A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of binary character/graphics images from grayscale document images
CVGIP: Graphical Models and Image Processing
CVGIP: Graphical Models and Image Processing
Binarization and multithresholding of document images using connectivity
CVGIP: Graphical Models and Image Processing
A System of Image Analysis Based on a Pretopological Approach
Intelligent Autonomous Systems, An International Conference
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Extraction of signatures from check background based on a filiformity criterion
IEEE Transactions on Image Processing
Learning and modeling biosignatures from tissue images
Computers in Biology and Medicine
Weak inclusions and digital spaces
Pattern Recognition Letters
General Adaptive Neighborhood-Based Pretopological Image Filtering
Journal of Mathematical Imaging and Vision
A new algorithm to extract the lines and edges through orthogonal projections
Digital Signal Processing
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We present an approach based on a pretopological formalism that allows the mathematical modeling of image segmentation by region growing. The choice of pretopology is motivated by the fact that it has less axioms than the topology which facilitates its adaptation to discrete spaces and in particularly image processing. In our approach, the pretopological adherency function associated to the pretopological structure is defined by a criterion of homogeneity. We apply our approach to the extraction of handwritten information on check background with images of scene and to edge detection. The aggregation from chosen initial germ ends to a closed part of the image composed by a stroke (a line of handwriting) or a line of contours. The evaluation, undertaken on 60 checks with various background images and different images, gives 92% of good results for the extracted handwriting with a complete elimination of the background and good results for edge detection.