Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Jayant V. Kulkarni is active.

Publication


Featured researches published by Jayant V. Kulkarni.


Pattern Recognition | 2006

Rapid and brief communication: Orientation feature for fingerprint matching

Jayant V. Kulkarni; Bhushan D. Patil; Raghunath S. Holambe

This paper describes a fingerprint verification algorithm based on the orientation field. The orientation field of a fingerprint image has also been used for image alignment. Area around the core point has been employed as an area of interest for determining the orientation feature map. The algorithm has been tested on two databases (database available from University of Bologna, Biometrics Laboratory and FVC2002). The performance of the algorithm is measured in terms of receiver operating characteristics (ROC). For the University of Bologna database, at ~0% false acceptance rate (FAR) the genuine acceptance rate (GAR) observed is ~78% and at ~11% FAR, GAR is ~97%. For the FVC2002 database at ~0% FAR the GAR observed is 75% and at ~18% FAR, GAR is 93%. Proposed algorithm yields better GAR at low FAR with reduced computational complexity. Because of simplicity in computations the algorithm can be easily implemented as an embedded automatic fingerprint identification system (AFIS).


international conference on electrical and control engineering | 2012

Optimized MFR & automated local entropy thresholding for retinal blood vessel extraction

Saumitra Kumar Kuri; Sanika S. Patankar; Jayant V. Kulkarni

Retinal blood vessel extraction plays important role in diagnosis of many diseases such as diabetic retinopathy (DR), hypertension, glaucoma and arteriosclerosis. In this paper optimized matched filter response is used to enhance the blood vessel followed by local entropy thresholding used to segment the vessels automatically. First optimized matched filter are applied to the retinal images to enhance vessels then we used their corresponding co-occurrence matrix & automatic to find local entropy thresholding that used for segmented the blood vessel in retinal image. The results shows that automated local entropy thresholding more successful compare to other methods in our proposed matched have 95.86 % average accuracy.


advances in computing and communications | 2015

Features based classification of hard exudates in retinal images

Anup V. Deshmukh; Tejas G. Patil; Sanika S. Patankar; Jayant V. Kulkarni

Diabetes mellitus is a major disease spread all across the globe. Long-time diabetes mellitus causes the complication in the retina called Diabetic Retinopathy (DR), which results in visual loss and sometimes blindness. In this paper, we discuss a simple and effective algorithm for segmentation of the optic disk (OD) and bright lesions such as hard exudates from color retinal images. Color fundus images are enhanced using brightness transform function. Morphological operator along with the Circular Hough Transform (CHT) is used for optic disk segmentation. Further, local mean and entropy based region growing technique is applied in order to classify exudate - non-exudate pixels in retinal images. The performance of the proposed algorithm has been tested on publicly available standard Messidor database images with varied disease levels and non-uniform illumination. Experimentation yields 94% success rate for localization of the optic disk, 99% accuracy of classification of exudate - non-exudate pixels and subject level accuracy is found to be 93% and 67% in identifying the abnormal (with exudates) and normal (without exudates) images respectively.


2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) | 2013

Gear tooth fault detection by autoregressive modelling

Neeta K. Nikhar; Sanika S. Patankar; Jayant V. Kulkarni

Gears are important element in a variety of industrial applications. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. This paper present a gear tooth fault diagnosis technique of Autoregressive (AR) modeling of vibration signals. AR model coefficient is been determined by Yule-Walker equation with Levision-Durbin recursive algorithm. The model order is an essential part and is calculated by Akaike Information Criteria. The vibration signal of normal and faulty gear is been modeled and frequency response of AR model of the faulty gear is been compared with the AR model of the normal gear. The changes in the frequency spectrum indicate the fault.


international conference on computational intelligence and computing research | 2012

