A Short Review Of Neural Network Techniques In Visual Servoing Of Robotic Manipulators.

Robotics is one of the most challenging applications of soft computing techniques. It is characterized by direct interaction with a real world, sensory feedback and a complex control system

Skin Lesion Segmentation using Deep Hypercolumn Descriptors

We present a image segmentation method based on deep hypercolumndescriptors which produces state-of-the-art results for thesegmentation of several classes of benign and malignant skin lesions.We achieve a Jaccard index of 0.792 on the 2017 ISIC SkinLesion Segmentation Challenge dataset

Segmentation Using Wavelet And GVF Snake.

The Gradient Vector Flow (GVF) snake is a popular technique to segment object in image processing. Its advantages arc insensitivity to contour initialization and its ability to deform into highly concave part of the object compared to other deformable contour models

Automated Extraction Of Small Structures In Medical Images Based On Multi Scale Approach.

Multi scale techniques coupled with active contours have been widely used to locate the boundaries of structures in noisy images. Significant fine structures have been emphasized through appropriate scale selection

Deformable Boundary Initialization For Object Detection In Natural Images Using, Multiple Scale Edges.

The deformable contour model is a popular technique to segment object in image processing. Its applications range from edge and curve detection, to shape modeling and visual tracking

Comparison Of HSRNAFold and RNAFold Algorithms for RNA Secondary Structure Prediction.

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function

Textured Renyl Entropy for Image Thresholding

This paper introduces Textured Renyi Entropy for image thresholding based on a novel combination mechanism

Feature selection via dimensionality reduction for object class recognition

This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset. Experimental results show that the feature selection algorithms are able to retain the most relevant and discriminant features, while maintaining recognition accuracy and improving model building time