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We analyze simple everyday actions with a view to developing strategies that an intelligent robot can use to perform these same actions. The domain of tasks studied are in the class of simple machine type of actions involving hand tools. The tool is assumed to be composed of two principal geometric primitives that serve as the handle and the output end respectively. A task is modeled as an operation on a target object by the tool. This desired effect determines a motion trajectory for the output end of the tool. The decisions on grasp location and orientation are made based on the handle motions computed above. In addition to planning grasps and manipulations, we also formulate strategies for recognizing such tools. Tool recognition (from visual input) is based on the geometric information extracted. All objects in a scene are segmented into volumetric primitives. The primitives are then analyzed for their suitability to participate in the required task. Different primitives are ranked according to these criteria and the most suitable object is chosen to function as the tool.

We present here the development of a texture-like measure to aid the quantification of rock face stability using two familiar transforms in a novel combination. It is shown that the Fourier and Hough transforms together can yield accurate quantitative information relating to the texture of an image. With respect to rock faces, the textural quality of the image is a direct measure of the stability index, since the orientation, distribution, and number of fissures indicate its stability. Stability of rock faces for mining operations is currently estimated manually, prior to further excavation. Manual inspection is often undesirable due to the subjective nature of, and potential hazard to, the human inspector. This provides the motivation to develop an automated system which can survey the scene via some sensors and process the resulting data to compute a preliminary stability index before further detailed inspection and subsequent excavation. We present in this paper experimental results from real images of local rock faces that demonstrate the viability of this technique.

There has been an increasing interest in the study of video based fire detection algorithms as video based surveillance systems become widely available for indoor and outdoor monitoring applications. Although many video based smoke-detection algorithms have been developed and applied in various experimental or real life applications, but the standard method for evaluating their quality has not yet been proposed. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. In this project, the wavelet support vector machine (WSVM)-based model is used for Wild fire detection (WFD). Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. The new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM). Moreover , the proposed model utilizes the principle of wavelet analysis to facilitate nonlinear characteristic extraction of the image data. To reduce misclassification due to fog, an efficient fog removal scheme using adaptive normalization method.