Search results for "Viola–Jones object detection framework"

showing 4 items of 4 documents

Object Recognition and Modeling Using SIFT Features

2013

In this paper we present a technique for object recognition and modelling based on local image features matching. Given a complete set of views of an object the goal of our technique is the recognition of the same object in an image of a cluttered environment containing the object and an estimate of its pose. The method is based on visual modeling of objects from a multi-view representation of the object to recognize. The first step consists of creating object model, selecting a subset of the available views using SIFT descriptors to evaluate image similarity and relevance. The selected views are then assumed as the model of the object and we show that they can effectively be used to visual…

Object RecognitionSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSIFT.business.industryComputer science3D single-object recognitionObject Recognition; Pose Estimation; Object Model; SIFT.ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognition3D pose estimationObject (computer science)Object-oriented designPose EstimationHaar-like featuresObject modelViola–Jones object detection frameworkComputer visionArtificial intelligencebusinessPoseObject Model
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The iterative object symmetry transform

2005

This paper introduces a new operator named the Iterated Object Transform that is computed by combining the Object Symmetry Transform with the morphological operator erosion. This new operator has been applied on both binary and gray levels images showing the ability to grasp the internal structure of a digital object. We present some experiments on real images in face analysis.

business.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONObject (computer science)Erosion (morphology)Object detectionObject-class detectionsymbols.namesakeOperator (computer programming)Fourier transformsymbolsComputer visionViola–Jones object detection frameworkArtificial intelligenceSymmetry (geometry)businessMathematics2004 International Conference on Image Processing, 2004. ICIP '04.
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Views selection for SIFT based object modeling and recognition

2016

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, …

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 intelligencebusiness
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Midground Object Detection in Real World Video Scenes,

2007

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScene statisticsObject (computer science)Object detectionObject-class detectionComputational efficiencyComputer networksSalientVideo trackingHuman visual system modelComputer visionViola–Jones object detection frameworkArtificial intelligencebusiness
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