Papers by Keyword: Color Model

Abstract: Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.

Abstract: Mathematical morphology can be seen as a special digital image processing method and theory, which has been widely used in various fields. In this paper, the mathematical morphology is applied to the color image processing. In thespace of color image, I have simply expounded the theories and properties of color morphological changs, and defined its morphological operators. According to the application of omni-directional and multi-angle structuring elements composite morphological filter in gray image, I put forward a kind of color morphological filter with omni-directional and multi-angle structuring elements composite. This algorithm has retained its advantages in gray image, however, remaining some drawbacks. Through the optimization of results based on this algorithm, we finally get the relatively ideal denoising effects.

Abstract: Face recognition technology is a significant branch of the study of artificial intelligence, the recognition precision is easily affected by facial expressions, skin colors, beam angles in the images and apparels. This essay tests human face images in the format of 24 BMP and realizes face location and mark of five sense organs. Firstly, color space model is adopted to set up skin color distribution model to segment skin regions; secondly, the obtained regions are judged and screened preliminarily, and optimized based on the characteristics of segmented regions with region optimization algorithm of depth-width ratio, rejecting the region with the similar color of the skin caused by some disturbing factors and other naked parts of the body, through which the rough region of human face could be attained and face location could be realized; finally, five organs of the obtained face region is located with the method of grey level region in combination with searching rectangle.

Abstract: Fire detection based on images is an effective method for fire prevention, especially in big room or badly environment. It is important to extract the features of a flame image. According to the idea of visual saliency in computer vision, saliency model of brightness, color and flame texture are proposed here. The saliency of flame brightness is indicated by the V component in HSV color space. The saliency of flame color is expressed by the relation of R and B in the RGB color space. The saliency of flame texture is described by the distance between the feature vectors which are the combination of features with Local Binary Pattern. Experimental results show the saliency model is effective for flame feature extraction.

Abstract: Flame Detection based on video is an important method for fire prevention. To save flame detection time, the moving objects were segmented from images in a video by using GMM(Gaussian Mixture Model) firstly. Then moving objects were determined as flame candidate or not by their color feature and area changes. The experiments show that the method could detect flame in video effectively with a low false positive rate.

Abstract: How to explore higher efficient and more credible fire-detection system by rapid development of computer and image processing techniques has aroused public’s extensive attention. To achieve fully automatic surveillance of fires, an intelligent fire detection method based on a multi-stage decision strategy of image processing is proposed. Both static and dynamic characteristics of the fire images sequence are considered. First of all time difference is used to process the gray-scale image to obtain the moving region in the scene, secondly apply color segmentation to get the ROI of fire region, thirdly shape features such as randomness of area size, edge likelihood are calculated to avoid some interference, at last the polygonal and irregular characters of flame like sharp corners and circularity are used to identified the fire. Experimental result shows the Fire-detection method presented in this paper could detect fire in the image sequence effectively, and it is capable of distinguishing Environmental light changes, background color interference and light false identification. Multi-stage decision strategy can improve the algorithm performance and reduce false-alarm rate. The proposed method has broad application prospects in the important military, social security, forest-fire alarm, commercial applications, and so on.

Abstract: With the development of technology concerning face recognition, it has aroused great attentions from related scientists and is envisioned to be widely applied in various fields in the future. In this paper we are going to describe an effective way for detecting face in the statistic images. The proposed method will firstly detect face region via using the color model, which has been described in my previous paper [1] so as to locate the candidate pixels. Then we analyze the gray value distribution of candidate pixels to segment a more accurate region. Finally the face region is likely to be obtained after the exclusion of superfluous skin region.

Abstract: A method of the classifying of ceramic tiles’ color difference is proposed, and the online detection system based on linear array color CCD sensors is designed. After the image of tile grabbed by CCD is transformed to the HIS color model, a series of image processing and analyzing methods are used to calculate the eigenvalue of sample. The minimum distance classifier is used to carry out tiles’ classifying. Experimental results show the method is effective.