Topic
HSL and HSV
About: HSL and HSV is a research topic. Over the lifetime, 2737 publications have been published within this topic receiving 38552 citations. The topic is also known as: HSL & HSV.
Papers published on a yearly basis
Papers
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TL;DR: This work uses a simple statistical analysis to impose one image's color characteristics on another by choosing an appropriate source image and applying its characteristic to another image.
Abstract: We use a simple statistical analysis to impose one image's color characteristics on another. We can achieve color correction by choosing an appropriate source image and apply its characteristic to another image.
2,615 citations
23 Jun 2014
TL;DR: The contribution of color in a tracking-by-detection framework is investigated and an adaptive low-dimensional variant of color attributes is proposed, suggesting that color attributes provides superior performance for visual tracking.
Abstract: Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
1,499 citations
01 Feb 1997
TL;DR: It is shown that CCV’s can give superior results to color histogram-based methods for comparing images that incorporates spatial information, and to whom correspondence should be addressed tograms for image retrieval.
Abstract: Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his∗To whom correspondence should be addressed tograms for image retrieval.
931 citations
TL;DR: A spatial extension to the CIELAB color metric that is useful for measuring color reproduction errors of digital images is described, and over patterned regions of the image, the reproduction errors measured using the spatial extension ofCIELAB correspond to perceived color errors better than errors computed without theatial extension.
Abstract: — We describe a spatial extension to the CIELAB color metric that is useful for measuring color reproduction errors of digital images. To compute the error, digital color images are spatially filtered using a pattern-color separable method and then converted into the CIELAB representation. Over patterned regions of the image, the reproduction errors measured using the spatial extension of CIELAB correspond to perceived color errors better than errors computed without the spatial extension. Over uniform spatial regions of the image, errors computed with the extension are equal to errors computed using the standard CIELAB formulae.
790 citations
TL;DR: An efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.
Abstract: Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is extracted in order to form compact and meaningful feature vectors. Then, an efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.
641 citations