CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selection cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prio...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 34; H. 7; S. 1312 - 1328
Hauptverfasser:	Carreira, J., Sminchisescu, C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.07.2012 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial intelligence Categories Computer science; control theory; systems Constraints Detectors Exact sciences and technology figure-ground segmentation Hypotheses Image color analysis Image edge detection Image segmentation Information retrieval. Graph learning Mathematical models Object recognition Object segmentation Pattern recognition. Digital image processing. Computational geometry Segmentation Shape Studies Theoretical computing Image recognition Stimulus Pipeline processor Image processing Grid Labelling learning Ground truth Modeling Relevance Image segmentation figure-ground segmentation Graph cut Object oriented Bottom up method Overlay Regularity Image classification
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!