From Single Image to List of Objects Based on Edge and Blob Detection

In this paper we present a new method for obtaining a list of interest objects from a single image. Our object extraction method works on two well known algorithms: the Canny edge detection method and the quadrilaterals detection. Our approach allows to select only the significant elements of the image. In addition, this method allows to filter out unnecessary key points in a simple way (for example obtained by the SIFT algorithm) from the background image. The effectiveness of the method is confirmed by experimental research.

[1]  Leszek Rutkowski,et al.  A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification , 1986 .

[2]  Krzysztof Cpalka,et al.  A New Method for Design and Reduction of Neuro-Fuzzy Classification Systems , 2009, IEEE Transactions on Neural Networks.

[3]  Rafal Grycuk,et al.  Content-Based Image Indexing by Data Clustering and Inverse Document Frequency , 2014, BDAS.

[4]  Marcin Zalasinski,et al.  Novel Algorithm for the On-Line Signature Verification , 2012, ICAISC.

[5]  Marcin Gabryel,et al.  Genetic Cost Optimization of the GI/M/1/N Finite-Buffer Queue with a Single Vacation Policy , 2013, ICAISC.

[6]  Beatriz Pérez-Sánchez,et al.  Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms , 2012, SOCO 2012.

[7]  Marcin Gabryel,et al.  Object Detection by Simple Fuzzy Classifiers Generated by Boosting , 2013, ICAISC.

[8]  L. Rutkowski.  Non-parametric learning algorithms in time-varying environments☆ , 1989 .

[9]  Leszek Rutkowski,et al.  Flexible Takagi Sugeno Neuro Fuzzy Structures for Nonlinear Approximation , 2005 .

[10]  Marcin Gabryel,et al.  Creating Learning Sets for Control Systems Using an Evolutionary Method , 2012, ICAISC.

[11]  Marcin Gabryel,et al.  On the Application of Orthogonal Series Density Estimation for Image Classification Based on Feature Description , 2013, KICSS.

[12]  Janusz T. Starczewski.  A Type-1 Approximation of Interval Type-2 FLS , 2009, WILF.

[13]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[14]  Krzysztof Cpalka,et al.  A New Method to Construct of Interpretable Models of Dynamic Systems , 2012, ICAISC.

[15]  Rafal Grycuk,et al.  Improved Digital Image Segmentation Based on Stereo Vision and Mean Shift Algorithm , 2013, PPAM.

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[18]  Kees Joost Batenburg,et al.  Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization , 2009, IEEE Transactions on Medical Imaging.

[19]  S. Lakshmi,et al.  IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT, 2010. A study of Edge Detection Techniques for Segmentation Computing Approaches , 2022 .

[20]  Dariusz Mrozek,et al.  Beyond Databases, Architectures, and Structures , 2014, Communications in Computer and Information Science.

[21]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing, 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I , 2010, International Conference on Artificial Intelligence and Soft Computing.

[22]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[23]  W. Greblicki,et al.  An orthogonal series estimate of time-varying regression , 1983 .

[24]  Robert Nowicki,et al.  Rough-Neuro-Fuzzy System with MICOG Defuzzification , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[25]  Witold Pedrycz,et al.  Fuzzy Logic and Applications , 2013, Lecture Notes in Computer Science.

[26]  M. Bazarganigilani.  Optimized image feature selection using pairwise classifiers , 2011 .

[27]  Leszek Rutkowski,et al.  Soft Techniques for Bayesian Classification , 2003 .

[28]  Piotr Dziwiñski,et al.  Hybrid State Variables - Fuzzy Logic Modelling of Nonlinear Objects , 2013, ICAISC.

[29]  Robert Nowicki,et al.  On classification with missing data using rough-neuro-fuzzy systems , 2010, Int. J. Appl. Math. Comput. Sci..

[30]  Noboru Ohnishi,et al.  Image segmentation and object extraction based on geometric features of regions , 1998, Electronic Imaging.

[31]  Lauren Barghout,et al.  Real-world scene perception and perceptual organization: Lessons from Computer Vision , 2013 .

[32]  Guillaume Damiand,et al.  Split-and-merge algorithms defined on topological maps for 3D image segmentation , 2003, Graph. Model..

[33]  Meng Joo Er,et al.  Online Speed Profile Generation for Industrial Machine Tool Based on Neuro-fuzzy Approach , 2010, ICAISC.