Condition monitoring of rotary machinery using Continuous Wavelets

Soudeh. H. Yaghouti; Sanika S. Patankar; Jayant V. Kulkarni

Condition monitoring has significant importance in manufacturing industry. Avoiding production loss and minimizing the probability of occurance of calamitous machine failure, based on updated information acquired from machine status on-line is the aim of condition monitoring. This paper discusses various vibration signal analysis techniques. The experimentation has been carried out using a mechanical setup consisting of rotary machine. The setup has a provision of introducing fault (uncertainty) by way of using gear with broken tooth. The effect of uncertainty (introduced in the vibration signal because of gear with broken tooth) is analyzed using Fourier Transform and Continuous Wavelet Transform (with Daubechies having three vanishing moments and Mexican Hat basis functions). From the experimental results, it is observed that the uncertainty due to broken tooth has been significantly detected by Continuous Wavelet Transform using Mexican Hat basis function as compared to Fourier Transform.


ieee india conference | 2015

Intensity features based classification of hard exudates in retinal images

Anuj C. Somkuwar; Tejas G. Patil; Sanika S. Patankar; Jayant V. Kulkarni

A major cause of blindness is diabetic retinopathy, which is found in the people who suffer from diabetes, which can be detected through a screening process. Hard exudates are one of the signs of diabetic retinopathy, which caused due to breakdown of retinal blood vessels. This paper presents a method for classification of hard exudates using 6-Dimensional intensity based features. The exudates and non-exudates (background) classification is performed using the Euclidean distance classifier. The proposed method is tested against publicly available databases such as DIARETDB1, e-ophtha EX, MESSIDOR. The proposed algorithm demonstrates maximum subject level accuracy of 96.92% on DIARETDB1.


International Journal of Computer Applications | 2015

Automated Segmentation of Retinal Blood Vessels using Optimized Gabor Filter with Local Entropy Thresholding

Saumitra Kumar Kuri; Jayant V. Kulkarni

Blood vessel in retinal image plays a vital role in medical diagnosis of many diseases. Diabetic retinopathy is one of the diseases which damages the retina and leads to blindness. Segmentation of blood vessels is helpful for ophthalmologists and this paper presents a new automatic method to extract blood vessels with high accuracy. This algorithm is comprised of optimized Gabor filter with local entropy thresholding for vessels segmentation under various normal or abnormal conditions. The frequency and orientation of Gabor filter are tuned to match that of a part of blood vessels to be enhanced in a green channel image. Segmentation of blood vessels pixels are classified by local entropy thresholding technique in this method. The performance of the proposed algorithm is evaluated by MATLAB software with DRIVE database. General Terms Bio-informatics, Computer Aided Diagnosis system


international conference on applied and theoretical computing and communication technology | 2016

Bearing fault diagnosis using discrete Wavelet Transform and Artificial Neural Network

Aditi B. Patil; Jitendra A. Gaikwad; Jayant V. Kulkarni

Rotating machinery has vast industrial applications in fields of petroleum, automotive, HVAC and food processing. Rotating machineries use bearings to perform rotational or linear movement of various subcomponents while reducing friction and stress. Compared other types of bearing, REBs offer a good balance of key attributes like friction, lifetime, stiffness, speed and cost. Hence, real-time monitoring and diagnosis of bearings is crucial to prevent failures, improve safety, avoid unforeseen downtime of production assembly lines and lower cost. We propose an approach based on Wavelet Transform and ANN for analysis of vibration signals from a rolling element bearing to identify and multi-classify its component defects. The vibration signals from the REB being analyzed are passed over to the software setup consisting of Wavelet Transform and ANN. To remove noise and extract the relevant features from this signal, we pass the vibration signal through a Wavelet transform. These features are retrieved using time domain parameters like Skewness, Kurtosis, RMS and Crest Factor and they are used as an input for ANN classifier. The role of the ANN is to classify the bearing fault features produced by the Wavelet Transform and identify bearing faults, if any. To this end, we have designed a feedforward topology ANN using the sigmoid transfer function. The ANN training methodology uses three learning paradigms - namely, Levenberg-Marquardt, Resilient Back-propogation and Scaled Conjugate method. The learning models generated by each algorithm are tested to find the one which gives better accuracy. The outcome of this experiment indicates that DWT and ANN can together achieve good accuracy and reliability in detection and classification of bearing faults.


