22 citations · 63 across the 9 of their papers we have counts for
9 papers
Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization
Akshit Achara, Ram Krishna Pandey
Supervised deep learning models require significant amount of labeled data to achieve an acceptable performance on a specific task. However, when tested on unseen data, the models…
TrueDeep: A systematic approach of crack detection with less data
Ram Krishna Pandey, Akshit Achara
Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either n…
CoreDeep: Improving Crack Detection Algorithms Using Width Stochasticity
Ram Krishna Pandey, Akshit Achara
Automatically detecting or segmenting cracks in images can help in reducing the cost of maintenance or operations. Detecting, measuring and quantifying cracks for distress analysis…
Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images
Ram Krishna Pandey, Souvik Karmakar, A G Ramakrishnan +1
In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the f…
Binary Document Image Super Resolution for Improved Readability and OCR Performance
Ram Krishna Pandey, K Vignesh, A G Ramakrishnan +1
There is a need for information retrieval from large collections of low-resolution (LR) binary document images, which can be found in digital libraries across the world, where the…
MSCE: An edge preserving robust loss function for improving super-resolution algorithms
Ram Krishna Pandey, Nabagata Saha, Samarjit Karmakar +1
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning ba…