Conventional fringe projection profilometry methods often have difficulty in reconstructing the 3D model of objects when the fringe images have the so-called highlight regions due to strong illumination from nearby light sources. Within a highlight region, the fringe pattern is often overwhelmed by the strong reflected light. Thus, the 3D information of the object, which is originally embedded in the fringe pattern, can no longer be retrieved. In this paper, a novel inpainting algorithm is proposed to restore the fringe images in the presence of highlights. The proposed method first detects the highlight regions based on a Gaussian mixture model. Then, a geometric sketch of the missing fringes is made and used as the initial guess of an iterative regularization procedure for regenerating the missing fringes. The simulation and experimental results show that the proposed algorithm can accurately reconstruct the 3D model of objects even when their fringe images have large highlight regions. It significantly outperforms the traditional approaches in both quantitative and qualitative evaluations.

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In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

AbstrakDewasa ini perkembangan teknologi di dunia robot edukasi berkembang pesat. Robot-robot edukasi ini sering digunakan dalam riset penelitian karena kemudahan-kemudahan yang diberikannya dari segi perangkat keras. Salah satu contoh robot edukasi adalah robot LEGO Mindstorms NXT. Pada penelitian ini robot LEGO dibangun dalam bentuk robot line follower. Robot ini mampu menelusuri dan mencari jalan keluar dari labirin dinamis. Dalam menelusuri dan mencari jalan keluar, robot LEGO menggunakan algoritma Breadth First Search dan Manhattan Distance dalam memutuskan jalan mana yang harus diambil. Ketika menemui objek halangan, robot LEGO akan mengenali dan menghindari objek halangan tersebut dengan algoritma Obstacle Detection yang dimilikinya. Hasil implementasi membuktikan bahwa algoritma penelusuran labirin dinamis ini dapat diimplementasikan pada robot LEGO meskipun terdapat banyak keterbatasan dalam robot LEGO. Kata Kunci: breadth first search, labirin dinamis, LEGO mindstroms NXT, line following, obstacle detection AbstractNowadays, the development of technology in educational robots is rapidly evolving. Educational robots are often used in research studies because they provide convenience in terms of hardware. One example is the educational robot LEGO Mindstorms NXT robot. In this research, LEGO robots built in the form of line follower robot. Robot is able to browse and find a way out of the dynamic labyrinth. In track and find a way out, LEGO robot uses an algorithm Breadth First Search and Manhattan Distance in deciding which path to take. When encountering an obstacle object, LEGO robot will recognize and avoid that obstacle objects with Obstacle Detection algorithm. The results prove that the implementation of a dynamic maze search algorithm can be implemented on a LEGO robot even though there are many limitations in LEGO robot.

In a fringe projection profilometry (FPP) process, the captured fringe images can be modeled as the superimposition of the projected fringe patterns on the texture of the objects. Extracting the fringe patterns from the captured fringe images is an essential procedure in FPP; but traditional single-shot FPP methods often fail to perform if the objects have a highly textured surface. In this paper, a new single-shot FPP algorithm which allows the object texture and fringe pattern to be estimated simultaneously is proposed. The heart of the proposed algorithm is an enhanced morphological component analysis (MCA) tailored for FPP problems. Conventional MCA methods which use a uniform threshold in an iterative optimization process are inefficient to separate fringe-like patterns from image texture. We extend the conventional MCA by taking advantage of the low-rank structure of the fringe's sparse representation to enable an adaptive thresholding process. It ends up with a robust single-shot FPP algorithm that can extract the fringe pattern even if the object has a highly textured surface. The proposed approach has a side benefit that the object texture can be simultaneously obtained in the fringe pattern estimation process, which is useful in many FPP applications. Experimental results have demonstrated the improved performance of the proposed algorithm over the conventional single-shot FPP approaches.