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Probabilistic Relaxation Labeling: A Short Survey on Object Recognition

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Abbas Zohrevand

Abbas Zohrevand. Probabilistic Relaxation Labeling: A Short Survey on Object Recognition. International Journal of Computer Applications 181(38):40-44, January 2019. BibTeX

	author = {Abbas Zohrevand},
	title = {Probabilistic Relaxation Labeling: A Short Survey on Object Recognition},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2019},
	volume = {181},
	number = {38},
	month = {Jan},
	year = {2019},
	issn = {0975-8887},
	pages = {40-44},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2019918387},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Object recognition problem can be defined as classifying input object(s) to number of predefined classes. Object recognition is one of the most important sections in computer vision. While this filed has been studied from long time ago, but it still suffers from several challenges such as: occlusion, rotation, distortion illumination, and scaling. The conventional object recognition system has two phases. Firstly: extraction of the most important (informatics or key pints) parts from object image (scene image) and predefined class image (model image), secondly matching between object and model. The Probabilistic Relaxation Labeling (PRL) is one of the popular probabilistic approaches in matching among model and scene. In this paper we review two phase and report the most important works based PRL.


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Object recognition, Probabilistic Relaxation Labeling, image descriptor, model image, scene image.