Unlike the conventional feedforward neural network, an emergent learning technique—which we call extreme learning machine (ELM)—provides a generalized performance of neural network with less user intervention and comparatively faster training. We study ELM with five different activation functions, sigmoidal, sine, hard limiter, triangular basis, and radial basis, for handwritten Indic script identification in multiscript documents. To describe scripts, both script dependent and independent features are computed. For validation, a dataset of 3300 handwritten line-level document images (300 samples per script) of 11 official Indic scripts is used. In our study, we observe that the sigmoidal activation function performs the best regardless of the number of scripts used, i.e., script identification cases: biscript, triscript, and multiscript.
Presence of foreign objects (buttons, medical devices) adversely impact the performance of the automated chest X-ray (CXR) screening. We present a novel image processing and machine learning technique to detect circle-like foreign elements in CXR images that helps avoid confusions in automated detection of abnormalities, such as nodules and other calcifications. In our technique, we apply <strong>normalized cross-correlation</strong> using a few templates to collect potential circle-like elements and <strong>unsupervised clustering</strong> to make a decision. We validated our fully automatic technique on a set of 400 publicly available images hosted by LHNCBC, U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). Our method achieved an accuracy greater than 90% and outperforms existing techniques that are reported in the literature.