A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting focused objects in complex visual images
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive image compression using local pattern information
Pattern Recognition Letters
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic segmentation of focused objects from images with low depth of field
Pattern Recognition Letters
Multiclass object classification for real-time video surveillance systems
Pattern Recognition Letters
Variable lighting face recognition using discrete wavelet transform
Pattern Recognition Letters
A method for mixed states texture segmentation with simultaneous parameter estimation
Pattern Recognition Letters
Segmenting a low-depth-of-field image using morphological filters and region merging
IEEE Transactions on Image Processing
Unsupervized Video Segmentation With Low Depth of Field
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present an inherent model of the low depth-of-field (DOF) images, referred as the Amplitude Decomposition Model, which turns out to be useful for the detection and segmentation of focused objects in the low DOF images. By analyzing the low DOF image in frequency domain, the Amplitude Decomposition Model is firstly investigated, i.e., the amplitude spectrum of the low DOF image can be decomposed into the amplitude of its totally defocused version and the high-frequency difference amplitude of its focused regions. Based on this model, we propose a method for detecting focused objects. Using the detection result, we then utilize a thresholding method to segment the focused objects and employ the graph cut technique to refine the focused object boundary. Experimental results show that the proposed method can extract focused objects effectively and is comparable to the state-of-the-art methods.