A Study of Image Pre-processing for Faster Object Recognition

TL;DR

This paper investigates an image pre-processing technique aimed at enhancing the accuracy and efficiency of object recognition systems by improving image quality before feature extraction.

Contribution

It proposes a new image pre-processing method that boosts recognition accuracy and reduces training data requirements for machine learning and deep learning models.

Findings

Improved object recognition accuracy with the proposed pre-processing method

Reduced number of training images needed for effective recognition

Better performance compared to previous approaches

Abstract

Quality of image always plays a vital role in in-creasing object recognition or classification rate. A good quality image gives better recognition or classification rate than any unprocessed noisy images. It is more difficult to extract features from such unprocessed images which in-turn reduces object recognition or classification rate. To overcome problems occurred due to low quality image, typically pre-processing is done before extracting features from the image. Our project proposes an image pre-processing method, so that the performance of selected Machine Learning algorithms or Deep Learning algorithms increases in terms of increased accuracy or reduced the number of training images. In the later part, we compare the performance results by using our method with the previous used approaches.