Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Experimental and theoretical evidence for molecular forces driving surface segregation in photonic colloidal assemblies

Surface segregation in binary colloidal mixtures offers a simple way to control both surface and bulk properties without affecting their bulk composition. Here, we combine experiments and coarse-grained molecular dynamics (CG-MD) simulations to delineate the effects of particle chemistry and size on surface segregation in photonic colloidal assemblies from binary mixtures of melanin and silica particles of size ratio (Dlarge/Dsmall) ranging from 1.0 to similar to 2.2. We find that melanin and/or smaller particles segregate at the surface of micrometer-sized colloidal assemblies (supraballs) prepared by an emulsion process. Conversely, no such surface segregation occurs in films prepared by evaporative assembly. CG-MD simulations explain the experimental observations by showing that particles with the larger contact angle (melanin) are enriched at the supraball surface regardless of the relative strength of particle-interface interactions, a result with implications for the broad understanding and design of colloidal particle assemblies

Embed System for Robotic Arm with 3 Degree of Freedom Controller using Computational Vision on Real-Time

This Paper deals with robotic arm embed controller system, with distributed system based on protocol communication between one server supporting multiple points and mobile applications trough sockets .The proposed system utilizes hand with glove gesture in three-dimensional recognition using fuzzy implementation to set x,y,z coordinates. This approach present all implementation over: two raspberry PI arm based computer running client program, x64 PC running server program, and one robot arm controlled by ATmega328p based board.Comment: 8 pages,9 figures, published on AIFL 2014 conference (AIFL-2014 Submission 20

Goldilocks and the Three Parameters:Empirically Finding the "Just Right" for Segmenting Food Images for the AFINI-T System

Measuring nutritional intake is a tool that is critical to themonitoring of health, both as an individual or of a group. It isespecially important in the monitoring of those at risk formalnutrition, an issue which costs billions of dollars globally, andcurrent methods used in practice are manual, time-consuming,and have inherent biases and inaccuracies. This study proposes anovel imaging system with a superpixel-based segmentationalgorithm as part of an automated nutritional intake system. Thestudy also examines three important parameters of the algorithmand their ideal values; region size and spatial regularization forsuperpixel segmentation, as well as spatial weighting inclustering. The experimental results demonstrate that theproposed system is effective in segmenting an image of a plate intoits constituent foods

Dendritic spine shape classification from two-photon microscopy images (Dendritik diken şekillerinin iki foton mikroskopi görüntüleri kullanılarak sınıflandırılması)

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly