Computer Vision for Color Detection

Joy, Deborah T.; Kaur, Gitika; Chugh, Aarti; Bajaj, Shalini Bhaskar

doi:10.21276/ijircst.2021.9.3.9

Cited by 13 publications

(6 citation statements)

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“…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%

“…This additional layer of data empowers a more exact and complex comprehension of the visual climate and is fundamental in situations where standard procedures could miss the mark. Accordingly, consolidating a variety recognizable proof with different viewpoints can fundamentally further develop items affirmation framework accuracy and potency [1]. One of the essential types of item recognition is color identification, which includes distinguishing the fundamental tints of an object in a picture or video.…”

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confidence: 99%

“…In any case, object identification algorithms might be made undeniably more precise and tough by coordinating color detection with different properties like surface and structure. This is because of the way that color might offer additional specific circumstance and recognizable proof data about a thing, which can be used to increment acknowledgment execution and disambiguate objects [1] [2]. Making object recognizable proof calculations that utilises various colors notwithstanding different properties like shape, surface, and setting has gained popularity in recent years.…”

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confidence: 99%

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

Nayyer,

Kumar,

Rajput

et al. 2024

EAI Endorsed Trans IoT

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Nayyer,

Kumar,

Rajput

et al. 2024

EAI Endorsed Trans IoT

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“…In this paper, they use a completely deep learning-based approach to solve the problem of object detection in an end-to-end fashion. D.Joy et al [1] proposes the idea of teaching a computer to detect and define a colour well enough to have useful applications. Ettore Baglio [3] in his book "Chemistry and technology of honey production.…”

Section: Literature Surveymentioning

confidence: 99%

Effective and Efficient Honey Harvest Alert System for Bee Farms

Raj¹,

Kavin²,

Jeeva³

et al. 2022

J. Phys.: Conf. Ser.

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The technology described in this paper could be used to support the rural people who practice apiculture. The present innovation helps to increase the quality attributes of the honey by automating the process of monitoring the honey broods in order to obtain the first quality honey. The proposed invention continuously monitors the honey brood using the webcam and image processing technique is used to identify the capping level with absolute accuracy. We also monitor the humidity level inside the brood box using the humidity sensor ensuring the quality of honey produced and quality of the environment for the sustaining of bees. Image processing and machine learning is used to identify the level of capping occurred in the beehive and first quality of honey could be extracted if at least 75% of cells of the hive are capped.

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