Journal of Intelligent & Fuzzy Systems - Volume 36, issue 3

Authors: Jeena, R.S. | Sukesh Kumar, A. | Mahadevan, K.

Article Type: Research Article

Abstract: Stroke is a cerebrovascular disease which is one of the significant causes of adult impairment. Research shows that retinal fundus images carry vital information for the prediction of various cardiovascular diseases like Stroke. This work investigates a multi-texture description for the computer aided diagnosis of Stroke from retinal fundus images. Texture of the retinal background is analyzed, thereby eliminating the need for segmentation. Gabor Filter (GF), Local Binary Pattern (LBP) and Histogram of Oriented gradients (HOG) are the texture descriptors implemented in this work. The texture descriptors are applied to the second Eigen channel obtained by Principal Component Analysis (PCA). …Extracted features are concatenated to form a multi-texture representation and dimensionality reduction is done by ReliefF feature selection method. The compact feature vector is given to Naïve Bayes classifier and performance metrics are evaluated. We have evaluated the performance of individual feature descriptors and multiple feature descriptors in retinal fundus images for stroke diagnosis. Multi-texture description outperforms individual texture descriptors by an accuracy of 95.1 %. Show more

Keywords: Stroke, Gabor filter, local binary pattern, histogram of oriented gradients, principal component analysis, ReliefF

DOI: 10.3233/JIFS-169914

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2025-2032, 2019

Authors: Abirami, S.P. | Kousalya, G. | Karthick, R.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is increasing rapidly at higher rate which has a greater impact in many organizations to contribute to ASD coaching and training. The highest complexity lies in the factor of early identification of ASD to support training. Many researchers have proved that early identification of autism and appropriate coaching to children with high ASD can result in a quality improvement in child’s lifestyle. This early identification of autism can be screened through stages involving evaluation of eye gaze, emotion, expression, linguistic ability, responsiveness. Objective of this paper mainly focuses on analyzing the facial expression of children with …autism in a contact less environment as the children could even respond to the target object they face. The paper identifies the various facial expressions of autism children excluding the time and event occurrence. These expressions are used as an early screening method to identify children who may fall under autistic characteristic in the near future. Moreover the facial expressions could be analyzed in a live video environment as stimuli emotional sequence that further leads to next level of screening. The paper also analyses the major facial expression perceived by the children along with the variation in facial expression dynamics that a normal Toddler (TD) possess. Show more

Keywords: Autism spectrum disorder, early identification, classification, feature identification, facial expression

DOI: 10.3233/JIFS-169915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2033-2042, 2019

Authors: Navdeep, | Goyal, Sonal | Rani, Asha | Singh, Vijander

Article Type: Research Article

Abstract: Local Binary Pattern (LBP) is considered as an effective image descriptor as it is based on joint distribution of gray level differences. The main attributes of LBP are discriminatory power, robustness to brilliance change, simplicity and computational efficiency. In contrary LBP is highly sensitive to noise, rotation, non-rigid deformation, view point variations and scaling. Therefore, in the present work an improved version of LBP i.e. ILBP is proposed to overcome the limitations of basic LBP. ILBP replaces the fixed-weighted matrix of basic LBP by a pixel difference matrix. The proposed method is assessed on synthetic as well as real-time images. …The results obtained are compared with LBP and other state-of-the-art edge detection techniques like HLBP, Canny and Sobel methods. The results reveal that performance of ILBP is superior to other edge detection methods under consideration. Further the proposed technique is highly efficient for noisy, blurred and low pixel valued images. Show more

Keywords: Digital radiography imaging, edge extraction, LBP, Canny, Sobel

DOI: 10.3233/JIFS-169916

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2043-2054, 2019

Authors: Mayaluri, Zefree Lazarus | Gupta, Supratim

Article Type: Research Article

Abstract:  Accurate detection and localization of eye features under spectacles - is quite a relevant but challenging problem in the application field of Human-Computer Interactive (HCI) systems. The ill-effects caused by the usage of spectacles like occlusion, glare and secondary reflection formation are termed as “The Spectacle Problem.” In this paper, the authors alleviate the spectacle problem by employing a two-image based data fusion approach. Detail-preserving filters like Joint Bilateral Filter (JBF) and Guided Image Filter (GIF) are compared individually, to find out the suitability and consistency of the filters in the proposed data fusion approach. Experimentation on CASIA NIR-VIS 2.0 …and self-generated facial database demonstrate that GIF based filtering approach has higher local and global eye feature preservation capability while mitigating the spectacle problem. Show more

