Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 6; pp. 1080 - 1085
• Main Authors: Kovacs, L., Sziranyi, T.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.06.2007; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Blinds; Cameras; Classification; Computer science; control theory; systems; Computer Simulation; Data mining; Deconvolution; Exact sciences and technology; feature representation; Focusing; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image retrieval; Image segmentation; Indexing; indexing methods; Information Storage and Retrieval - methods; Information systems. Data bases; Intelligence; Layout; Memory organisation. Data processing; Models, Statistical; Optical distortion; Optical noise; Pattern analysis; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Reproducibility of Results; Sensitivity and Specificity; sharpening and deblurring; Software; Transform methods; video retrieval; sharpening and deblurring; feature representation; Image retrieval; Noise reduction; Blind deconvolution; Blurred image; Interest region; indexing methods; video retrieval; Classification; Pattern analysis; Artificial intelligence; Transform methods; Indexing; Signal to noise ratio
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2007.1079

We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing