Suranjana Samanta

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Domain adaptation (DA) is the process in which labeled training samples available from one domain is used to improve the performance of statistical tasks performed on test samples drawn from a different domain. The domain from which the training samples are obtained is termed as the source domain, and the counterpart consisting of the test samples is termed(More)
Face Recognition (FR) in surveillance scenarios has attracted the attention of researchers over the last few years. The bottleneck as a large gap in both resolution and contrast between training (high-resolution gallery) and testing (degraded, low quality probes) sets, must be overcome using efficient statistical learning methods. In this paper, we propose(More)
There is an increasing need for automatically segmenting the regions of different landforms from a multispectral satellite image. The problem of Landform classification using data only from a 3-band optical sensor (IRS-series), in the absence of DEM (Digital Elevation Model) data, is complex due to overlapping and confusing spectral reflectance from several(More)
Over the last few years, a few researchers have made attempts to bridge the gap between Training (high-resolution gallery) and Testing (degraded, low quality probes) sets for Face Recognition under the surveillance conditions, using efficient low-level processing and statistical learning methods. In this paper, this challenging task of FR in degraded(More)