6533b835fe1ef96bd129ff06
RESEARCH PRODUCT
Views selection for SIFT based object modeling and recognition
Marco La CasciaLuca GrecoAlessandro Brunosubject
Similarity (geometry)Computer science3D single-object recognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONLearning objectScale-invariant feature transform02 engineering and technologySIFT0202 electrical engineering electronic engineering information engineeringMedia TechnologyComputer vision060201 languages & linguisticsObject RecognitionSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryFeature matchingCognitive neuroscience of visual object recognitionPattern recognition06 humanities and the artsObject (computer science)Object Modeling0602 languages and literatureSignal ProcessingObject model020201 artificial intelligence & image processingViola–Jones object detection frameworkArtificial intelligencebusinessdescription
In this paper we focus on automatically learning object models in the framework of keypoint based object recognition. The proposed method uses a collection of views of the objects to build the model. For each object the collection is composed of N×M views obtained rotating the object around its vertical and horizontal axis. As keypoint based object recognition using a complete set of views is computationally expensive, we focused on the definition of a selection method that creates, for each object, a subset of the initial views that visually summarize the characteristics of the object and should be suited for recognition. We select the views by determining maxima and minima of a function, based on the number of SIFT descriptors able to evaluate views similarity and relevance. Experimental results for recognition on a publicly available dataset are reported.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-07-01 |