sufficiently well for them to act independently in human-centered environments. To achieve accurate yet versatile indoor scene recognition, the book presents a hierarchical data structure for scenes – the Implicit Shape Model trees. Further, it also provides training and recognition algorithms for these trees. In general, entire indoor scenes cannot be perceived from a single point of view. To address this problem the authors introduce Active Scene Recognition (ASR), a concept that embeds canonical scene recognition in a decision-making system that selects camera views for a mobile robot to drive to so that it can find objects not yet localized. The authors formalize the automatic selection of camera views as a Next-Best-View (NBV) problem to which they contribute an algorithmic solution, which focuses on realistic problem modeling while maintaining its computational efficiency. Lastly, the book introduces a method for predicting the poses of objects to be searched, establishing the otherwise missing link between scene recognition and NBV estimation.

The field of Object-Oriented Programming (OOP) has attracted increasing attention during the last few years. OOP is now recognized as an important tool for making better and more flexible information systems. This book is the proceedings of the second European Conference on Object-Oriented Programming (ECOOP '88) that was held in Oslo, Norway, from August 15 to 17, 1988. The objectives of ECOOP '88 were to present the best international work in the field of OOP to interested persons from industry and academia, and to be a forum for the exchange of ideas and the growth of professional relationships. Each of the 103 papers submitted was subject to a thorough refereeing process. The 22 papers selected are collected in these proceedings together with one invited paper. These 23 papers from 13 different countries comprise the currently best international work in the field of OOP. The contents of the papers include areas such as: Theory, Languages, Didactics, Implementation, Applications, Concurrency and Databases. The interest in object-oriented programming is rapidly increasing, especially within the areas of Concurrency and Databases. With its 5 papers on concurrency and 7 papers on databases, the proceedings contain important new material on these subjects. This book is a must for persons who want to keep themselves up to date in the field of OOP.

This monograph provides a detailed record of the ?GRUFF? research project. The goal of the GRUFF project is to develop techniques for robotic vision systems to recognize objects by reasoning about their intended function rather than matching to a pre-defined database of 2-D object appearances or 3-D object shapes. The contributions of this work are: a demonstration of the feasibility of the ?form and function? approach to reasoning about 3-D shapes; a demonstration of the concept of using a small number of knowledge primitives as component building blocks in creating a function-based definition of an object category; and an indexing mechanism to make processing for recognition more efficient without any substantial decrease in correctness of classification. Results are given for the analysis of over 500 3-D shape descriptions created with a solid modeling tool and over 200 shape descriptions extracted from real laser range finder images.

A unique multidisciplinary perspective on the problem of visual object categorization.

The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.