Color-to-Grayscale: Does the Method Matter in Image Recognition?
Reads0
Chats0
TLDR
A simple method is identified that generally works best for face and object recognition, and two that work well for recognizing textures, which are tested using a modern descriptor-based image recognition framework.Abstract:
In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.read more
Citations
More filters
Journal ArticleDOI
An yield estimation in citrus orchards via fruit detection and counting using image processing
TL;DR: The proposed citrus recognition and counting algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops.
Proceedings ArticleDOI
Covid-19 Face Mask Detection Using TensorFlow, Keras and OpenCV
TL;DR: A simplified approach to detect the presence of masks correctly without causing over-fitting using some basic Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit-Learn is presented.
Journal ArticleDOI
A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images
TL;DR: The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis, and reduces on the time required by the cytotechnician to screen very many pap- smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides.
Journal ArticleDOI
Image quantization as a dimensionality reduction procedure in color and texture feature extraction
TL;DR: The results indicate that quantization simplify images before feature extraction and dimensionality reduction, producing more compact vectors and reducing system complexity.
Journal ArticleDOI
Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm
TL;DR: The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Book ChapterDOI
Probability Inequalities for sums of Bounded Random Variables
TL;DR: In this article, upper bounds for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt are derived for certain sums of dependent random variables such as U statistics.