Keywords: Spectacle problem, image fusion, glare-reflection removal, detail preserving filters, eye detection

DOI: 10.3233/JIFS-169917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2055-2065, 2019

Authors: Jacob, Naveen Varghese | Sowmya, V. | Soman, K.P.

Article Type: Research Article

Abstract:  Hyperspectral Image (HSI) store the reflectance values of a single scene or object in several continuous bands of electromagnetic spectrum. When the image is recorded, the information in some of the spectral bands gets mixed with noise. The classification accuracy of hyperspectral image varies inversely with the quantity and nature of noise present in the cluster of spectral bands. Thus, denoising is a fundamental prerequisite in image processing applications like classification, unmixing, etc. In this paper, we compare the effect of denoising via classification using Vectorized Convolutional Neural Network (VCNN), kernel based Support Vector Machine (SVM) and Grand Unified Regularized …Least Squares (GURLS) classifiers. The classifiers are provided with raw data (without denoising) and denoised data using spectral and spatial Least Square (LS) techniques. The data given to the network are in the form of pixels, so we call the convolutional neural network (CNN) as VCNN. The experiments are performed on three standard HSI datasets. The performance of the classifiers are evaluated based on overall and class-wise accuracy. Show more

Keywords: Hyperspectral Image, CNN, GURLS, LIBSVM, Least Square Denoising, IBBC

DOI: 10.3233/JIFS-169918

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2067-2073, 2019

Authors: George, Koshy | Vishnukumar, S.

Article Type: Research Article

Abstract:  The need for understanding the terrain or conditions of large areas aerially has gained prominence as the aerial images provide a near clear coverage of the area under study. Individual image provides just a portion of the area, thus to understand the whole area, mosaicking or stitching of these images is needed. Image mosaicking aids in providing with a ”Big Picture” as an outcome by joining the images taken during the flight. In this paper we propose a method which aims at generating a seamless aerial mosaick using only the images captured by the UAV as input. This involves identifying …candidate images from the images captured by the UAV periodically during its flight and stitching the images together. This method evaluates various feature descriptors and feature matching techniques that can be integrated into the mosaicking system. The proposed work is a hybrid approach that uses the Scale Invariant Feature Transform (SIFT) for feature extraction and the key features are matched using the Fast Library for Approximate Nearest Neighbors (FLANN). RANdom Sample Consensus (RANSAC), is used for the removal of features that are redundant or act as outliner, providing candidates for Homography estimation. This is followed by image stitching that involves the use of Multi-Band Blending to produce a visually seamless mosaick. The results obtained were evaluated for quality using Universal Quality index Measure (QIM) and is found to be perfect. Show more

Keywords: Mosaicking, UAV, SIFT, FLANN, RANSAC, Homography, Multi-band Blending

DOI: 10.3233/JIFS-169919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2075-2083, 2019

Authors: Vamshi Durgam, K.K. | Shanmugha Sundaram, G.A.

Article Type: Research Article

Abstract:  Real-time occupant posture tracking information has significant value in ADAS equipped vehicles to enable better safety and mitigate risks of injuries to occupants within vehicular cabin during sudden deceleration due to abrupt braking maneuvers or crash scenarios. This information will be helpful for timely activation of airbags and various other conventional safety restraints that provide safety and mitigate injuries. Here, a proximity sensing system is proposed that uses capacitive electrodes to acquire the occupant posture and motion data. These electrodes are deployed as an array placed along three orthogonal sensing axes such as in the seats, along the roof and …dashboard, and along the door panel. Motion detection cameras and other sensors like ultrasound or infrared sensors have line of sight issue, while the accumulation of dirt would become a problem for sensing the data accurately. A prototype hardware has been implemented and the proximity capacitance data was acquired for discrete distances from 0.1 to 0.8 m, in the three electrode orientations. Applying optimization and curve fitting techniques on this data, derived data sets were then obtained, that mimic a typical crash-test dummy behavior during impact. The resultant algorithm can offer a precise localization estimate of the occupant with respect to an electrode layout along the roof, seat and door orientations, and hence classify the occupant posture inside the vehicular cabin. Show more

Keywords: Capacitive proximity sensors, Crash-test dummy profile, Localization, ADAS

DOI: 10.3233/JIFS-169920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2085-2094, 2019

Authors: Nair, Preethi S. | Rao, K.R. | Nair, Madhu S.

