Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

FEMT: a computational approach for fog elimination using multiple thresholds

Abstract: Refining visibility through haze removal from image becomes an inevitable chore and essential to recognize and track vehicles, traffic signal, and signs clearly under road safety. That can face a recurrent degradation under destitute climatic circumstances for instance fog, rain, cloud, and smog. To diminish this constraint, various methods were designed and implemented, but most were not capable of obtaining the improved quantitative outcomes. Therefore, a new algorithm Fog Elimination using Multiple Thresholds (FEMT) for single image haze eviction that meritoriously obtains the significant results on both gray and colored over real and synthetic images using multiple thresholds is proposed in this paper. The proposed method targets on the light regions by reducing the brightness and increasing the contrast of image at different levels. Finally, by grouping all the obtained resultant images leads to the generation of the resultant defogged image. The qualitative and quantitative analysis is carried out for an assessment of digitalized de-hazed images acquired from the proposed algorithm and compared to the prior techniques. Simulated fallouts entitle high resemblance to the corresponding ground truth, reduction in computation time consumption to 88% and error of 98%. The proposed approach can be applied in the field of robotics, human activity monitoring, smart systems, and digital investigation on the hazy images.

Prediction of Underwater Surface Target Through SONAR: A Case Study of Machine Learning

Abstract: The discovery of rocks and minerals would have been very difficult past the development of the SONAR technique, which relays on certain parameters to be able to detect the obstacle or the surface is a rock or a mine. Machine learning has drawn the attention of maximum part of the technology-related and based industries, by showing advancements in the predictive analytics. The main aim is to emanate a capable prediction representative, united by the machine learning algorithmic characteristics, which can figure out if the target of the sound wave is either a rock or a mine or any other organism or any kind of other body. This attempt is a clear-cut case study which comes up with a machine learning plan for the grading of rocks and minerals, executed on a huge, highly spatial and complex SONAR dataset. The attempts are done on highly spatial SONAR dataset and achieved an accuracy of 83.17%, and AUC came out to be 0.92. With random forest algorithm, the results are further optimized by feature selection to get the accuracy of 90%. Assuring results are found, when the fulfillment of the designed groundwork is set side by side with the standard classifiers like SVM, random forest, etc., using different evaluation metrics like accuracy, sensitivity, etc. Machine learning is performing a major role in improving the quality of detection of underwater natural resources and will tend be better in the near future.