In hand gesture recognition or hand tracking systems relied on hand modeling methods, it is usually required to extract from a hand image some hand features. This paper presents a new robust method based on connected component labeling (CCL), distance transform (DT) and Hough transform (HT) to fast and precisely extract the center of the hand, the directions and the fingertip positions of all outstretched fingers on a skin color detection image. First, the method uses a simple but reliable technique that is performed on both the connected component labeling image and the distance transform image to extract the center of the hand and a set of features pixels, which are called distance-based feature pixels. Then, the Hough transform is calculated on these feature pixels to detect all outstretched fingers as lines. From the line detection result, the finger directions and the fingertip positions are determined easily and precisely. This method can be carried out fast and accurately, even when the skin color detection image includes hand, faces and some noise. Moreover, the number of distance-based feature pixels is usually not so high; therefore, the line detection process based on the Hough transform can be performed very fast. That can satisfy the demands of a real-time human-robot interaction system based on hand gestures or hand tracking.

& Reinforcement learning (RL) has been widely used to solve problems with a little feedback from environment. Q learning can solve Markov decision processes (MDPs) quite well. For partially observable Markov decision processes (POMDPs), a recurrent neural network (RNN) can be used to approximate Q values. However, learning time for these problems is typically very long. We present a new combination of RL and RNN to find a good policy for POMDPs in a shorter learning time. This method contains two phases: firstly, state space is divided into two groups (fully observable state group and hidden state group); secondly, a Q value table is used to store values of fully observable states and an RNN is used to approximate values for hidden states. Results of experiments in two grid world problems show that the proposed method enables an agent to acquire a policy with better learning performance compared to the method using only a RNN.

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This paper proposes modification of the conventional Sum of Absolute Differences (SAD) for performance improvement in depth-map estimation from stereo images captured by a camera in a stereo system. The conventional SAD is commonly search in whole stereo images to find out the difference in pixels between the left and right captured images, and then obtains the corresponding disparity map and this may lead to high elapsing time. In order to reduce the number of searching pixels, the proposed modified SAD tries to estimate the difference only from edge pixels which are referred as pixels-ofinterest and bring significant information about depth map. The number of pixels being searched is reduced to about 17% on the total pixels, hence the total elapsing time is saved up to around 89% compared to that of the conventional SAD. This results is promising for implementation of a real-time vision system. Keywords-sum of absolute difference, stereo camera, disparity map, stereo visionI.

This chapter reviews the theoretical literature on tourism experience and highlights the relevance of various psychological theories and concepts in understanding tourist experiences. The chapter first provides a brief history of the study of tourism experience, and then discusses the development of the concept of experience and different theoretical models.