A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.

This paper proposes a novel approach of constructing statistical preference models for context-aware personalized applications such as recommender systems. In constructing context-aware statistical preference models, one of the most important but difficult problems is acquiring a large amount of training data in various contexts/situations. In particular, some situations require a heavy workload to set them up or to collect subjects capable of answering the inquiries under those situations. Because of this difficulty, it is usually done to simply collect a small amount of data in a real situation, or to collect a large amount of data in a supposed situation, i.e., a situation that the subject pretends that he is in the specific situation to answer inquiries. However, both approaches have problems. As for the former approach, the performance of the constructed preference model is likely to be poor because the amount of data is small. For the latter approach, the data acquired in the supposed situation may differ from that acquired in the real situation. Nevertheless, the difference has not been taken seriously in existing researches. In this paper we propose methods of obtaining a better preference model by integrating a small amount of real situation data with a large amount of supposed situation data. The methods are evaluated using data regarding food preferences. The experimental results show that the precision of the preference model can be improved significantly.

In the task of forming high-level object-centered models from low-level image-based features, parts serve as an intermediate representation. A representation of parts for object recognition should be rich, stable, and invariant to changes in the viewing conditions. In addition, it should be capable of describing partially occluded shapes. This paper describes a method for decomposing shapes into parts. The method is based on pairs of negative curvature minima which have a good continuation at their boundary tangents. A measure of good continuation is proposed by using the coefficients of cocircularity, smoothness, and proximity. This method could recover parts in a direct computation, therefore efficient in calculation than the former. Currently, we assume that the shape is a closed planar curve.