Automatic Defect Detection and Classification Technique from Image Processing

Image processing is one of the most increasing areas in computer science. As technology advances, the analog imaging is switched to the digital system. Every day, we capture huge amount of images which are very difficult to maintain manually within a certain period of time. So the concept and application of the digital imaging grows rapidly. Digital image processing[7] is used to extract various features from images. This is done by computers automatically without or with little human intervention. One of the most important operations on digital image[2] is to identify and classify various kinds of defects. Thus to detect the defects from any image some methods are developed. In this paper a defect detection method for ceramic tiles is proposed. The proposed method is tested for images with resolution 1920×1080 pixels. The method has tested only for defects such as blobs and cracks

Fuzzy Neural Network Capability Studies in Land Cover Perpiksel Based Classification Using Landsat7 ETM+

Variasi kondisi penutup lahan di permukaan bumi menyebabkan sistem klasifikasi dengan pendekatan parametrik (maximum likelihood) cenderung kurang baik dalam mengklasifikasikan penutup lahan pada subkelas yang lebih detil jika dibandingkan dengan pendekatan non-parametrik. Sementara, pendekatan non-parametrik dengan sistem hard classifier secara teori juga tidak mampu memberikan hasil yang lebih baik pada batas samar tiap piksel kelas penutup lahan jika dibandingkan dengan sistem soft classifier. Peneltian ini bertujuan untuk mengetahui akurasi hasil klasifikasi perpiksel penutup lahan menggunakan metode Fuzzy neural network (FNN). Penelitian ini menggunakan citra Landsat 7 ETM+. Citra ini diklasifikasikan dengan menggunakan 3 sistem klasifikasi yakni maximum likelihood, artificial neural network (non parametrik), dan FNN (fuzzy logic - non parametrik). Hasil penelitian menunjukkan FNN mampu memberikan akurasi yang jauh lebih baik dibandingkan dengan 2 sistem klasifikasi lainnya. FNN dengan dan tanpa data gabungan masing-masing memberikan akurasi sebesar 78.87% dan 80.41%. Sementara itu, sistem klasifikasi lainnya memberikan akurasi dibawah 65%.Kata Kunci; pendekatan parametrik, artificial neural network, fuzzy neural network, penutup lahan, akuras

Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions

In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous