DocumentCode :
702709
Title :
3-D image analysis using MapReduce
Author :
Patil, Jyoti ; Mane, Sunayana
Author_Institution :
Dept. of Inf. Technol., JSPM´s Jayawantrao Sawant Coll. of Eng., Pune, India
fYear :
2015
fDate :
8-10 Jan. 2015
Firstpage :
1
Lastpage :
5
Abstract :
With the use of Internet more and more information can be searched from the web. Lots of 3D data is generated by satellite and in medical field due to advanced 3D camera capturing techniques. In last few decades 3D image analysis became essential in the field of Computer-Aided Design (CAD), medical imaging and entertainment.3D models leads to the urgent requirement of effective and efficient 3D model analysis in real-time. This process demands high computation time, storage capacity and network bandwidth. The process of extracting features from large 3D/4D images and analyzing them with different machine learning algorithm is really a challenging task. In this paper, we are proposing use of Hadoop´s Map-Reduce technique for analyzing large scale images. Map-Reduce is used in distributed processing for optimizing different tasks.
Keywords :
data handling; feature extraction; image processing; learning (artificial intelligence); parallel processing; solid modelling; 3D camera capturing techniques; 3D data generation; 3D image analysis; 3D model analysis; CAD; Hadoop MapReduce technique; Internet; World Wide Web; computer-aided design; distributed processing; entertainment; feature extraction; machine learning algorithm; medical field; medical imaging; satellite; Biomedical imaging; Feature extraction; Histograms; Image analysis; Indexing; Three-dimensional displays; Visualization; 3D image analysis; Big Data; Hadoop; Map-Reduce; Medical Images; Support Vector Machine; Texture Analysis etc.;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing (ICPC), 2015 International Conference on
Conference_Location :
Pune
Type :
conf
DOI :
10.1109/PERVASIVE.2015.7087069
Filename :
7087069
Link To Document :
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