A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The system architecture which is proposed in this paper consists of two parts, the learning and the detection part of road sign images. The proposed system has the standard architecture with adaboost algorithm. Adaboost is a popular algorithm which used to detect an object in real time. To improve the detection rate of adaboost algorithm, this paper proposes a new combination method of classifiers in every stage. In the case of detecting road signs in real environment, it can be ambiguous to decide to which class input images belong. To overcome this problem, we propose a method that applies fuzzy measure and fuzzy integral which use the importance and the evaluated values of classifiers within one stage. It is called fuzzy-boosting in this paper. Also, to improve the speed of a road sign detection algorithm using adaboost at the detection step, we propose a method which chooses several candidates by using MC generator. In this paper, as the sub-windows of chosen candidates pass classifiers which are made from fuzzy-boosting, we decide whether a road sign is detected or not. Using experiment result, we analyze and compare the detection speed and the classification error rate of the proposed algorithm applied to various environment and condition.

A new adaptive model based on fuzzy integrals has been presented and used for combining three well-known methods, Eigenface, Fisherface and SOMface, for face classification. After training the competence estimation functions, the adaptive mechanism enables our system the filtering of unsure judgments of classifiers for a specific input. Comparison with classical and non-adaptive approaches proves the superiority of this model. Also we examined how these features contribute to the combined result and whether they can together establish a more robust feature.

There were researches that measured effort required to understand and adapt components based on the complexity of the component, which is some general criterion related to the intrinsic quality of the component to be adapted and understood. They, however, don't consider significance of the measurement attributes and user must decide reusability of similar components for himself. Therefore, in this paper, we propose a new method that can measure the DOR (Degree Of Reusability) of the components by considering the significance of the measurement attributes. We calculates the relative significance of them by using rough set and integrate the significance with the measurement value by using Sugeno's fuzzy integral. Lastly, we apply our method to the source code components and show through statistical technique that it can be used as the ordinal and ratio scale.

Understanding unknown objects in images is one of the most important fields of the computer vision. We are confronted with the problem of dealing with the ambiguity of the image information about unknown objects in the scene. The purpose of this paper is to propose a new object recognition method based on the fuzzy relation system and the fuzzy integral. In order to deal with the ambiguity of the image information, we apply the fuzzy theory to object recognition subjects. Firstly, we define the degree of similarity based on the fuzzy relation system among input images and object models. In the next, to avoid the uncertainty of relations between the input image and the 2-D aspects of models, we integrate the degree of similarity obtained from several input images by the fuzzy integral. This proposing method makes it possible to recognize the unknown objects correctly under the ambiguity of the image information. And the validity of our method is confirmed by the experiments with six kinds of chairs.