TRIANGULAR MESH APPROACH FOR AUTOMATIC REPAIR OF MISSING SURFACES OF LOD2 BUILDING MODELS

3D city models are increasingly being used to represent the complexity of today’s urban areas, as they aid in understanding how different aspects of a city can function. For instance, several municipalities and governmental organisations have constructed their 3D city models for various purposes. These 3D models, which are normally complex and contain semantics information, have typically been used for visualisation and visual analysis purposes. However, most of the available 3D models open datasets contain many geometric and topological errors, e.g., missing surfaces (holes), self-intersecting surfaces, duplicate vertices, etc. These errors prevent the datasets from being used for advanced applications such as 3D spatial analysis which requires valid datasets and topology to calculate its volume, detect surface orientation, area calculation, etc. Therefore, certain repairs must be done before taking these models into actual applications, and hole-filling (of missing surfaces) is an important one among them. Several studies on the topic of automatic repair of the 3D model have been conducted by various researchers, with different approaches have been developed. Thus, this paper describes a triangular mesh approach for automatically repair invalid (missing surfaces) 3D building model (LOD2). The developed approach demonstrates an ability to repair missing surfaces (with holes) in a 3D building model by reconstructing geometries of the holes of the affected model. The repaired model is validated and produced a closed-two manifold model.

LIMOFilling: Local Information Guide Hole-Filling and Sharp Feature Recovery for Manifold Meshes

Manifold mesh, a triangular network for representing 3D objects, is widely used to reconstruct accurate 3D models of objects structure. The complexity of these objects and self-occlusion, however, can cause cameras to miss some areas, creating holes in the model. The existing hole-filling methods do not have the ability to detect holes at the model boundaries, leaving overlaps between the newly generated triangles, and also lack the ability to recover missing sharp features in the hole-region. To solve these problems, LIMOFilling, a new method for filling holes in 3D manifold meshes was proposed, and recovering the sharp features. The proposed method, detects the boundary holes robustly by constructing local overlap judgments, and provides the possibility for sharp features recovery using local structure information, as well as reduces the cost of maintaining manifold meshes thus enhancing their utility. The novel method against the existing methods have been tested on different types of holes in four scenes. Experimental results demonstrate the visual effect of the proposed method and the quality of the generated meshes, relative to the existing methods. The proposed hole-detection algorithm found almost all of the holes in different scenes and qualitatively, the subsequent repairs are difficult to see with the naked eye.

Segmentasi Citra Warna Otomatis Rambu Lalu Lintas dengan Penerapan Mask Thresholder

Segmentasi rambu jalan dan sistem pengenalan merupakan aspek penting dan esensial untuk digunakan pada sistem autopilot, smart car atau autonomous vehicle yang memungkinkan kendaraan dapat berjalan tanpa pengemudi manusia. Namun demikian pada paper ini citra rambu jalan yang diproses dalam bentuk image dan bukan vidio/kamera. Penelitian ini bertujuan memperoleh citra warna rambu jalan dengan presisi yang tinggi. Pada penelitian ini ditawarkan metode segmentation rambu jalan dengan mengembangkan Fuzzy C-means dengan menginjeksikan teknik mask-tresholder untuk mendapatkan hasil segmentasi warna rambu jalan dengan presisi tinggi. Mula-mula citra dideteksi dalam ruang warna RGB kemudian diubah menjadi model warna L*a*b dan dilanjukan ekstraksi untuk mendapatkan komponan warna *a*. Fuzzy C-means dterapkan pada citra warna *a* untuk segmentasi foreground dan background. Proses dilanjutkan dengan operasi opening – closing dan hole filling untuk mereduksi noise pada hasil segmentasi. Sampai pada tahap ini hasil segmentasi yang diperoleh adalah citra binary dimana foreground dalam warna putih dan bacground dalam warna hitam. Oleh karena itu untuk mendapatkan citra hasil segmentasi rambu jalan dalam ruang warna RGB maka diperlukan proses konvolusi. Teknik konvolusi yang dilakukan pada penelitian ini adalah dengan mengalikan tiap piksel citra mask tresholder dengan citra semula dalam ruang warna RGB. Citra mask tresholder yang digunakan dalam penelitian ini adalah citra binary hasil segmentasi dengan Fuzzy Cmeans. Metode yang ditawarkan telah diuji dengan citra rambu jalan sejumlah 18 citra. Hasil yang diperoleh menunjukkan kinerja metode yang diusulkan mampu mensegmentasi citra rambu jalan dalam sesuai warna citra semula. Menggunakan metode jaccard untuk mengukur akurasi kinerja didapat tingkat akurasi adalah 97,73%.

Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.

Deep Learning based Intraretinal Layer Segmentation using Cascaded Compressed U-Net

Intraretinal layer segmentation on macular optical coherence tomography (OCT) images generates non invasive biomarkers querying neuronal structures with near cellular resolution. While first deep learning methods have delivered promising results with high computing power demands, a reliable, power efficient and reproducible intraretinal layer segmentation is still an unmet need. We propose a cascaded two-stage network for intraretinal layer segmentation, with both networks being compressed versions of U-Net (CCU-INSEG). The first network is responsible for retinal tissue segmentation from OCT B-scans. The second network segments 8 intraretinal layers with high fidelity. By compressing U-Net, we achieve 392- and 26-time reductions in model size and parameters in the first and second network, respectively. Still, our method delivers almost similar accuracy compared to U-Net without additional constraints of computation and memory resources. At the post-processing stage, we introduce Laplacian-based outlier detection with layer surface hole filling by adaptive non-linear interpolation. We trained our method using 17,458 B-scans from patients with autoimmune optic neuropathies, i.e. multiple sclerosis, and healthy controls. Voxel-wise comparison against manual segmentation produces a mean absolute error of 2.3mu, which is 2.5x better than the device's own segmentation. Voxel-wise comparison against external multicenter data leads to a mean absolute error of 2.6mu for glaucoma data using the same gold standard segmentation approach, and 3.7mu mean absolute error compared against an externally segmented reference data set. In 20 macular volume scans from patients with severe disease, 3.5% of B-scan segmentation results were rejected by an experienced grader, whereas this was the case in 41.4% of B-scans segmented with a graph-based reference method.

Image Speckle Denoising for Securing Internet of Smart Sensors

How to improve utility performance when securing sensitive data is an important research problem in Internet of smart sensors. In this paper, we study secured image speckle denoising for networked synthetic aperture radar (SAR). Speckle noise of SAR affects image quality and has a great influence on target detection and recognition. MSTAR dataset is often used in image target recognition. In this paper, a subregion-based method is proposed in order to improve the accuracy of target recognition and better retain target information while filtering and denoising the image. The new method applies advanced encryption techniques to protect sensitive data against malicious attack. Firstly, the image is divided into marked areas and unmarked areas through edge extraction and hole filling. Secondly, we use different size windows and filtering methods to filter the image in different areas. The experimental results show that the proposed algorithm has obvious advantages over MR-NLM, SSIM-NLM, Frost, and BM3D filtering in terms of equivalent view number and preserving edge and structure.