PENEGASAN KENAMPAKAN KABEL SUTET PADA FOTO MOSAIC DENGAN ALGORITMA RANSAC BERBASIS BAHASA PEMROGRAMAN PYTHON (Studi Kasus: Perumahan Puncak Dieng, Desa Kalisongo, Kecamatan Dau, Kabupaten Malang)

Foto mosaic adalah teknik pengolahan yang digunakan untuk menghasilkan gambar yang menampilkan pemandangan yang luas dan meluas. Pada pengolahan ini masih mengalami beberapa permasalahan seperti keadaan pencahayaan yang buruk atau kehadiran objek lain. RANSAC (Random Sample Consensus) adalah sebuah algoritma yang digunakan untuk memperkirakan parameter model dari data yang mengandung data yang tidak sesuai (outlier). Metode RANSAC memilih secara acak titik atau sampel data yang akan digunakan unutk membuat model garis. Sampel tersebut digunakan untuk menghitung parameter model yang sesuai. Data yang sesuai atau tidak sesuai ditentukan menggunakan nilai ambang batas yang ditentukan. Hasil dari metode ini juga tergantung dari jumlah iterasi yang dilakukan dalam melakukan pengolahannya. Pada penelitian ini metode RANSAC diterapkan dalam penegasan kabel SUTET pada foto mosaic berbasis pemrograman bahasa python. Pada penelitian ini dilakuakn uji validasi seberapa baik model garis yang dihasilkan metode RANSAC terhadap garis kabel listrik sebenarnya. Uji ini dilakukan dengan membandingkan hasil data yang sesuai (inlier) dan tidak sesuai (outlier) pada hasil perhitungan algoritma RANSAC dengan perhitungan manual apakah hasilnya sama atau tidak sesuai dengan nilai ambang yangh dapat diterima adalah ≤ ± 2 (piksel). Perhitungan pada metode ini menggunakan regresi linier dan euclidean distance. Berdasarkan hasil penelitian ini untuk nilai data yang sesuai (inlier) dan tidak sesuai (outlier) sudah sesuai dengan nilai ambnag batas yang sudah ditentukan. Sedangkan untuk hasil secara visualisasi metode RANSAC berbasis bahasa pemrograman python belum sesuai. Metode RANSAC dapat diterapkan hanya pada satu buah foto. Hal ini dikarenakan model garis yang dihasilkan tidak sesuai dengan penegasan kenampakan kabel SUTET menggunakan bahasa pemrograman python. Distorsi atau penyimpangan ini mengakibatkan model garis yang dihasilkan metode RANSAC mengikuti kenampakan kabel yang dianggap benar pada foto mosaic tersebut

Learning Regional Attraction for Line Segment Detection

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with arXiv:1812.0212

Dielectric-sphere-based microsystem for optical super-resolution imaging

Well-established imaging techniques proved that features below the diffraction limit can be observed optically using so-called super-resolution microscopies, which overcome Abbe's resolution limit. In traditional far-field microscopy, the introduction of fluorescent samples and engineered light paths was key for this breakthrough. In parallel, near-field techniques with similar performance were developed, but they suffered from a limited field-of-view. The merge of the two approaches was already demonstrated ~15 years ago, when micrometer-sized dielectric objects positioned on a sample were found to be able to image the sample with super-resolution. By observing the sample through the micro-object with a classical optical microscope, the latter could capture a virtual image showing sub-diffraction details. Although this way the near-field information transfer into the far-field by the micro-object was proven and found to be key for enabling super-resolution imaging, the limited field-of-view, as determined by the size of the micro-object, remained an issue. In this dissertation, a novel method is presented that provides a microscopy technique capable of achieving super-resolution without field-of-view restrictions. Based on previous studies, dielectric microspheres were chosen for this imaging technique. First, the working principle of these microspheres was explored by investigating both the illumination and the reflected light path. These findings provided a better understanding on the phenomena working behind microsphere-assisted imaging and allowed to create an engineering toolbox that can be used to design microsphere-based optical systems. This was followed by an investigation on microfabrication techniques, in order to create a microchip that can serve as a bridge between a single microsphere and the macro-sized-components of a classical optical microscope. The resulting chip was later embedded in a custom fixation system that allowed scanning of this microsphere over the sample, while keeping its position fixed compared to the microscope objective. The microscope mounted camera recorded pictures during the scan, which were used to generate a large field-of-view super-resolution image by stitching. After initial successes, the setup was improved in terms of robustness and application range. The new version allowed field-of-view in the millimeter range, while it could be operated in both oil- and water-immersion. Parallel imaging with an array of microspheres was also implemented, which further enhanced the imaging speed. The algorithmic background (including an automated scanning and image reconstruction protocol) of this microscopy method was developed in-house. Its validation showed superior performance compared to existing software. Future developments (e.g. employment of 3D-printing for mass-production, imaging in vivo biological samples, metrology applications) are envisioned. The findings presented here may pave the road for an easy-to-use, generalized super-resolution imaging system