Detection of Important Features from Images Using Heuristic Approach

Digital systems offer high quality images, for which information is encoded with precision. Pixels represent the features of objects, therefore we can use this information to detect purposes. In this article we present our research on methodology based on a heuristic approach. A model of bio inspired algorithm was used to search between the pixels and evaluate which of them are representing important components of the objects. Therefore this methodology serves as detection model to find the features of interest. Presented research results show that the developed approach show high potential and proposed methodology makes the search efficient.

[1]  Damodar Reddy Edla,et al.  SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification , 2017, Comput. Biol. Medicine.

[2]  Kagan Tumer,et al.  Fitness function shaping in multiagent cooperative coevolutionary algorithms , 2017, Autonomous Agents and Multi-Agent Systems.

[3]  Leida Li,et al.  Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation , 2017, IEEE Access.

[4]  Jacek Mandziuk,et al.  Neuro-evolutionary system for FOREX trading , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[5]  Piotr Artiemjew,et al.  A New Classifier Based on the Dual Indiscernibility Matrix , 2016, ICIST.

[6]  Marcin Korytkowski,et al.  Fast image classification by boosting fuzzy classifiers , 2016, Inf. Sci..

[7]  Maciej Swiechowski,et al.  Fast interpreter for logical reasoning in general game playing , 2016, J. Log. Comput..

[8]  Milan Nos,et al.  Customizing host IDE for non-programming users of pure embedded DSLs , 2017 .

[9]  Rutuparna Panda,et al.  Edge magnitude based multilevel thresholding using Cuckoo search technique , 2013, Expert Syst. Appl..

[10]  LinLin Shen,et al.  Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person , 2017, Pattern Recognit..

[11]  David G. Lowe,et al.  Probabilistic Models of Appearance for 3-D Object Recognition , 2000, International Journal of Computer Vision.

[12]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[13]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[14]  Carsten Witt,et al.  MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions , 2014, Algorithmica.

[15]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[16]  Andrzej Stateczny,et al.  Clustering Bathymetric Data for Electronic Navigational Charts , 2016 .

[17]  Daniel Ebânca,et al.  Multimedia data for efficient detection of visual objects , 2017, IMCOM.

[18]  Jean-François Brethé,et al.  Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle , 2017, Adv. Robotics.

[19]  Punam Bedi,et al.  Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm , 2014, Expert Syst. Appl..

[20]  Rafal Grycuk,et al.  Content-based image retrieval optimization by differential evolution , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[21]  Anna Fabijanska,et al.  A novel approach for quantification of time-intensity curves in a DCE-MRI image series with an application to prostate cancer , 2016, Comput. Biol. Medicine.

[22]  Jacek Mandziuk,et al.  A memetic approach to vehicle routing problem with dynamic requests , 2016, Appl. Soft Comput..

[23]  Marcin Gabryel The Bag-of-Features Algorithm for Practical Applications Using the MySQL Database , 2016, ICAISC.

[24]  Marta Wlodarczyk-Sielicka Importance of Neighborhood Parameters During Clustering of Bathymetric Data Using Neural Network , 2016, ICIST.

[25]  Weisi Lin,et al.  Learning ECOC Code Matrix for Multiclass Classification with Application to Glaucoma Diagnosis , 2016, Journal of Medical Systems.

[26]  Slawomir Koziel,et al.  Computational Optimization, Methods and Algorithms , 2016, Computational Optimization, Methods and Algorithms.

[27]  Olga Sourina,et al.  CogniMeter: EEG-Based Brain States Monitoring , 2016, Trans. Comput. Sci..

[28]  Zbigniew Marszalek Novel Recursive Fast Sort Algorithm , 2016, ICIST.

[29]  Witold Kazimierski,et al.  Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters , 2016 .

[30]  Giacomo Capizzi,et al.  Toward adaptive heuristic video frames capturing and correction in real-time , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[31]  Gurjit Singh Walia,et al.  Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search , 2014, Expert Syst. Appl..

[32]  R. Nelson,et al.  Large-scale tests of a keyed, appearance-based 3-D object recognition system , 1998, Vision Research.

[33]  Matús Sulír,et al.  Language composition using source code annotations , 2016, Comput. Sci. Inf. Syst..

[34]  Anton van den Hengel,et al.  Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition , 2015, Pattern Recognit..

[35]  James J. Little,et al.  Global localization using distinctive visual features , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[36]  Yang Tao,et al.  DUAL-CAMERA NIR/MIR IMAGING FOR STEM-END/CALYX IDENTIFICATION IN APPLE DEFECT SORTING , 2000 .

[37]  Rafal Grycuk,et al.  Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database , 2015, ICAISC.