Gaurav Goel ¹, Maitreyee Dutta ², Sudhir Goswami ³

Affiliations
1 IT Department, MIET, Meerut - 250005, Uttar Pradesh, IN
2 CSE Department, NITTTR, Chandigarh - 160019, Punjab, IN
3 CSE Department, MIET, Meerut - 250005, Uttar Pradesh, IN

Abstract

It has been observed that characteristics of underwater images are similar to day hazy images under poor lighting situation. So the problem of image visibility and color cast for underwater scenes is still challenging and demands more research attempts in this direction. This paper proposes an approach for shallow underwater visibility improvement which utilizes a combination of histogram equalization, gamma correction, and dark channel prior with morphological operation, in order to achieve effective haze subtraction and avoid halo effects in a single image with a complex structure. Our methodology, also considerthe effect of an adaptive gamma correction technique for further refinement of transmission map. The outcomes shows better peak signal to noise ratio and contrast noise ratio values over existing state-of-the-art methods based ondark channel prior. Results shows that our proposed approachin turn can be utilized in various underwater applications such as forensic image recognition, telecommunication cables recognition etc.

Keywords

Gamma, Haze, Histogram, Morphological, Underwater, Visibility

Full Text

   

Jasjit S. Sodhi ¹, Maitreyee Dutta ², Naveen Aggarwal ³

Affiliations
1 Department of Biotechnology, Himachal Pradesh University, Shimla - 171005, Himachal Pradesh, IN
2 Department of Computer Science, N.I.T.T.T.R, Chandigarh - 160019, Punjab, IN
3 Department of Computer Science, U.I.E.T, Punjab University, Chandigarh - 160014, Punjab, IN

Abstract

Objectives: This paper presents the use of machine learning technique in order to eliminate the components affecting the human decision making process. To assist decision making in career counseling, an Artificial Intelligence model was implemented using Artificial Neural Network (ANN) in MATLAB for predicting vocational stream of pursuit based on the behavioral characteristic of the beneficiary. Methods/Statistical Analysis: The Differential Aptitude Test (DAT) battery; and Scientific Knowledge and Aptitude Test (SKAT) were used to assess an individual’s specific abilities in different areas. The training data set for the ANN model was procured in the form of normalized scores based on occupational/vocational profiles as given by the authors of DAT battery and SKAT. The trained ANN was tested with normalized stanine scores of 100 tenth class studentsraised through cluster random sampling technique for predicting a vocational stream of pursuit. In order to evaluate its accuracy and efficiency, three techniques of classification were employed. The unclassified data was classified by using Discriminant function analysis, ANN and two classifications were obtained from the trained counselors. Findings: The classification result obtained from the above mentioned techniques were compared and was found that the ANN system and Discriminant function analysis agreed approximately by 91% over all the test cases. The results of the statistical method support the classification made by the ANN. The two counsellors were in agreement with ANN’s classification output by approximately 81%. However, the counsellors disagreed with each other’s prediction approximately by 27% over all the test cases. The experimental results support the hypothesis that the proposed machine learning technique performed better than the prediction made by counsellors. Application/Improvements: In the Indian scenario, the developed machine learning system may be used as a standalone system in places where there is the paucity of counsellorsor can assist in the career decision making provided by human beings.

Keywords

Artificial Neural Network, Career Counseling, Decision Support System, Differential Aptitude Test Battery, Discriminant Function Analysis, Scientific Aptitude and Knowledge Test.

Full Text

   

Praveen Kr. Mishra ¹, Maitreyee Dutta ²

Affiliations
1 CSE Department, BIT, Meerut – 250001, Uttar Pradesh, IN
2 CSE Department, NITTTR, Chandigarh – 160019, Punjab, IN

Abstract

This paper proposes an approach for Increasing visibility of single hazy image under night condition which utilizes a combination of histogram equalization, gamma correction, and dark channel prior with soft matting technique, refined transmission procedure to avoid the generation of block artifacts in the restored image, and effective transmission map estimation by adjusting its intensity via an enhanced transmission procedure based on the adaptive gamma correction technique. Our result shows better contrast noise ratio and peak signal to noise ratio values over existing methods based on dark channel prior.

Keywords

Haze, Histogram Equalization, Gamma Correction, Low Light, Visibility.

Full Text

   

Gulshan Goyal ¹, Maitreyee Dutta ²

Affiliations
1 I.K. Gujral Punjab Technical University (Punjab) & Faculty of Engineering, CCET, Chandigarh – 160019, IN
2 NITTTR, Chandigarh - 160019, IN

Abstract

Objectives: Performance and efficiency of thinning algorithms is essential in the field of image analysis and recognition. The present paper aims at experimental approach for performance analysis of different thinning algorithms for offline handwritten devnagri numeral script on multiparameter scale. Methods/Statistical Analysis: Algorithms based on datasets are reviewed and three algorithms based on their characteristics and strengths are implemented and their performance is evaluated based on pixel count in output image, compression ratio, pixel removal parameter, connectivity, triangle counts, unit pixel width, and information loss and topology preservation measure. Findings: Experimental findings indicate the strength and weakness of each thinning algorithm. Application/Improvements: The novelty of work is use of large parameter set for experimental performance evaluation. The findings and subsequent discussion aim at providing parametric strength of different thinning algorithms.

Keywords

Devnagri Numeral, Handwritten Character Recognition, Skeleton, Thinning, Topology, Triangle Count, Unit Pixel Width.

Full Text

   

Ramteke Mamta ¹, Maitreyee Dutta ²

Affiliations
1 Department of Computer Science, Chandigarh Engineering College, Chandigarh – 160019, IN
2 Department of Electronics and Communication Engineering, NITTTR Chandigarh, Chandigarh – 160019, IN

Abstract

Objectives: In this paper, the image restoration technique is designed based on the Genetic algorithm (GA) and cepstrum filtering. Methods/Statistical Analysis: We used the cepstrum method to find out motion blur parameters angle and length of spectrum according to the observed image for the cepstrum filtering. Findings: The GA as an optimization strategy can adjust the parameters and provide an appropriate value of theta and length. Optimized values of theta and length help to compute the PSF which is close to the real value of PSF as it increases the PSNR. The experimental results demonstrate that use of optimization technique in cepstrum filtering improve the PSNR of the restored image, but encounters some ringing effect.

Keywords

Cepstrum, Genetic Algorithm, Image Restoration, Parameter Estimation.

Full Text