Anand Deshpande

Super-resolution for iris feature extraction

Super-resolution technique can be used to fix the low resolution problem for recognizing the iris at a distance. Two frequency domain super-resolution algorithms, Papoulis-Gerchberg (PG) and Projection onto Convex Sets, are implemented to increase the resolution of iris images. The performance analysis of these algorithms is carried out by extracting Gray Level Co-occurrence Matrix (GLCM) features of super-resoluted iris images. The super-resoluted iris region is normalized, extracted GLCM features and compared with the GLCM features of normalized original iris region. It has been observed that the GLCM features reconstructed images using above algorithm closely matches with that of original iris image. The error between the GLCM features of original normalized and normalized super-resoluted image using Papoulis-Gerchberg is less compared to that of Projection onto Convex Sets.

Modeling and optimization of furan molding sand system using design of experiments and particle swarm optimization

The present research work is focussed on establishing the complex nonlinear input–output relations of a furan resin-based molding sand system. Further, a set of input parameters, which will result in optimized mold properties, is determined. Grain fineness number, setting time, percentage of resin, and hardener are considered as process variables. Mold properties, such as green compression strength, shear strength, mold hardness, gas evolution, permeability, and collapsibility are treated as the process outputs. Nonlinear input–output relations have been developed and statistical analysis has been carried out by utilizing design of experiments, central composite design. Surface plots are developed to study and analyze the input–output relations. The input parameters that will result in best molding conditions and improve casting quality characteristics are determined by utilizing desirability function approach and multiple particle swarm optimization-based crowding distance (MOPSO-CD) techniques. The optimum value for the process variables namely grain fineness number, furan resin, hardener, and setting time are found to be equal to 55, 1.85, 1.2, and 60, respectively. The quality characteristics of the castings namely yield strength, ultimate tensile strength, hardness, density, and secondary dendrite arm spacing are found to improve by 14.03%, 15.08%, 14.14%, 12%, 2.22%, and 12.24%, respectively for the castings made in optimized molding sand conditions.

Super resolution and recognition of long range captured multi-frame iris images

In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)-based super-resolution approach, fuzzy entropy-based feature selector and neural network (NN) classifier. The framework uses linear kernel co-variance function in local patch-based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no-reference image quality assessment model, Gray level co-occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal-to-noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.

Super resolution based low cost vision system

Machine vision (MV) is the technology which provides camera based analysis of images for various applications such as automatic quality inspection, pattern recognition, process flow control and pattern classification. The machine vision system is expensive as it contains high resolution camera and lenses. The paper proposes an algorithm to develop a low cost web camera based vision system for screw thread inspection. The Bayesian super-resolution method is used to super-resolute the images captured using low resolution web cameras. The parameters such as major, minor and pitch diameters, depth and thread angles are measured by using the proposed dimension measurement method. The results of web camera based automatic inspection of major diameter, minor diameter, pitch diameter, thread and depth of hex lag screw thread shows an error of range 0.000 to 0.310 mm. The comprehensive experimental results reveal that the proposed approach is suitable for real-time high speed quality analysis in various industries.

Study of the Effect of Nano-silica Particles on Resin-Bonded Moulding Sand Properties and Quality of Casting

The cast quality in chemical bonded sand mould system is influenced primarily by sand mould properties such as, compression strength, permeability, gas evolution, and collapsibility. Amount of resin and hardener, curing time and number of strokes influence the sand mould properties. The experiments are conducted with the above mentioned input output, as per Taguchi’s L9 orthogonal array. Pareto analysis of variance is conducted to determine the percent contribution of inputs on output, individually. The optimal factor level is determined for each output separately. The conflicting requirements in foundry sand mould properties can be solved by multiple objective optimization. Principal component analysis is applied to determine the relative importance of individual output. Grey relational analysis is used to convert multiple objective functions to a single objective function for optimization task. Pareto analysis is utilized to determine the optimal input factor combination and their relative percent contribution towards moulding sand properties. The nano-silica particles are used as additive to enhance the moulding sand properties. The results have shown that, the nano-silica particles pose a remarkable improvement in sand mould properties and casting quality.

