3D object recognition from range images

İzciler, Fatih
Recognizing generic objects by single or multi view range images is a contemporary popular problem in 3D object recognition area with developing technology of scanning devices such as laser range scanners. This problem is vital to current and future vision systems performing shape based matching and classification of the objects in an arbitrary scene. Despite improvements on scanners, there are still imperfections on range scans such as holes or unconnected parts on images. This studyobjects at proposing and comparing algorithms that match a range image to complete 3D models in a target database.The study started with a baseline algorithm which usesstatistical representation of 3D shapesbased on 4D geometricfeatures, namely SURFLET-Pair relations.The feature describes the geometrical relationof a surface-point pair and reflects local and the global characteristics of the object. With the desire of generating solution to the problem,another algorithmthat interpretsSURFLET-Pairslike in the baseline algorithm, in which histograms of the features are used,isconsidered. Moreover, two other methods are proposed by applying 2D space filing curves on range images and applying 4D space filling curves on histograms of SURFLET-Pairs. Wavelet transforms are used for filtering purposes in these algorithms. These methods are tried to be compact, robust, independent on a global coordinate frame and descriptive enough to be distinguish queries’ categories.Baseline and proposed algorithms are implemented on a database in which range scans of real objects with imperfections are queries while generic 3D objects from various different categories are target dataset.


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Yalçın Bayramoğlu, Neslihan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2011)
The improvements in 3D scanning technologies have led the necessity for managing range image databases. Hence, the requirement of describing and indexing this type of data arises. Up to now, rather much work is achieved on capturing, transmission and visualization; however, there is still a gap in the 3D semantic analysis between the requirements of the applications and the obtained results. In this thesis we studied 3D semantic analysis of range data. Under this broad title we address segmentation of range...
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In this paper an object recognition and localization system based on ultrasonic range imaging to be used in optically opaque environments is introduced. The system is especially designed for robotics applications. The ultrasonic image is acquired by scanning ultrasonic transducers in two dimensions above the area where objects are located. The features that are used for recognition and localization processes are extracted from the outermost boundaries of the objects present in the input scene. Experimental ...
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3D Face Reconstruction Using Stereo Images and Structured Light
OZTURK, Ahmet Oguz; Halıcı, Uğur; ULUSOY PARNAS, İLKAY; AKAGUNDUZ, Erdem (2008-04-22)
In this paper, the 3D face scanner that we developed using stereo cameras and structured light together is presented. Structured light having a pattern of vertical lines is used to create feature points and to match them easily. 3D point cloud obtained by stereo analysis is post processed to obtain the 3D model in obj format.
Automatic target recognition and detection in infrared imagery under cluttered background
GÜNDOĞDU, ERHAN; KOÇ, AYKUT; Alatan, Abdullah Aydın (2017-09-14)
Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object ...
Citation Formats
F. İzciler, “3D object recognition from range images,” M.S. - Master of Science, Middle East Technical University, 2012.