Article Type: Research Article

Abstract:  The state-of-the-art video compression standard, High Efficiency Video Coding (HEVC) has standardised to contemplate a better compression of high and ultra high resolution videos. HEVC introduced a lot of new coding tools to meet its improved coding efficiency, but at a bit increase in the computational cost. The intra prediction mode decision strategy is one among them and the use of a large number of intra prediction modes at different PU sizes is the reason for an improved mode prediction as well as the high computational complexity. Besides the number of prediction modes, HEVC adopts a Rough Mode Decision (RMD) …process as well as Rate Distortion Optimization (RDO) process in the intra prediction stage, which takes substantial amount of execution time. Hence a method to reduce this time complexity by incorporating machine learning technique in RMD process is proposed in this work. In this method, we use Support Vector Machine (SVM) to find out the best mode in the RMD stage. Experimental results indicate that the proposed method considerably reduces the computational cost of the HEVC reference software while retaining the visual quality of videos. Since HEVC supports real-time video processing, the proposed method sounds to be applicable. Show more

Keywords: HEVC, Intra prediction, RMD, RDO, SVM

DOI: 10.3233/JIFS-169921

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2095-2106, 2019

Authors: Vasudevan, Shriram K. | Abhishek, S.N. | Kumar, Vignesh | Aswin, T.S. | Nair, Prashant R.

Article Type: Research Article

Abstract: Mathematics is the cradle of all creations, without which the world cannot move an inch. Mathematical functions are ‘extensively’ used in physics, ‘structurally’ used in graphics, ‘practically’ used in civil engineering, ‘potentially’ used in mechanical engineering and in many other fields as well. One fairly common difficulty faced by the engineers and scientists is to find the right function that solves their problem. This involves a lot of time consuming tasks. The idea proposed here is an android application that captures the mathematical expression using a built-in camera which produces a java code that can be utilized for solving the …inputs of their needs. Along with the code, it also displays a basic plot of the function, which will be more helpful for selecting the apt solution for any problem. There are many applications in the market which can compute the result of a mathematical equation. But, this application will give an executable java code which can be used for further problem solving. So, the application is unique in its way and this is a new dimension of using image processing for code generation. This application supports polynomial, logarithmic, trigonometric and exponential functions up to four variables. Show more

Keywords: Optical character recognition, mathematical equation, code generation, Plot, equation solving

DOI: 10.3233/JIFS-169922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2107-2116, 2019

Authors: Venkatesh, Veeramuthu | Raj, Pethuru | Kannan, K. | Balakrishnan, P.

Article Type: Research Article

Abstract: Human activity recognition emerges as one of the prominent research areas in the recent past. However, the activity recognition still encounters many challenges like reliability of sensor data and accuracy of prediction that severely affects the aspect of decision making. In this paper, a futuristic framework has been proposed and experimented to build a precision-centric activity recognition method by analyzing the data obtained from Environment Monitoring System (EMS) and Personalized Positions Detection System (PPDS) using machine learning methods such as AdaBoost, Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). Further, the proposed approach utilizes the Dempster-Shafer Theory (DST)-based complete …sensor data fusion thereby improving the global activity recognition performance. Finally, the proposed approach is validated using a real-world dataset obtained from UCI machine learning repository. The results conclude that the proposed activity recognition framework outperforms its existing context/situation-awareness approaches in terms of reliability, efficiency, and accuracy. Show more

Keywords: Activity recognition, machine learning, data-fusion, feature extraction, classifier, boosting

DOI: 10.3233/JIFS-169923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2117-2124, 2019