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.

Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Abstract: Plant disease classification has been associated with the production of essential food crops and human society. In this paper, we classify tomato plant disease using two different features: texture and color. For a texture feature, we extract statistical texture information (shape, scale and location) of an image from Scale invariant Feature Transform (SIFT) feature. As a main contribution, a new approach is introduced to model the Scale Invariant Feature Transform (SIFT) texture feature by Johnson SB distribution for statistical texture information of an image. The moment method is used to estimate the parameters of Johnson SB distribution. The mathematical representation of SIFT feature is matrix representation and too complex to be applied in image classification. Therefore, we propose a new statistical feature to represent the image in few numbers of dimensions. For a color feature, we extract statistical color information of an image from RGB color channel. The color statistics feature is the combination of mean, standard deviation and moments from degree three to five for each RGB color channel. Our proposed feature is a combination of statistical texture and color features to classify tomato plant disease. The experimental performance on PlantVillage database is compared with state-of-art feature vectors to highlight the advantages of the proposed feature.

Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN

Abstract: As 4-sensor line scan camera technology has matured, red (R), green (G), blue (B), and near-infrared (RGB-NIR) datasets have begun to appear in large numbers. The RGB-NIR data contain the rich color features of the RGB image and the sharp edge features of the NIR image. At present, in many studies, the RGB-NIR data are input directly into the processing algorithms for calculation of the 4D data; in these cases, redundant information is included, and the high correlation between the bands results in an inability to fully exploit the characteristics of the RGB-NIR data. In this paper, we propose a double-channel convolutional neural network (CNN) algorithm that takes into account the strong correlation between the R, G, and B bands in aerial images and the weaker correlation between the NIR band and the R, G, and B bands. First, the features of the RGB and NIR bands are calculated in two different CNN networks, and subsequently, feature fusion is performed in the fully connected layer. This is followed by the classification. By combining the two neural networks of RGB-CNN and NIR-CNN, the respective characteristics of the RGB-NIR data are fully exploited.

Color Imaging System Using a Single CCD Area Array

Abstract: A color imaging system employing a single CCD sensor with a color filter array has been implemented. The system incorporates color channel enhancement and interpolation and provides display on a conventional color video monitor. Color correction matrix coefficients are computed. The response of the system is compared to that of a photographic film.

Single image dehazing using gradient channel prior

Abstract: The dehazing techniques designed so far are not so-effective at preserving texture details, especially in case of a complex background and large haze gradient image. Therefore, the exploration of new alternatives for designing an effective prior is desirable. Thus, in this research work, Gradient profile prior (GPP) is designed to evaluate depth map from hazy images. The transmission map is also improved by utilizing Guided anisotropic diffusion and iterative learning based image filter (GADILF). The restoration model is also improved to reduce the effect of pixels saturation and color distortion from restored images. Performance analysis demonstrates that GPP can naturally restore the hazy image especially at the edges of sudden changes in the obtained depth map. Through extensive analysis, it has been found that GPP based dehazing can effectively suppress visual artefacts for hazy images and yield high-quality results as compared to the competitive dehazing techniques both quantitatively and qualitatively. Moreover, the relatively high computational speed of the proposed technique will facilitate it in real-time applications.