Colour Detection for Interior Designs Using Machine Learning

Dewingong, Tarcisius Forjong; Afor, Mary Echabuo; Mishra, Pradeep Kumar; Mishra, Sasmita; Mishra, Gouri Sankar; Aliyu, Bello Ibrahim

doi:10.1007/978-3-031-23724-9_23

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assortment of information from the eleven research papers offers a careful handle of color detection and identification systems, explaining the numerous applications and approaches that they utilize. From traditional RGB variety models to state of the art strategies like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and K-Nearest Neighbours (KNN) algorithms, this examination feature the variety of color detection techniques [1][2][3][4][5][6][7][8][9][10][11]. The purposes are various and incorporate PC vision, picture handling, security, horticulture, and openness for those with visual disabilities.…”

Section: Related Workmentioning

confidence: 99%

“…These incorporate growing datasets, handling application-explicit troubles, and researching novel methodologies such coordinating unsupervised machine learning for color image segmentation. In light of everything, these examination distributions give a significant commitment to the area of color detection overall, showing its flexibility, helpful applications, and forthcoming social impact [1][2][3][4][5][6][7][8][9][10][11]. These…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques

Nayyer,

Kumar,

Rajput

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

INTRODUCTION: Object recognition is a crucial task in computer vision, with applications in robotics, autonomous vehicles, and security systems. OBJECTIVES: The objective of this paper is to propose a novel approach for object recognition by combining color detection and machine learning techniques. METHODS: The research employs YOLO v3, a state-of-the-art object detection algorithm, and k-means optimized clustering to enhance the accuracy and efficiency of object recognition. RESULTS: The main results obtained in this paper showcase the outperformance of the authors’ approach on a standard object recognition dataset compared to state-of-the-art approaches using only color features. Additionally, the effectiveness of this approach is demonstrated in a real-world scenario of detecting and tracking objects in a video stream. CONCLUSION: In conclusion, this approach, integrating color and shape features, has the potential to significantly enhance the accuracy and robustness of object recognition systems. This contribution can pave the way for the development of more reliable and efficient object recognition systems across various applications.

show abstract