Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination, expressions and obstacles. In this paper, an improved ASM framework, GentleBoost based SIFT-ASM is proposed. Local appearances of landmarks are originally represented by SIFT (Scale-Invariant Feature Transform) descriptors, which are gradient orientation histograms based representations of image neighborhood. They can provide more robust and accurate guidance for search than grey-level profiles. Moreover, GentleBoost classifiers are applied to model and search the SIFT features instead of the unnecessary assumption of Gaussian distribution. Experimental results show that SIFT-ASM significantly outperforms the original ASM in aligning and localizing facial features.

Service robots need to be able to recognize and identify objects located within complex backgrounds. Since no single method may work in every situation, several methods need to be combined and robots have to select the appropriate one automatically. In this paper we propose a scheme to classify situations depending on the characteristics of the object of interest and user demand. We classify situations into four groups and employ different techniques for each. We use Scale-invariant feature transform (SIFT), Kernel Principal Components Analysis (KPCA) in conjunction with Support Vector Machine (SVM) using intensity, color, and Gabor features for five object categories. We show that the use of appropriate features is important for the use of KPCA and SVM based techniques on different kinds of objects. Through experiments we show that by using our categorization scheme a service robot can select an appropriate feature and method, and considerably improve its recognition performance. Yet, recognition is not perfect. Thus, we propose to combine the autonomous method with an interactive method that allows the robot to recognize the user request for a specific object and class when the robot fails to recognize the object. We also propose an interactive way to update the object model that is used to recognize an object upon failure in conjunction with the user's feedback.

In this paper a new class of single error-correcting fixed block-length (d, k) codes has been proposed. The correctable error types are peak-shift error, insertion or deletion error, symmetric error, etc. The basic technique to construct codes is a systematic construction algorithm of multilevel sequences with a constant Lee weight (TALG algorithm). The coding rate and efficiency are considerably good, and hence the proposed new codes will be very useful for improving the reliability of high density magnetic recording.

Ordered binary decision diagrams (OBDDs) have been widely used in many CAD applications as efficient data structures for representing and manipulating Boolean functions. For the efficient use of the OBDD, it is essential to find a good variable order, because the size of the OBDD heavily depends on its variable order. Dynamic variable reordering is a promising solution to the variable ordering problem of the OBDD. Dynamic variable reordering with the sifting algorithm is especially effective in minimizing the size of the OBDD and reduces the need to find a good initial variable order. However, it is very time-consuming for practical use. In this paper, we propose two new implementation techniques for fast dynamic variable reordering. One of the proposed techniques reduces the number of variable swaps by using the lower bound of the OBDD size, and the other accelerates the variable swap itself by recording the node states before the swap and the pivot nodes of the swap. By using these new techniques, we have achieved the speed-up ranging from 2.5 to 9.8 for benchmark circuits. These techniques have reduced the disadvantage of dynamic variable reordering and have made it more attractive for users.