Toward category-level object recognition / Jean Ponce [and others] (eds.).
Contributor(s): Ponce, JeanMaterial type: TextSeries: SerienbezeichnungLNCS sublibrary: ; Lecture notes in computer science: 4170.Publisher: Berlin ; New York : Springer, ©2006Description: 1 online resource (xi, 618 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9783540687955; 3540687955; 9783540687948; 3540687947Subject(s): Computer vision -- Congresses | Pattern recognition systems -- Congresses | Image processing -- Digital techniques -- Congresses | Object-oriented methods (Computer science) -- Congresses | Image processing -- Digital techniques | Object-oriented methods (Computer science) | Computer vision | Pattern recognition systems | Informatique | Computer vision | Image processing -- Digital techniques | Object-oriented methods (Computer science) | Pattern recognition systems | Objekt Kategorie | Dreidimensionale Bildverarbeitung | Objekterkennung | Merkmalsextraktion | Maschinelles SehenGenre/Form: Electronic books. | Conference papers and proceedings. | Aufsatzsammlung. | Online-Publikation. Additional physical formats: Print version:: Toward category-level object recognition.DDC classification: 006.3/7 LOC classification: TA1634 | .T69 2006ebOther classification: TP391. 4-532 | SS 4800 | ST 330 | DAT 770f | DAT 760f Online resources: Click here to access online
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Includes bibliographical references and index.
pt. 1. Introduction -- pt. 2. Recognition of specific objects -- pt. 3. Recognition of object categories -- pt. 4. Recognition of object categories with geometric relations -- pt. 5. Joint recognition and segmentation.
Print version record.
Although research in computer vision for recognizing 3D objects in photographs dates back to the 1960s, progress was relatively slow until the turn of the millennium, and only now do we see the emergence of effective techniques for recognizing object categories with different appearances under large variations in the observation conditions. Tremendous progress has been achieved in the past five years, thanks largely to the integration of new data representations, such as invariant semi-local features, developed in the computer vision community with the effective models of data distribution and classification procedures developed in the statistical machine-learning community. 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 main goals of these two workshops were to promote the creation of an international object recognition community, with common datasets and evaluation procedures, to map the state of the art and identify the main open problems and opportunities for synergistic research, and to articulate the industrial and societal needs and opportunities for object recognition research worldwide. 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.