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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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors show that pre-processing and proper cloud-screening of CZCS data are necessary for accurate satellite-derived pigment concentrations, especially in the coastal margins, where pigment content is high and image distortion associated with electronic overshoot.
Abstract: Clouds are removed from Coastal Zone Color Scanner (CZCS) data using channel 5. Instrumentation problems require pre-processing of channel 5 before an intelligent cloud-screening algorithm can be used. For example, at intervals of about 16 lines, the sensor records anomalously low radiances. Moreover, the calibration equation yields negative radiances when the sensor records zero counts, and pixels corrupted by electronic overshoot must also be excluded. The remaining pixels may then be used in conjunction with the procedure of Simpson and Humphrey to determine the CZCS cloud mask. These results plus in situ observations of phytoplankton pigment concentration show that pre-processing and proper cloud-screening of CZCS data are necessary for accurate satellite-derived pigment concentrations. This is especially true in the coastal margins, where pigment content is high and image distortion associated with electronic overshoot is also present. The pre-processing algorithm is critical to obtaining accurate global estimates of pigment from spacecraft data.

31 citations

Patent
15 Aug 2017
TL;DR: In this article, an image-recognition-technology-based shelf recognition method is proposed, which consists of 360-degree shooting on a target product and images of the commodity at all angles are collected, data processing is carried out to obtain a sample image set, and a shelf image not including the target product is obtained from another channel to get a trained verification image set; S2, the collected sample image sets and verification picture set are sent to a training machine to carry out mode training, thereby obtaining a recognition engine with image recognition capability; S3, a shelf commodity
Abstract: The invention provides an image-recognition-technology-based shelf recognition method. The method comprises: S1, 360-degree shooting is carried out on a target product and images of the commodity at all angles are collected, data processing is carried out to obtain a sample image set, and a shelf image not including the target product is obtained from another channel to obtain a trained verification image set; S2, the collected sample image set and verification picture set are sent to a training machine to carry out mode training, thereby obtaining a recognition engine with image recognition capability; S3,a shelf commodity display image is collected and the collected image is uploaded to a PC terminal for data preprocessing; and S4, the image set after data preprocessing at the step S3 is placed into the recognition engine for identification. With the deep-learning image recognition technology, rapid and accurate identification of information in a shelf image and statistics of the information can be realized, so that the trouble of manual field checking can be avoided. The method can be operated simply and conveniently; commodity arrangement information of a plurality of stores can be obtained within short time, so that the management work efficiency and accuracy can be improved.

31 citations

Proceedings ArticleDOI
22 Apr 1998
TL;DR: Experimental results show that the watermark image is transparent to embedding for large amounts of hidden data, and the quality of the extracted signature is high even when the watermarked image is subjected to up to 75% wavelet compression and 85% JPEG lossy compression.
Abstract: Describes a data hiding technique which uses noise-resilient channel codes based on multidimensional lattices. A trade-off between between the quantity of hidden data and the quality of the watermarked image is achieved by varying the number of quantization levels for the signature and a scale factor for data embedding. Experimental results show that the watermarked image is transparent to embedding for large amounts of hidden data, and the quality of the extracted signature is high even when the watermarked image is subjected to up to 75% wavelet compression and 85% JPEG lossy compression. These results can be combined with a private key-based scheme to make unauthorized retrieval practically impossible, even with the knowledge of the algorithm.

31 citations

Proceedings ArticleDOI
Beijing Chen1, Huazhong Shu1, Hui Zhang1, Gang Chen1, Limin Luo1 
23 Aug 2010
TL;DR: It is shown that the QZMs can be obtained via the conventional Zernike moments of each channel, and a set of combined invariants to rotation and translation (RT) using the modulus of centralQZMs is constructed.
Abstract: Moments and moment invariants are useful tool in pattern recognition and image analysis. Conventional methods to deal with color images are based on RGB decomposition or graying. In this paper, by using the theory of quaternions, we introduce a set of quaternion Zernike moments (QZMs) for color images in a holistic manner. It is shown that the QZMs can be obtained via the conventional Zernike moments of each channel. We also construct a set of combined invariants to rotation and translation (RT) using the modulus of central QZMs. Experimental results show that the proposed descriptors are more efficient than the existing ones.

31 citations

Proceedings ArticleDOI
10 Apr 2016
TL;DR: Uber-in-light as discussed by the authors is an unobtrusive and accurate VLC system that enables real-time screen-camera communication, applicable to any screen and camera, which encodes the data as complementary intensity changes over Red, Green, and Blue (RGB) color channels.
Abstract: Recently, Visible Light Communication (VLC) over a screen-camera channel has drawn considerable attention to unobtrusive design. It overcomes the distractive nature of traditional coded image approaches (e.g., barcodes). Previous unobtrusive methods fall into two categories: 1) utilizing alpha channel, a well known concept in computer graphics, to encode bits into the pixel translucency change with off-the-shelf smart devices; and 2) leveraging the spatial-temporal flicker-fusion property of human vision system with the fast frame rate of modern displays. However, these approaches heavily rely on high-end devices to achieve both unobtrusive and high accuracy screen-camera-based data communication without affecting video-viewing experience. Unlike previous approaches, we propose Uber-in-light, a novel unobtrusive and accurate VLC system, that enables real-time screen-camera communication, applicable to any screen and camera. The proposed system encodes the data as complementary intensity changes over Red, Green, and Blue (RGB) color channels that could be successfully decoded by camera while leaving the human visual perception unaffected. We design a MFSK modulation scheme with dedicated frame synchronization signal embedded in an orthogonal color channel to achieve high throughput. Furthermore, together with the complementary color intensity, an enhanced MUSIC-based demodulation scheme is developed to ensure highly accurate data transmission. Our user experience experiments confirmed the effectiveness of delivering unobtrusive data across different types of video content and resolutions. Extensive real-time performance evaluations are conducted using our prototype implementation to demonstrate the efficiency and reliability of the proposed system under diverse wireless environments.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202216
2021559
2020643
2019696
2018613
2017496