Improvements of Object Detection Using Boosted Histograms

Keywords

Histogram, Artificial intelligence, AdaBoost, Computer science, Benchmark (surveying)

References

• Pattern ClassificationShigeo Abe — 2012
• Representing and Recognizing the Visual Appearance of Materials using Three-dimensional TextonsThomas Leung, Jitendra Malik — 2001
• A decision-theoretic generalization of on-line learning and an application to boostingYoav Freund, Robert E. Schapire — 1997
• Shape Matching and Object Recognition Using Shape ContextsSerge Belongie, Jitendra Malik, Jan Puzicha — 2002
• Recognition without Correspondence using Multidimensional Receptive Field HistogramsBernt Schiele, James L. Crowley — 2004
• Selection of scale-invariant parts for object class recognitionDorko, Schmid — 2003
• Learning object detection from a small number of examples: the importance of good featuresK. Levi, Yaakov Weiss — 2004
• Object recognition from local scale-invariant featuresDavid Lowe — 1999
• Integrating representative and discriminant models for object category detectionMario Fritz, Bastian Leibe, Barbara Caputo — 2005
• Integral histogram: a fast way to extract histograms in Cartesian spacesFatih Porikli — 2005
• A statistical method for 3D object detection applied to faces and carsHenry Schneiderman, Takeo Kanade — 2000
• Local features for object class recognitionKrystian Mikolajczyk, Bastian Leibe, Bernt Schiele — 2005
• Histograms of oriented gradients for human detectionNavneet Dalal, Bill Triggs — 2005
• Sharing features: efficient boosting procedures for multiclass object detectionAntonio Torralba, Kevin Murphy, William T. Freeman — 2004
• Color indexingMichael J. Swain, Dana H. Ballard — 1991
• The 2005 PASCAL Visual Object Classes ChallengeM. Everingham, Andrew Zisserman, Christopher K. I. Williams — 2005
• Pattern ClassificationKatherine L. Silversides — 2021
• A performance evaluation of local descriptorsKrystian Mikolajczyk, C. Schmid — 2005

Cited by

• People detection, tracking and re-identification through a video camera network. (Détection, suivi et ré-identification de personnes à travers un réseau de caméra vidéo)Malik Souded — 2013
• Scene Understanding: perception, multi-sensor fusion, spatio-temporal reasoning and activity recognition. (Interprétation de Scènes : perception, fusion multi-capteurs, raisonnement spatio-temporel et reconnaissance d'activités)François Brémond — 2007
• People Detection and Re-identification for Multi Surveillance CamerasEtienne Corvée, Sławomir Bąk, François Brémond — 2012
• Feature Construction Using Evolution-COnstructed Features For General Object RecognitionKirt Lillywhite — 2012
• The Texture-Transform : An Operator for Texture Detection and DiscriminationAlireza Tavakoli Targhi — 2009
• Improving Feature Level Likelihoods using Cloud FeaturesHeydar Maboudi Afkham, Stefan Carlsson, Josephine Sullivan — 2012
• Contribution à la détection et à la reconnaissance d'objets dans les imagesH. Harzallah — 2011
• Interactive Object Retrieval using Interpretable Visual Models. (Recherche Interactive d'Objets à l'Aide de Modèles Visuels Interprétables)Ahmed Rebaï — 2011
• Sparse representation-based human detection: a scale-embedded dictionary approachG. Krishna Vinay, Sk. Mohammadul Haque, R. Venkatesh Babu — 2015
• Kernel methods in computer vision: object localization, clustering, and taxonomy discoveryMatthew B. Blaschko — 2009
• Joint boosting of histogram like features for the generic recognition of object classes and subclassesDömötör László Molnár, Zoltán Szlávik — 2011
• Reading Street Signs using a Generic Structured Object Detection and Signature Recognition ApproachS. N. Parizi, Alireza Tavakoli Targhi, O. Aghazadeh — 2009
• Monocular visual scene understanding from mobile platformsChristian Wojek — 2010
• Learning Object Location Predictors with Boosting and Grammar-Guided Feature ExtractionDamian Eads, Edward Rosten, David P. Helmbold — 2009
• Modeling, representing and learning of visual categoriesMario Fritz — 2009
• Contributions to object detection and human action recognitionM. Jimenez, M. Jesus — 2011
• Conditional random fields for detection of visual object classesPaul Schnitzspan — 2010
• SISTEM VERIFIKASI TANDA TANGAN OFF-LINE BERDASAR CIRI HISTOGRAM OF ORIENTED GRADIENT (HOG) DAN HISTOGRAM OF CURVATURE (HoC)Agus Wahyu Widodo, Agus Harjoko — 2015
• FPGA Acceleration of RankBoost in Web Search EnginesNingyi Xu, Xiongfei Cai, Rui Gao — 2009
• A new framework for on-line object tracking based on SURFQuan Miao, Guijin Wang, Chenbo Shi — 2011

Related papers

• Efficient face detection method with eye region judgmentChun Fu Lin, Sheng‐Fuu Lin — 2013
• Face Detection Based on Adaboost Algorithm and OpenCVZhao Run-peng — 2012
• The research of face detection method based on adaboost algorithm and skin color segmentationChang Liu — 2017
• Face Detection Based on Adaboost and Parallel RealizationBo Song — 2010
• Face detection based on extended Haar-like featuresChangsaar Chai, Yulin Wang — 2010
• Occluded Face Detection Based on Adaboost TechnologyHua Wang, Xin Gu, Xiaoli Li — 2015
• Object class recognition using range of multiple computer vision algorithmsVisar Shehu, A. Dika — 2011
• Using a Multi-Faced Technique SPFACS Video Object Design Analysis of The AAM Algorithm Applies Smile DetectionByung‐Kwan Choi — 2015
• Object recognition using FPGA and TINY YOLOK. Saranya, S. Vijayashaarathi, C. Sarah Christel — 2023