Sistem Inspeksi Label Produk Menggunakan Metode Golden Template Comparison dan Konveyor Pemilah

Inspeksi label produk menjadi bagian penting dalam pengawasan fabrikasi dan kendali kualitas hasil produksi di industri. Beberapa industri masih menerapkan pemeriksaan label produk secara visual-manual oleh operator. Cara ini memiliki keterbatasan dan cenderung tidak cukup efektif. Penelitian ini mengembangkan sistem inspeksi kualitas pelabelan produk secara otomatis menggunakan penggabungan metode golden template comparison berdasarkan analisis pixel matching dan konveyor pemilah. Perangkat konveyor pemilah mampu memindahkan produk ke proses deteksi dua sisi label dan memilah produk secara otomatis berdasarkan hasil inspeksi produk. Penggunaan golden template comparison ini efektif membandingkan intensitas pixel dari objek yang diperiksa dengan golden template yang sudah ditentukan. Hasil penelitian menunjukkan sistem ini berhasil menganalisa produk yang pass dan fail berdasarkan perhitungan total defect area kedua sisi front dan back pada label produk sesuai standar acuan klasifikasi cacat yang ditentukan.

A New Method on Super Pixel Reducing Stereo Matching Time of Integrated Imaging

Stereo vision and 3D reconstruction technologies are increasingly concerned in many fields. Stereo matching algorithm is the core of stereo vision and also a technical difficulty. A novel method based on super pixels is mentioned in this paper to reduce the calculating amount and the time. Stereo images from University of Tsukuba are used to test our method. The proposed method spends only 1% of the time spent by the conventional method. Through a two-step super-pixel matching optimization, it takes 6.72 s to match a picture, which is 12.96% of the pre-optimization.

Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.