Showing 4 results for Image Processing
A. Abadpour, S. Kasaei,
Volume 1, Issue 3 (7-2005)
Abstract
A robust skin detector is the primary need of many fields of computer vision,
including face detection, gesture recognition, and pornography filtering. Less than 10 years
ago, the first paper on automatic pornography filtering was published. Since then, different
researchers claim different color spaces to be the best choice for skin detection in
pornography filtering. Unfortunately, no comprehensive work is performed on evaluating
different color spaces and their performance for detecting naked persons. As such,
researchers usualy refer to the results of skin detection based on the work doen for face
detection, which underlies different imaging conditions. In this paper, we examine 21 color
spaces in all their possible representations for pixel-based skin detection in pornographic
images. Consequently, this paper holds a large investigation in the field of skin detection,
and a specific run on the pornographic images.
A. Banaei, S. Samavi, E. Nasr Esfahani,
Volume 1, Issue 4 (10-2005)
Abstract
Microarray technology is a new and powerful tool for concurrent monitoring of
large number of genes expressions. Each microarray experiment produces hundreds of
images. Each digital image requires a large storage space. Hence, real-time processing of
these images and transmission of them necessitates efficient and custom-made lossless
compression schemes. In this paper, we offer a new architecture for lossless compression of
microarray images. In this architecture, we have used a dedicated hardware for separation
of foreground pixels from the background ones. By separating these pixels and using
pipeline architecture, a higher lossless compression ratio has been achieved as compared to
other existing methods
A. Sadr, N. Orouji,
Volume 15, Issue 2 (6-2019)
Abstract
Clifford Algebra (CA) is an effective substitute for classic algebra as the modern generation of mathematics. However, massive computational loads of CA-based algorithms have hindered its practical usage in the past decades. Nowadays, due to magnificent developments in computational architectures and systems, CA framework plays a vital role in the intuitive description of many scientific issues. Geometric Product is the most important CA operator, which created a novel perspective on image processing problems. In this work, Geometric Product and its properties are discussed precisely, and it is used for image partitioning as a straightforward instance. Efficient implementation of CA operators needs a specialized structure, therefore a hardware architecture is proposed that achieves 25x speed-up in comparison to the software approach.
Eisa Zarepour, Mohammad Reza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour,
Volume 20, Issue 3 (9-2024)
Abstract
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
