Dynamic Color Object Recognition Using Fuzzy Logic

Reyes, Napoleon H.; Dadios, Elmer P.

doi:10.20965/jaciii.2004.p0029

Cited by 24 publications

(17 citation statements)

References 4 publications

(7 reference statements)

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“…The actual size of the robot is 7.5 cubic centimeter [18]. Each robot is identified by a camera vision system through the two colored patches on the top part of its body [16].…”

Section: The Robot Soccer Identification and Collision Event Detectionmentioning

confidence: 99%

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Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

Dadios

Park

2009

International Journal of Computer Games Technology

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This paper presents a new algorithm for real time event detection using Finite State Machines with multiple Fuzzy Logic Probability Evaluators (FLPEs). A machine referee for a robot soccer game is developed and is used as the platform to test the proposed algorithm. A novel technique to detect collisions and other events in microrobot soccer game under inaccurate and insufficient information is presented. The robots' collision is used to determine goalkeeper charging and goal score events which are crucial for the machine referee's decisions. The Main State Machine (MSM) handles the schedule of event activation. The FLPE calculates the probabilities of the true occurrence of the events. Final decisions about the occurrences of events are evaluated and compared through threshold crisp probability values. The outputs of FLPEs can be combined to calculate the probability of an event composed of subevents. Using multiple fuzzy logic system, the FLPE utilizes minimal number of rules and can be tuned individually. Experimental results show the accuracy and robustness of the proposed algorithm.

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“…The actual size of the robot is 7.5 cubic centimeter [18]. Each robot is identified by a camera vision system through the two colored patches on the top part of its body [16].…”

Section: The Robot Soccer Identification and Collision Event Detectionmentioning

confidence: 99%

“…The vision system provides the frame information like the coordinates and direction of the robots [16][17][18]. The decision making system will detect events like pushing, hitting, stalemate, ball shot, and others by inspecting the frame information.…”

Section: The Robot Soccer Game Event Referee Systemmentioning

confidence: 99%

Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

Dadios

Park

2009

International Journal of Computer Games Technology

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“…Vision System for Autonomous Golf Playing Micro Robot Figure 3 shows the vision system algorithm to identify the objects captured by the camera on a golf playing field. The algorithm uses color object recognition [13], hence, the robot has 2 color patches, the ball has orange color, and the hole has pink color. Initially, it undergoes a color space transformation from RGB to RG-chromaticity space [14].…”

Section: Processes Involved In Designning the Controller Of The mentioning

confidence: 99%

“…The centroid of the target object provides the actual object coordinates inside the playing field. set, the sequential connected component labeling algorithm will be used [13]. The flags were also used to trigger which size filtering technique should be implemented.…”

Section: Processes Involved In Designning the Controller Of The mentioning

confidence: 99%

The Autonomous Golf Playing Micro Robot: With Global Vision and Fuzzy Logic Controller

Jabson

Leong

Licarte

et al. 2008

International Journal on Smart Sensing and Intelligent Systems

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-This paper presents design concepts in developing an Autonomous Golf Playing Micro Robot. The robot moves autonomously using 2 DC motors controlled by a remote PC using fuzzy logic system. A servomotor is used for the motion of the putter in hitting the golf ball. A microcontroller is used to process the information sent by the computer through RF communication and drive the robot motors for appropriate motions. The robot plays golf` in a defined environment and avoids obstacles.A real golf ball with orange color is use during the game. The system utilizes global camera vision that serves as the eyes of the robot to know its dynamic environment for navigation. The vision system developed detects the position of the robot, golf hole, golf boundaries, and the golf ball within the playing field on real time using color object recognition algorithm. A modified golf tournament between autonomous robot golf player and man operated robot golf player was conducted. Results shows the accuracy and robustness of the autonomous robot developed in performing its tasks.

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“…In addition, confounding effects due to the spectral reflectance characteristic of the object, the spectral power distribution of the illuminant [1] and sensitivity of the camera make the colour classification task very difficult [2]. On the contrary, our model, the human visual system is able to recognise the colours of objects irrespective of the light used to illuminate them.…”

Section: Introductionmentioning

confidence: 99%

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Shin

Reyes

2009

Memetic Comp.

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This paper presents a complete Fuzzy-Geneticbased self-calibrating illumination intensity-invariant colour classification system. Previously, we have developed a novel fuzzy colour processing technique called Fuzzy Colour Contrast Fusion (FCCF) that selectively and adaptively corrects colours depicting target colour objects. FCCF has been proven to compensate for the effects of spatially varying illumination intensities in the scene, in various colour spaces. However, FCCF requires a huge set of parameters that is extremely tedious to calibrate by hand. To address these problems, we present a system that combines FCCF with a Heuristic-Assisted Genetic Algorithm (HAGA). FCCF-HAGA fully automates the fine-tuning of all colour descriptors, with significantly improved colour classification accuracy. Furthermore, we have reduced FCCF's storage space requirements by processing colour channels selectively at varying colour depths. This is accomplished by combining a Variable Colour Depth (VCD) algorithm with FCCF that searches for the most effective colour depth for each colour channel. Our results show that for all cases, the FCCF-HAGA-VCD combination improves pie-slice colour classification. For six different target colours, under varying illuminations, the hybrid algorithm was able to yield 17.63% higher overall colour classification accuracy as compared to the pure fuzzy approach. Furthermore, it was able to reduce LUT storage space requirements by 78.06%, as compared to the full-colour depth LUT.

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Dynamic Color Object Recognition Using Fuzzy Logic

Cited by 24 publications

References 4 publications

Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

The Autonomous Golf Playing Micro Robot: With Global Vision and Fuzzy Logic Controller

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

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