Person: Banerjee, Asim
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Asim Banerjee
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Publication Metadata only Human action recognition using fusion of features for unconstrained video sequences(Elsevier, 01-08-2018) Patel, Chirag I; Garg, Sanjay; Zaveri, Tanish; Banerjee, Asim; Patel, Ripal; DA-IICT, GandhinagarEffective modeling of the human action using different features is a critical task for�human action recognition; hence, the fusion of features concept has been used in our proposed work. By fusing several modalities, features, or classifier decision scores, we present six different fusion models inspired by the early fusion schemes, late fusion schemes, and intermediate fusion schemes. In the first two models, we have utilized early fusion technique. The third and fourth models exploit intermediate fusion techniques. In the fourth model, we confront a kernel-based fusion scheme, which takes advantage of kernel basis of classifiers i.e.�Support Vector Machine�(SVM). In the fifth and sixth models, we have demonstrated late fusion techniques. The performance of all models is evaluated with ASLAN and UCF11 benchmark dataset of action videos. We obtained significant improvements with the proposed fusion schemes relative to the usual fusion schemes relative state-of-the-art methods.Publication Metadata only Top-Down and bottom-up cues based moving object detection for varied background video sequences(Wiley, 16-11-2014) Patel, Chirag I; Garg, Sanjay; Zaveri, Tanish; Banerjee, Asim; DA-IICT, GandhinagarMoving object detection is a crucial and critical task for any surveillance system. Conventionally, a moving object detection task is performed on the basis of consecutive frame difference or background models which are based on some mathematical aspects or probabilistic approaches. But, these approaches are based on some initial conditions and short amount of time is needed to learn all these models. Also, the bottleneck in all these previous approaches is that they require neat and clean background or need to create a background first by using some approaches and that it is essential to update them regularly to cope with the illuminating changes. In this paper, moving object detection is executed using visual attention where there is no need for background formulation and updates as it is background independent. Many bottom-up approaches and one combination of bottom-up and top-down approaches are proposed in the present paper. The proposed approaches seem more efficient due to inessential requirement of learning background model and due to being independent of previous video frames. Results indicate that the proposed approach works even against slight movements in the background and in various outdoor conditions.Publication Metadata only Trigonometry-based motion blur parameter estimation algorithm(ICO, 01-06-2018) Gajjar, Ruchi; Zaveri, Tanish; Banerjee, Asim; Murthy, K V V; DA-IICT, GandhinagarNumerical simulations using the computational fluid dynamic software PHOENICS were performed. The most profitable orientation was with the condenser oriented back to the wind direction, a configuration that lowers the wind velocity near the foil due to the combination of free convection and wind recirculation flows.Publication Metadata only Image similarity based on intensity using mutual information(01-04-2013) Mistry, D; Banerjee, Asim; Tatu, Aditya; DA-IICT, GandhinagarPublication Metadata only Discrete wavelet transform using MATLAB(01-03-2013) Mistry, D; Banerjee, Asim; DA-IICT, GandhinagarPublication Metadata only Review: image registration(01-02-2012) Mistry, Darshana; Banerjee, Asim; DA-IICT, Gandhinagar