Object-shape recognition and 3D reconstruction from tactile sensor images.

This article presents a novel approach of edged and edgeless object-shape recognition and 3D reconstruction from gradient-based analysis of tactile images. We recognize an object's shape by visualizing a surface topology in our mind while grasping the object in our palm and also taking help from our past experience of exploring similar kind of objects. The proposed hybrid recognition strategy works in similar way in two stages. In the first stage, conventional object-shape recognition using linear support vector machine classifier is performed where regional descriptors features have been extracted from the tactile image. A 3D shape reconstruction is also performed depending upon the edged or edgeless objects classified from the tactile images. In the second stage, the hybrid recognition scheme utilizes the feature set comprising both the previously obtained regional descriptors features and some gradient-related information from the reconstructed object-shape image for the final recognition in corresponding four classes of objects viz. planar, one-edged object, two-edged object and cylindrical objects. The hybrid strategy achieves 97.62 % classification accuracy, while the conventional recognition scheme reaches only to 92.60 %. Moreover, the proposed algorithm has been proved to be less noise prone and more statistically robust.

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.

The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.