We present a system for constructing 3D models from simple hand-drawn sketches. The system exploits a priori knowledge about the type of object being sketched. In particular, we assume that the class of object being drawn is known, (e.g., a rocket ship) and that the object will be drawn using a fixed number of known components. By using these assumptions, we show that we can build a sketch-base...

Human–Machine Cooperations (HMCs) can balance the advantages and disadvantages of human computation (accurate but costly) and machine computation (cheap but inaccurate). This paper studies HMCs in agglomerative hierarchical clusterings, where the machine can ask the human some questions. The human will return the answers to the machine, and the machine will use these answers to correct errors i...

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map i...

Here, an algorithm is presented for solving the minimum sum-of-squares clustering problems using their difference of convex representations. The proposed algorithm is based on an incremental approach and applies the well known DC algorithm at each iteration. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets.

each license plate recognition system is composed of three main parts, namely, license plate detection, character segmentation and character recognition. in this paper, we focus on the improvement and innovation of the character recognition step. for this purpose, a new hierarchical architecture based on support vector machines (svms) is suggested for persian license plate characters recognitio...