Penerapan Algoritme Segmentasi Mean Shift Dan Pemilihan Fitur Dasar Warna Pada Sistem Temu Kembali Citra Berbasis ISI

Sistem Temu Kembali Citra Berbasis lsi adalah sistem yang mengorganisir data citra hasil pengenalan objek, mengenali data citra baru dan mampu melakukan pencarian data citra berdasarkan objek dari citra da/am jumlah citra besar dan variasi gambar yang beragam. Salah satu implementasi penggunaan Sistem Temu Kembali Citra Berbasis lsi adalah pencarian koleksi barang di museum. User dapat mempero/eh informasi gambar tentang barang-barang koleksi museum hanya dengan memasukkan nama objek dari barang koleksi. Pada Tugas Akhir ini akan difokuskan pertama pada pembuatan Sistem Temu Kembali Citra Berbasis lsi dengan tiga tahapan. Pada tahapan pertama pelatihan data, data citra yang te/ah diberi label/keyword berisikan nama-nama objek pada citra disegmentasi menggunakan algoritme Mean Shift untuk menghasilkan region-region. Untuk menghasilkan vektor fitur citra maka dilakukan ekstraksi fitur dasar warna pada hasil segmentasi, menge/ompokkanlclustering region ke dalam kelas-ke/aslblob-b/ob dengan menggunakan algoritme K-Means berdasarkan vektor fitur, memprediksi keterhubungan antara blob dengan word dengan menggunakan algoritme Expectation Maximization. Tahap kedua, Pengenalan Data Citra baru di/akukan dengan menggunakan algoritme Nearest-Neighbour berdasarkan kedekatan vektor fitur data citra baru dengan data citra hasil pelatihan. Pada tahap ketiga dilakukan Pencarian Data Citra berdasarkan data input user berupa teks yang mewakili nama objek yang dicari dari data citra yang telah dihasi/kan pada tahapan pelatihan data. Fokus kedua yaitu ana/isis kinerja Sistem Temu Kembali Citra Berbasis lsi hasil dari pemilihanfitur dasar warna dalam domain (L*u*v, RGB, L *a*b, L *a*b dan CrCb), penggunaan algoritme mean shift pada tahapan awallprepocessing. Berdasarkan hasil uji coba, penentuan jumlah k/aster yang lebih banyak terbukti meningkatkan kinerja Sistem Temu Kembali Citra Berbasis lsi serta pemilihan fitur dasar warna domain L *u*v /ebih memiliki nilai kinerja yang lebih tinggi daripada domain warna yang lain (L *u*v, RGB, L *a*b, L *a*b, CrCb, dan kombinasinya

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

Texture-boundary detection in real-time

Boundary detection is an essential first-step for many computer vision applications. In practice, boundary detection is difficult because most images contain texture. Normally, texture-boundary detectors are complex, and so cannot run in real-time. On the other hand, the few texture boundary detectors that do run in real-time leave much to be desired in terms of quality. This thesis proposes two real-time texture-boundary detectors – the Variance Ridge Detector and the Texton Ridge Detector – both of which can detect high-quality texture-boundaries in real-time. The Variance Ridge Detector is able to run at 47 frames per second on 320 by 240 images, while scoring an F-measure of 0.62 (out of a theoretical maximum of 0.79) on the Berkeley segmentation dataset. The Texton Ridge Detector runs at 10 frames per second but produces slightly better results, with an F-measure score of 0.63. These objective measurements show that the two proposed texture-boundary detectors outperform all other texture-boundary detectors on either quality or speed. As boundary detection is so widely-used, this development could induce improvements to many real-time computer vision applications