Adaptif Range-Constrained Otsu Untuk Pemilihan Threshold Secara Otomatis Pada Histogram Citra Dengan Variansi Kelas Yang Tidak Seimbang

Image Thresholding merupakan proses segmentasi untuk memisahkan foreground dan background pada citra dengan cara membagi histogram citra menjadi dua kelas. Beberapa metode thresholding seperti Otsu dan Range-constrained Otsu menggunakan nilai variansi dari histogram untuk mendapatkan titik threshold, namun ketika menangani citra yang memiliki nilai variansi kelas foreground dan background tidak seimbang titik threshold yang dihasilkan kurang tepat. Paper ini mengusulkan metode Adaptif Range-constrained Otsu untuk mengatasi permasalahan variansi kelas yang tidak seimbang dengan cara mencari kelas yang memiliki nilai variansi lebih besar, untuk mendapatkan titik threshold yang lebih tepat. Pengujian menggunakan 22 NDT image dengan evaluasi misclassification error rate dan metode perankingan menunjukkan metode ini menghasilkan rerata ME 0.1153. Sedangkan Otsu sebesar 0.1746. Nilai rerata ranking 3.55, selisih 0.05 dibanding Kittler III. Hasil ini menunjukkan metode yang diusulkan kompetitif, terutama untuk segmentasi citra yang memiliki variansi kelas tidak sama

Oil Spill Detection Using Deep Neural Networks

Oil spills have catastrophic effects on the environment, wildlife, economy, and human health. Therefore, timely detection of oil spills can reduce these disastrous impacts. Existing oil spill detection practices include in-situ (e.g., acoustic method, vapor sampling, pressure-point-analysis, and negative pressure wave) and remote sensing methods (e.g., traditional image processing and image processing using artificial intelligence). These methods rely mostly on skilled personnel for data collection, processing, and analysis, thus leading to slow, costly, and subjective results. Furthermore, oil platforms and pipelines are often situated in remote, harsh areas, making inspections hazardous. To remedy this problem, in this Thesis, three state-of-the-art artificial intelligence (AI) models, namely VGG16, YOLOv3 (you-only-look-once), and mask R-CNN (mask region-based convolutional neural network) are used in a transfer learning scheme to facilitate the process of detecting oil spills and surrounding objects such as vessels and oil rigs. Keyword search, a semi-supervised machine learning approach, is used to collect red-green-blue (R-G-B) imagery for training and testing these models. The methodology includes image classification, object detection, and instance segmentation. The VGG16 model is used to predict the existence of an oil spill in an image, yielding an accuracy of 93%. The YOLOv3 model is implemented to detect and mark the location of vessels and oil rigs. The mean average precision for detecting these two object classes is 61.5% (46% for vessel and 77% for oil rig). The mask R-CNN model is utilized to identify oil spill boundaries at the pixel level in the input image. Results (considering all test images) indicate an average precision of 62%, and an average recall of 71%. Findings of this Thesis are sought to benefit oil and gas industry stakeholders and coastal communities by creating operational AI-assisted technologies for timely detection and response to oil spills and other environmental pollutions, ultimately contributing to human health, environment preservation, and profitability of energy exploration projects

Reconstrução de modelos CAD 3D baseada em imagem fotográfica digital

Mestrado em Engenharia MecânicaO processo de reconstrução de modelos computacionais tridimensionais (CAD3D) constitui uma importante área de investigação com variadas aplicações que incluem a engenharia inversa, assim como outras tecnologias assistidas por computador tais como: a engenharia assistida por computador, o fabrico assistido por computador e a prototipagem rápida. Tradicionalmente, as abordagens de reconstrução de geometrias 3D de peças técnicas são baseadas em informação bidimensional (2D) relativa a duas ou três projecções ortogonais (imagens ortográficas 2D). Adicionalmente, estas abordagens revelam pouca eficiência e flexibilidade em relação à reconstrução geométrica 3D de geometrias complexas. Refira-se igualmente que grande parte dos objectos usados em engenharia apresenta em regra geometrias complexas. Finalmente, as abordagens tradicionais estão limitadas à existência de desenhos 2D, o que pode inviabilizar a sua utilização em certas aplicações, nomeadamente onde esta informação ortográfica não exista. Este projecto de investigação pretende apresentar soluções para os referidos problemas. Tal passará pelo desenvolvimento de novas técnicas de reconstrução geométrica 3D que utilizem três, duas, ou apenas uma imagem bidimensional. Estas imagens em perspectiva serão obtidas através de uma câmara fotográfica digital de baixo custo, e por conseguinte, todos os dados digitais 2D necessários ao processo de reconstrução de um modelo CAD 3D serão sempre facilmente obtidos. ABSTRACT: The process of reconstruction of three-dimensional computer models CAD3D, is an important area of research with various applications that include reverse engineering, and other computer-assisted technologies such as: a computerassisted engineering, manufacture and computer-assisted rapid prototyping . Traditionally, approaches the reconstruction of 3D geometry of parts technical information are based on two-dimensional (2D) for two or three orthogonal projections (2D orthographic images). Additionally, these approaches show little efficiency and flexibility on the geometric 3D reconstruction of complex geometries. In addition, many of the objects used in engineering presents a rule complex geometries. Finally, traditional approaches are limited to the existence of 2D drawings, which can prevent its use in certain applications, particularly where there is spelling this information. This research project aims to provide solutions to those problems. This will require the development of new techniques for reconstruction using 3D geometric three, two, or only a two-dimensional image. These images in perspective will be obtained through a digital camera at low cost, and therefore, all 2D digital data necessary for the reconstruction of a 3D CAD model will always be easily obtained

Decoupled Deformable Model For 2D/3D Boundary Identification

The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects

Segmentation of Human Muscles of Mastication from Magnetic Resonance Images

Ph.DDOCTOR OF PHILOSOPH

Parallel Genetic Algorithm based Thresholding Schemes for Image Segmentation

In this thesis, the problem of image segmentation has been addressed using the notion of thresholding.Since the focus of this work is primarily on object/objects background classification and fault detection in a given scene, the segmentation problem is viewed as a classification problem. In this regard, the notion of thresholding has been used to classify the range of gray values and hence classifies the image. The gray level distributions of the original image or the proposed feature image have been used to obtain the optimal threshold. Initially, PGA based class models have been developed to classify different classes of a nonlinear multimodal function. This problem is formulated where the nonlinear multimodal function is viewed as consisting of multiple class distributions.Each class could be represented by the niche or peaks of that class.Hence, the problem has been formulated to detect the peaks of the functions. PGA based clustering algorithm has been proposed to maintain stable sub-populations in the niches and hence the peaks could be detected. A new interconnection model has been proposed for PGA to accelerate the rate of convergence to the optimal solution. Convergence analysis of the proposed PGA based algorithm has been carried out and is shown to converge to the solution. The proposed PGA based clustering algorithm could successfully be tested for different classes and is found to converge much faster than that of GA based clustering algorithm