• Harshkumar Dharamchand Acheliya
• Rani Rukappa Banne
• Harshal Ganesh Deore
• Krutika Ramakrishna Gholap

The presence of shadows in images can significantly impact the performance of various computer vision tasks, such as object recognition, tracking, and segmentation. This research paper proposes a novel approach for eliminating object shadows from images using the K-Nearest Neighbors (KNN) algorithm. The KNN algorithm, a popular non-parametric classification technique, is leveraged to classify shadow pixels based on their similarity to neighboring pixels. The proposed method aims to improve the accuracy and robustness of shadow removal in various applications by effectively distinguishing shadows from other image regions. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach in shadow elimination, outperforming existing state-of-the-art methods.

Object shadow removal, K-Nearest Neighbors algorithm, Computer vision, Image processing, Non-parametric classification.

"Elimination Of Object Shadow Using K-Nearest Neighbors Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.o422-o424, May-2023, Available :http://www.jetir.org/papers/JETIR2305F55.pdf

"Elimination Of Object Shadow Using K-Nearest Neighbors Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppo422-o424, May-2023, Available at : http://www.jetir.org/papers/JETIR2305F55.pdf