Object algebra in an object-oriented data model

Şahin, Kemal


Zontul, H; Ersak, Aydın (1994-04-14)
The paper presents an object-oriented representation of a robot manipulator. The representation developed here is appropriate to implement a robot simulation package. The software package is easy to use and gives insight to the robot kinematics, the dynamics and the control subjects. The robot representation has been tested in resolved motion rate control and hybrid position/force control applications.
Object-based image labeling through learning by example and multi-level segmentation
Xu, Y; Duygulu, P; Saber, E; Tekalp, AM; Yarman Vural, Fatoş Tunay (Elsevier BV, 2003-06-01)
We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate...
Object recognition and segmentation via shape models
Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016)
In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
Object Detection with Convolutional Context Features
Kaya, Emre Can; Alatan, Abdullah Aydın (2017-01-01)
A novel extension to Huh B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection ben...
Object Oriented Multi Block Approach for the Solution of the Euler Equations
Sert, Cüneyt; Dener, Cem (2004-01-01)
Citation Formats
K. Şahin, “Object algebra in an object-oriented data model,” Middle East Technical University, 1995.