02177nam a2200277 a 4500001001400000003000700014005001700021008004100038020002200079039010500101040000800206090003200214100002900246245008600275246007700361260003400438300005900472502010800531520086500639650001101504650001701515952012601532952013601658999002301794999008201817vtls000025682KUKTEM20251114204343.0080325t2007    my     f m    000 0 eng|d  aTHE0006077(Local) 9a201905151421baidac201107132002dVLOADc200908141420dVLOADc200908141352dVLOADy200803251043zkam  aUMP  aTJ211.3 .B35 2007 rs Thesis0 aMohd Baihaqi Mohd Adenan12aVision module for smart ping pong collector robot /cMohd Baihaqi Bin Mohd Adenan3 aVision module for smart ping pong collector roboth[electronic resource]  aKuantan, Pahang :bUMP,c2007  a42 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Electrical Engineering (Power systems)) -- Universiti Malaysia Pahang - 2007  aThe purpose of this project is to design and develop a pattern recognition system with using Artificial Neural Network (ANN) that can recognize the type of image based on the features extracted from the choose image. This system which can fully recognizing the types of the data had been add in the data storage or called as training data. The Graphic User Interface in Neural Network toolbox is used. This is the alternative way to change the common usage of the MATLAB which are use the command insert at command window. From this kind of system, we just need to insert the features data or training data. The recognition done after we insert the test data. The system will recognize whether the output is match with the training data. Then output will produce a kind of graph that describes the feature of the data which is same as the training data.-Author 0aMATLAB 0aRobot vision  00104071a20000b20000d2019-09-04l0oTJ211.3 .B35 2007 rs Thesisp0000029282r2019-09-04 00:00:00t1w2019-09-04yPSM  00104071a20000b20000d2019-09-04l0oCD 2610 | TJ211.3 .B35 2007 rs Thesisp0000029283r2019-09-04 00:00:00t1w2019-09-04yPSM  aVIRTUA40c569d575  aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992