Real-Time object color Identification

Real-Time object color Identification
Author(s):
Atharva Borawake, Nilima Kulkarni, Anshul Ghorse
DOI Number:
10.6084/m9.figshare.13650167
Keywords:
Color Histogram, Feature extraction, K-Nearest Neighbor (KNN)
Abstract
The computer vision field is a rapidly growing field devoted to analyzing and understanding digital images. We can create computer vision projects through OpenCV. In OpenCV image processing processes such as image filtering, simple geometric photo transformation, color space transition, histograms, etc. are covered. Picture and real-time object color identification focus on OpenCV color identification through using the RGB model as well as the K-Nearest Neighbors Classification algorithm trained on r, g, b pixel values. Color identification in the image can be done through the RGB value of the target pixel as input and then calculates the distance, and the nearest color is chosen. From this method, we can identify 800 plus different colors from our datasets including the RGB value of each color. We conduct extraction of features in real-time color identification of objects to extract their RGB color Histogram attributes from training images and trained classification algorithm via RGB Color Histogram attributes. The KNN classifier analyzes the webcam frames and performs feature extraction and then shows the color.
Article Details
Unique Paper ID: 150658 Publication Volume & Issue: Volume 7, Issue 8 Page(s): 191 - 195
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