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Satu teknik baru dicadangkan untuk mengkelaskan kerosakan yang boleh terjadi pada PCB menggunakan paradigma rangkaian neural. Algoritma untuk membahagi–bahagikan imej menjadi corak primitif, melingkupi corak primitif berkenaan, penandaan corak, normalisasi corak, dan pengkelasan telah dibangunkan berdasarkan pemprosesan imej morfologi penduaan dan rangkaian neural Learning Vector Quantization (LVQ). Ribuan corak rosak telah digunakan untuk tujuan latihan, dan rangkaian neural diuji untuk menilai prestasinya. Satu imej PCB yang rosak digunakan untuk memastikan teknik yang dicadangkan berfungsi. Kata kunci: PCB, pengkelasan kerosakan, pemprosesan imej morfologi, LVQ A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. Thousands of defective patterns have been used for training, and the neural network is tested for evaluating its performance. A defective PCB image is used to ensure the function of the proposed technique. Key words: PCB, defects classification, morphological image processing, LVQ

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

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