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Proposed scheme for automatic fabric weave type identification.
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... the analysis, the weft and warp directions were manually aligned before taking the snapshot. The complete steps of the proposed scheme for image acquisition, preprocessing and analysis of weave type identification are shown in Figure 2. Eng. ...
Context 2
... the analysis, the weft and warp directions were manually aligned before taking the snapshot. The complete steps of the proposed scheme for image acquisition, preprocessing and analysis of weave type identification are shown in Figure 2. Figure 2. Proposed scheme for automatic fabric weave type identification. ...
Context 3
... the analysis, the weft and warp directions were manually aligned before taking the snapshot. The complete steps of the proposed scheme for image acquisition, preprocessing and analysis of weave type identification are shown in Figure 2. Figure 2. Proposed scheme for automatic fabric weave type identification. ...
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... Penelitian selanjutnya yang dilakukan oleh Qureshi [13] menggunakan metode deteksi kontur untuk melakukan proses pengenalan citra kain tenun dengan beberapa proses pengolahan awal yaitu Penajaman kontras, perubahan menjadi grayscale, gaussian low pass filter. Namun, penelitian tersebut hanya mampu meraih angka akurasi 90%. ...
... Peneliti Metode Hasil Tamara [10] CNN 73% Wicaksana [11] CNN 76% Rizki [12] Faster R-CNN 82.14% Qureshi [13] Resnet50 90% ...
Pengenalan kain tenun melalui pengolahan citra memiliki nilai tinggi dalam melestarikan warisan budaya dan membantu dalam identifikasi dan klasifikasi produk tekstil tradisional. Untuk tujuan ini, penelitian ini mengusulkan pendekatan yang memanfaatkan pohon keputusan atau Decision Tree untuk mengenali gambar kain tenun khas Nusa Tenggara Timur. Efektivitas dalam klasifikasi data dimensi tinggi menjadikannya alat yang ideal untuk memodelkan pola unik yang ada dalam gambar kain tenun. Data set penelitian ini terdiri dari berbagai jenis kain tenun Nusa Tenggara Timur, dan hasil eksperimen menunjukkan keakuratan pendekatan yang kami usulkan dalam mengenali kain-kain ini, mencapai tingkat keberhasilan yang menjanjikan dalam klasifikasi motif dan pola yang kompleks. Temuan ini merupakan kontribusi yang signifikan terhadap pengembangan sistem otomatis untuk mengidentifikasi dan mendokumentasikan kain tenun tradisional, yang pada gilirannya dapat mendukung inisiatif pelestarian budaya dan pertumbuhan industri lokal di Nusa Tenggara Timur. The recognition of woven fabrics through image processing has a high value in preserving cultural heritage and assisting in the identification and classification of traditional textile products. To this end, this study proposes an approach that utilizes a Decision Tree (DT) to recognize images of woven fabrics typical of East Nusa Tenggara. DT's effectiveness in high-dimensional data classification makes it an ideal tool for modeling unique patterns present in woven fabric drawings. The dataset consisted of different types of East Nusa Tenggara woven fabrics, and experimental results showed the accuracy of the approach in recognizing these fabrics, achieving promising success rates in the classification of complex motifs and patterns. These findings represent a significant contribution to the development of automated systems for identifying and documenting traditional woven fabrics, which in turn can support cultural preservation initiatives and local industrial growth in East Nusa Tenggara.
