Dynamic gesture recognition using PCA with multi-scale theory and HMM

In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete Hidden Markov Models (DHMM). We use a hierarchical decision tree based on multiscale theory. Firstly we convolve all members of the training data with a Gaussian kernel, which blurs differences between images and reduces their separation in feature space. This reduces the number of eigenvectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space into two clusters using the k-means algorithm. Then the level of blurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided into two and the process is repeated. We thus produce a binary tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures in real time. The output of the decision tree is then input into DHMM to recognize temporal information

Fuzzy Logic Approach for Mobile Robot in Intelligent Space

This project introduces the fuzzy logic approach for mobile robot in intelligent space. There are three major algorithms involved. They are known as object classification, object tracking and obstacle avoidance. The inputs are received from cameras which are mounted at a ceiling. The main idea of the object classification is to classify object into three categories depending upon their colors; the categories are mobile robot, destinations and obstacle position. These categories are represented by X symbol with different colors. This system is to teach and train the mobile robot proceeding to destination without hitting the obstacle. The mobile robot is autonomous; that means, it could be pursuing to the target position automatically without user guided. In this project, fuzzy logic is use to guide the mobile robot direction until it reaches the target position. This system is generates in real-time and suitable for indoor environment applications. One of the unique advantages of this project is that it only uses, there only used a camera and image processing generated by the algorithms itself without additional sensor such as sonar or IR sensor

Robot environment learning with a mixed-linear probabilistic state-space model

This thesis proposes the use of a probabilistic state-space model with mixed-linear dynamics for learning to predict a robot's experiences. It is motivated by a desire to bridge the gap between traditional models with predefined objective semantics on the one hand, and the biologically-inspired "black box" behavioural paradigm on the other. A novel EM-type algorithm for the model is presented, which is less compuationally demanding than the Monte Carlo techniques developed for use in (for example) visual applications. The algorithm's E-step is slightly approximative, but an extension is described which would in principle make it asymptotically correct. Investigation using synthetically sampled data shows that the uncorrected E-step can any case make correct inferences about quite complicated systems. Results collected from two simulated mobile robot environments support the claim that mixed-linear models can capture both discontinuous and continuous structure in world in an intuitively natural manner; while they proved to perform only slightly better than simpler autoregressive hidden Markov models on these simple tasks, it is possible to claim tentatively that they might scale more effectively to environments in which trends over time played a larger role. Bayesian confidence regions—easily by mixed-linear model— proved be an effective guard for preventing it from making over-confident predictions outside its area of competence. A section on future extensions discusses how the model's easy invertibility could be harnessed to the ultimate aim of choosing actions, from a continuous space of possibilities, which maximise the robot's expected payoff over several steps into the futur

Local Appearance Space for Recognition of Navigation Landmarks

This paper presents a technique for visual recognition in which the appearance of objects is represented by families of surfaces in a local appearance space. An orthogonal family of local appearance descriptors is obtained by applying principal components analysis to small image windows. These descriptors define the axes of a local appearance space. Each local neighborhood of an image projects to a point in this space. By projecting the set of all neighborhoods of a certain size which compose an image we obtain a discrete sampling of a surface. Projecting neighborhoods from images taken at different viewing positions gives a family of surfaces which represent the possible local appearances from those viewing directions. In this manner we compose a representation in which an object or a landmark can be identified by directly addressing into the local appearance space. Visual landmarks (as well as objects) may be recognized by projecting windows from newly acquired images into the descri..