Color-based Object Categorization Model Using Fuzzy HSV Inference System

Color is one of the most identifiable properties of objects and wide range of colors need to be detected. Images captured in RGB color space are hard to extract more than three colors from them due to their cubic shape of merging luminance, saturation, and color spectrum. This paper presents a color-based Object Categorization model that utilizes the HSV color space. The process starts by converting the RGB space into three main components, Hue (wavelength of colors), Saturation (purity of colors), and Value (grayness level or lightness), Unfortunately, Hue causes some sort of fogginess when color classification is applied, due to the gradient between each color. For that, the hue fuzzy sets are used which categorize colors in images. They are used to identify nine deferent object's color and categorize them into the nine common pure colors. The categorization process is then carried out by a PUMA 560 robot in nine different trajectories based on the colors extracted by the fuzzy sets. In order to illuminate bright and dark objects and putting them into the tenth trajectory of the unwanted objects, we modified the saturation and value as fuzzy sets. The system is simulated, tested and proved its effectiveness in detecting colored objects due to their gradient and variations for environments with normal or highly structured light settings, using medium or high-resolution camera.

[1]  Herman Akdag,et al.  Color Image Profiling Using Fuzzy Sets , 2005 .

[2]  H S HUHN Vision in industry. , 1947, Transactions - American Academy of Ophthalmology and Otolaryngology. American Academy of Ophthalmology and Otolaryngology.

[3]  Atsushi Inoue,et al.  An Overview of FHSI: Fuzzy Color Space for Image Retrieval and Modeling Aesthetic Perceptions , 2018, 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR).

[4]  Isabelle Bloch,et al.  Fuzzy sets for image processing and understanding , 2015, Fuzzy Sets Syst..

[5]  Raimondo Schettini,et al.  A survey on methods for colour image indexing and retrieval in image databases , 2002 .

[6]  Atsushi Inoue,et al.  Perceptual color space: Motivations, methodology, applications , 2014, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS).

[7]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[8]  Professor Bruce G. Batchelor,et al.  Intelligent Vision Systems for Industry , 1997, Springer London.

[9]  Bo Du,et al.  Research on Segmentation Methods of Weed and Soil Background Under HSI Color Model , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[10]  M. N. Uddin,et al.  Color Sorting Robotic Arm , 2019, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).

[11]  Atsushi Inoue,et al.  FHSI: Toward More Human-Consistent Color Representation , 2016, J. Adv. Comput. Intell. Intell. Informatics.

[12]  Chen Rongbao,et al.  License plate location method based on modified HSI model of color image , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[13]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[14]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[15]  Hasan U. Zaman,et al.  A Novel Design of a Robotic Object Sorter Based on Color Differences using Image Processing Techniques , 2018, 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2).

[16]  Rusman Rusyadi,et al.  A MODEL VISION OF SORTING SYSTEM APPLICATION USING ROBOTIC MANIPULATOR , 2010 .