Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Robust and Transparent Color Modulation for Text Data Hiding

Abstract: This paper improves the use of text color modulation (TCM) as a reliable text document data hiding method. Using TCM, the characters in a document have their color components modified (possibly unperceptually) according to a side message to be embedded. This work presents a detection metric and an analysis determining the detection error rate in TCM, considering an assumed print and scan (PS) channel model. In addition, a perceptual impact model is employed to evaluate the perceptual difference between a modified and a non-modified character. Combining this perceptual model and the results from the detection error analysis it is possible to determine the optimum color modulation values. The proposed detection metric also exploits the orientation characteristics of color halftoning to reduce the error rate. In particular, because color halftoning algorithms use different screen orientation angles for each color channel, this is used as an effective feature to detect the embedded message. Experiments illustrate the validity of the analysis and the applicability of the method.

Systems and methods for detecting skin, eye region, and pupils

Abstract: Systems, methods, and processes are provided for locating pupils in a portrait image for applications such as facial recognition, facial authentication, and manufacture of identification documents. One proposed method comprises three steps; skin detection, eye detection, and pupil detection. In the first step, the skin detection employs a plurality of Gaussian skin models. In the second step, coarse eye locations are found by using the amount of deviation in the R (red) channel with an image that has been cropped by skin detection. A small block centered at an obtained coarse location is then further processed in pupil detection. The step of pupil detection involves determining a Pupil Index that measures the characteristics of a pupil. Experiments tested on highly jpeg compressed images show that the algorithm of this embodiment successfully locates pupil images. It is believed that this novel technique for locating pupils in images can improve the accuracy of face recognition and/or face authentication.

An automated cloud detection method based on the green channel of total-sky visible images

Abstract: . Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.