ieee international conference on recent trends in electronics information communication technology | 2016

Application of multi-scale fuzzy entropy for roller bearing fault detection and fault classification based on VPMCD

Priyanka Mehta; Jitendra A. Gaikwad; Jayant V. Kulkarni

Roller bearing is an integral component in various types of rotating machinery. Bearing fault detection is very important to prevent failure, increase safety, reduce production idle time and decrease maintenance cost. In this paper, Multi-scale Fuzzy Entropy(MFE) is used for fault detection of roller bearing and Variable predictive model-based class discrimination (VPMCD) is used as multi-fault classifier. Fuzzy entropy is calculated for complexity measure of time series constructed from motor vibration signal. Usually, as vibration signals tend to be non-linear, fuzzy entropy calculated for single scale may not contain all the fault information. Hence it is essential to calculate entropy for multiple scales. As a multi-fault classifier VPMCD has been used to classify bearing faults. Fault features created using MFE are used as an input for VPMCD classifier. VPMCD is applied here for roller bearing fault classification. The effect of motor rotational speed on the MFE values is investigated. Experimental analysis is conducted to evaluate performance of this method. The results of this experiment indicate that MFE and VPMCD together can achieve good accuracy and reliability in bearing fault detection and classification.


Computer Methods and Programs in Biomedicine | 2015

Orthogonal moments for determining correspondence between vessel bifurcations for retinal image registration

Sanika S. Patankar; Jayant V. Kulkarni

Retinal image registration is a necessary step in diagnosis and monitoring of Diabetes Retinopathy (DR), which is one of the leading causes of blindness. Long term diabetes affects the retinal blood vessels and capillaries eventually causing blindness. This progressive damage to retina and subsequent blindness can be prevented by periodic retinal screening. The extent of damage caused by DR can be assessed by comparing retinal images captured during periodic retinal screenings. During image acquisition at the time of periodic screenings translation, rotation and scale (TRS) are introduced in the retinal images. Therefore retinal image registration is an essential step in automated system for screening, diagnosis, treatment and evaluation of DR. This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features for determining the correspondence between the dominant points (vessel bifurcations) in the reference and test retinal images. As orthogonal moments are invariant to TRS; moment invariants features around a vessel bifurcation are unaltered due to TRS and can be used to determine the correspondence between reference and test retinal images. The vessel bifurcation points are located in segmented, thinned (mono pixel vessel width) retinal images and labeled in corresponding grayscale retinal images. The correspondence between vessel bifurcations in reference and test retinal image is established based on moment invariants features. Further the TRS in test retinal image with respect to reference retinal image is estimated using similarity transformation. The test retinal image is aligned with reference retinal image using the estimated registration parameters. The accuracy of registration is evaluated in terms of mean error and standard deviation of the labeled vessel bifurcation points in the aligned images. The experimentation is carried out on DRIVE database, STARE database, VARIA database and database provided by local government hospital in Pune, India. The experimental results exhibit effectiveness of the proposed algorithm for registration of retinal images.

Collaboration


Dive into the Jayant V. Kulkarni's collaboration.

Top Co-Authors

Avatar

Sanika S. Patankar

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Jitendra A. Gaikwad

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Tejas G. Patil

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Pramod Kanjalkar

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

S. M. Deokar

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Vijaykumar R. Bhanuse

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Pradeep M. Patil

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

S. S. Patankar

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Saumitra Kumar Kuri

Vishwakarma Institute of Technology

View shared research outputs
Top Co-Authors

Avatar

Suresh N. Mali

Sinhgad Institute of Technology

View shared research outputs
Researchain Logo
Decentralizing Knowledge