Mita Nasipuri

TL;DR: A methodology where local regions of varying heights and widths are created dynamically and genetic algorithm (GA) is applied on these local regions to sample the optimal set of local regions from where an optimal feature set can be extracted that has the best discriminating features.

TL;DR: The experimental results show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentsation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.

TL;DR: This work proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images using ICIAR 2018 breast histology image dataset which comprises of 4 different classes namely normal, benign, in situ and invasive cancer.

TL;DR: A new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals is introduced and it has been observed that MPCA+QTLR feature combination outperforms PCA+QTB feature combination and most other conventional features available in the literature.

TL;DR: This paper has described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangle text mixed with English words, which is the first handwritten database in this area available as an open source document.