The quality of user interactions in virtual environments influence the richness of experiences derived. In many virtual environments, interactions are captured by external visual sensor(s) that observe the environment. This requires end user tracking, and extraction of interaction -a task neither scalable, nor easy to accomplish.We suggest associating a sensor with the interaction so that the human computer interaction information extraction becomes less difficult. We term this paradigm as sensor on activity, and we exhibit this paradigm in a scalable multi-user virtual shooting environment built using off-theshelf components. Here, a sensor (camera) is attached to the activity (shooting). Tracking and inferring position and orientation of weapon, as in traditional setups, is not required any more. Our system is able to support firing at video frame rates. As the sensor need not be pre-configured, the virtual environment does not require instrumentation. Hence it can be easily ported to different locations and is accessible to masses.

Land use land cover changes (LULCC) are generally modeled using multi-scale spatio-temporal variables. Recently, Markov Chain (MC) has been used to model LULCC. However, the model is derived from the proportion of LULCC observed over a given period and it does not account for temporal factors such as macroeconomic , socioeconomic , etc. In this paper, we present a richer model based on Hidden Markov Model (HMM), grounded in the common knowledge that economic, social and LULCC processes are tightly coupled. We propose a HMM where LULCC classes represent hidden states and temporal factors represent emissions that are conditioned on the hidden states. To our knowledge, HMM has not been used in LULCC models in the past. We further demonstrate its integration with other spatio-temporal models such as Logistic Regression. The integrated model is applied on the LULCC data of Pune district in the state of Maharashtra (India) to predict and visualize urban LULCC over the past 14 years. We observe that the HMM integrated model has improved prediction accuracy as compared to the corresponding MC integrated model.

Defocus blur correction for projectors using a camera is useful when the projector is used in ad hoc environments. However, past literature has not explicitly considered the common situation when the projection surface includes a corner made up of two planar surfaces that abut each other, such as the ubiquitous office cubicle.In this paper, we advance the state of the art by demonstrating defocus correction in a non-parametric setting. Our method differs from prior methods in that (a) the luminance and chrominance channels are independently considered, and (b) a sparse sampling of the surface is used to discover the spatially varying defocus kernel.

Projectors are deployed in increasingly demanding environments. The fidelity of the projected image as seen by a user is compromised when projectors are deployed in dual-planar environments (e.g. corner of a room or an office cubicle), thereby diminishing the richness of the user experience. There are many reasons for this. The focus of this paper is to compensate for the global illumination effects due to inter-reflection of light. In the process we also correct geometry distortion. Our system is built from off-the-shelf components and easily deployable without any elaborated setup. In this paper, we describe two complementary methods to compensate for global illumination effects in dual-planar environments. Our methods are based on the systematic adaptation and interpretation of the classical radiosity equation in the image domain. The technique neither assumes nor computes 3D scene geometry, relying on an implicit inference. The system is calibrated in an off-line mode once; in our first method, corrected images and video are computed in real time, and in our second method, a richer scene is offered with a modest increase in computational time. The corrected images when projected have better contrast and are more appealing to the user.