Feature Extraction and Fuzzy-Based Feature Selection Method for Long Range Captured Iris Images

Long range captured iris recognition system is a biometric system consisting of pattern recognition and computer vision. In the process of iris recognition, feature extraction and feature selection play a major role in increasing the recognition accuracy. This paper proposes feature extraction method using discrete cosine transform domain-based no-reference image quality assessment model, gray-level co-occurrence matrix, Hu seven moments, and statistical features. It also proposes fuzzy entropy and interval-valued fuzzy set measure-based feature selection method. The selected feature vectors are classified by neural network classifier. The model is tested with CASIA long range iris database. The recognition accuracy is compared with the results obtained without feature selection and existing feature selection methods. It has been observed that the fuzzy entropy method gives better classification accuracy than existing feature selection method. The results demonstrate that the proposed work is well suited to extract the features of iris polar images captured at a long distance and to reduce the dimensionality by selecting the useful features which increase the recognition accuracy .

Unconstrained Iris Image Super Resolution in Transform Domain

In this paper, a method for super resolution of unconstrained or long-range captured iris images in discrete cosine transform domain is proposed. This method combines iterated back projection approach with the Papoulis-Gerchberg (PG) method to super resolute the iris images in discrete cosine transform domain. The method is tested on CASIA long-range iris database by comparing and analyzing the structural similarity index matrix, peak signal-to-noise ratio, visual information fidelity in pixel domain, and execution time of bicubic, Demirel, and Nazzal state-of-the-art algorithms. The result analysis shows that the proposed method is well suited for super resolution of unconstrained iris images in transform domain.

Optimisation for geometrical dimension of a product using 3D printer based on fused deposition modelling

The product development and the modification of the existing product are fundamental aspects of innovation and competitiveness of organisations, supported by concurrent engineering approaches. In the present work, an attempt has been made to establish a set of the process parameters for fused deposition modelling type rapid prototyping machine using Taguchi method (L8 orthogonal array) to gain better geometrical dimensions of products. The product, viz., rapid prototyping machine, thus manufactured was used as a pattern for casting. Literature review, brainstorming sessions, cause and effect diagrams, and pilot experiments demonstrated that geometrical dimensions are influenced by four major input parameters, namely, shrinkage allowance, Acrylonitrile Butadiene Styrene (ABS) temperature at nozzle, raster angle, and bed temperature. Taguchi method was utilised to study and analyse the influence of input parameters on geometric dimensions. Further, Taguchi method was used to optimise the level of process parameters of rapid prototyping three-dimensional (3D) printers. The confirmation experiments are conducted by using the rapid prototyping 3D printer, for the determined optimum process parameter set helped to obtain the desired geometrical dimensions of patterns meant for casting applications.

Gaussian Process Regression based iris polar image super resolution

In this work, Gaussian Process Regression (GPR) based novel framework is proposed to super resolute the long range captured iris polar images. The framework uses linear kernel co-variance function in GPR during the process of super resolution of iris image, without external dataset. The new technique is proposed to reduce the time taken to super resolute the iris polar image patches. The framework is tested using benchmark images as well as CASIA long range iris database by comparing and analyzing the peak signal to noise ratio (PSNR) and structural similarity index matrix (SSIM) of proposed algorithms with the existing algorithms. Empirical results indicate that the proposed framework, which improves PSNR up to 36 dB and promotes structural similarity index measurement (SSIM) up to 0.92 in averages, is better than the other existing method. The results demonstrate that the proposed approach outperforms some of the state-of-the-art super resolution approaches.

SINGLE FRAME SUPER RESOLUTION OF NONCOOPERATIVE IRIS IMAGES

Image super-resolution, a process to enhance image resolution, has important applications in biometrics, satellite imaging, high definition television, medical imaging, etc. The long range captured iris identification systems often suffer from low resolution and meager focus of the captured iris images. These degrade the iris recognition performance. This paper proposes enhanced iterated back projection (EIBP) method to super resolute the long range captured iris polar images. The performance of proposed method is tested and analyzed on CASIA long range iris database by comparing peak signal to noise ratio (PSNR) and structural similarity index (SSIM) with state-of-the-art super resolution (SR) algorithms. It is further analyzed by increasing the up-sampling factor. Performance analysis shows that the proposed method is superior to state-of-the-art algorithms, the peak signal-tonoise ratio improved about 0.1-1.5 dB. The results demonstrate that the proposed method is well suited to super resolve the iris polar images captured at a long distance.