1.

Record Nr.

UNINA9910465084703321

Titolo

The field research survival guide [[electronic resource] /] / edited by Arlene Rubin Stiffman

Pubbl/distr/stampa

New York, : Oxford University Press, 2009

ISBN

0-19-972414-8

9786611998493

1-281-99849-4

Descrizione fisica

1 online resource (279 p.)

Altri autori (Persone)

StiffmanArlene Rubin <1941->

Disciplina

361.0072

Soggetti

Social service - Fieldwork

Psychology - Fieldwork

Psychiatry - Research - Fieldwork

Public health - Fieldwork

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Contents; Contributors; 1 Balancing Science and Services: The Challenges and Rewards of Field Research; 2 Developing Questions when the Perfect Instrument Is Not Available; 3 Hiring, Training, and Retaining Research Staff and Interviewers; 4 Managing the Data from Survey Development through Archiving; 5 Data Preparation and Data Standards: The Devil Is in the Details; 6 Cultural Sensitivity and Cultural Disparities: Ethical Dilemmas, Legal Issues, and IRB Requirements; 7 Creating Interdisciplinary Research Teams and Using Consultants

8 "Indigenist" Collaborative Research Efforts in Native American Communities9 The Worst of all Possible Program Evaluation Outcomes; 10 The Influence of Research on Policy and Practice: Lessons from Studies of Asset Building and Low-Income Families; 11 Disseminating Results and Sharing Data and Publications; Index; A; B; C; D; E; F; G; H; I; J; L; M; N; O; P; Q; R; S; T; V

Sommario/riassunto

1. Balancing Science and Services: The Challenges and Rewards of Field Research, Kimberly Eaton Hoagwood and Sarah McCue Horwitz  2. Developing Questions when the Perfect Instrument is Not Available,



Sarah McCue Horwitz and Kimberly Eaton Hoagwood  3. Hiring, Training, and Retaining Research Staff and Interviewers, Elizabeth Mayfield Arnold and Mary Jane Rotheram-Borus  4. Managing the Data from Survey Development through Archiving, Peter Dore and Arlene Rubin Stiffman  5. Data Preparation and Data Standards: The Devil is in the Details, Catherine M. Smith, Carolyn Breda, Tonya Simmons, Ana Re

2.

Record Nr.

UNINA9910299854403321

Autore

Spehr Jens

Titolo

On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities / / by Jens Spehr

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015

ISBN

3-319-11325-9

Edizione

[1st ed. 2015.]

Descrizione fisica

1 online resource (210 p.)

Collana

Studies in Systems, Decision and Control, , 2198-4182 ; ; 11

Disciplina

006.3

006.37

006.4

006.6

Soggetti

Robotics

Automation

Computational intelligence

Optical data processing

Pattern perception

Robotics and Automation

Computational Intelligence

Image Processing and Computer Vision

Pattern Recognition

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition --



Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.

Sommario/riassunto

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.