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About 6,311 papers for “Color histogram”

RGB color histogram.

Bipul Neupane (7512269), Teerayut Horanont (499185), Nguyen Duy Hung (7512272) · Figshare · 2019

Cited by 0Open access

Anatomy of a color histogram

Carol L. Novak, Steven A. Shafer · 2003

It is shown that the color histogram has an even closer relationship to scene properties than has been previously described. Color histograms have identifiable features that relate in a precise mathematical way to scene properties. Object color and illumination color are the most obvious properties that are related to color distribution, and their extraction has already been described. It is shown here that the histogram of color variation may be further exploited to relate its shape to surface roughness and imaging geometry. An understanding of these features allows an improved estimate of illumination color and object color to be made.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 117

Proposal color-histogram aware network

Jianhao Tan, Shanshan Tan, Liming Liu et al. · Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence · 2019

Visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These research and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we use convolutional network to characterize the feature, and color features. Specifically, it contains region proposal network which including the classification branch and regression branch.

Cited by 0

Edge color histogram for image retrieval

Seong‐O Shim, Tae‐Sun Choi · Proceedings - International Conference on Image Processing · 2003

The classical color histogram for image indexing does not take into account the shape information of an image. A color histogram method with edge information is studied. Color distributions are found for the pixels of three types of edges (two directional edges and one non-directional edge) and three distance measures are computed on the basis of the color distribution of each edge type. The proposed similarity measure obtained by combining these three distance measures could reduce the false match rate in comparison with using only single (non-directional) edge types. Simulation results show an improvement in indexing quality as compared to that of the traditional color histogram and edge histogram.

Cited by 23

Focused edge detection using color histogram

Toshiyuki Kashiwagi, T. Egami, Shunichiro Oe et al. · 2002

The purpose of the study is to develop an edge focusing evaluation method that requires little calculation and has robustness in real scenes. By this method, we detect focused edges in the middle image from three captured consecutive images, which have slightly different focuses. Our method is based on the width of blurred edges that are examined by the frequency of the color using an RGB color histogram. It needs no local windows that are set in images to evaluate the focus sharpness by traditional methods. The validity of our method is verified by some experimental results.

Cited by 1

Robust Histogram Construction from Color Invariants

2008

Abstract A simple and effective object recognition scheme is torepresent and match images on the basis of color histograms. To obtain robustness against varying imaging cir-cumstances (e.g. a change in illumination, object pose, and viewpoint), color histograms are constructed from color in-variants. However, in general, color invariants are negatively affected by sensor noise due to the instabilities of thesecolor invariant transforms at many RGB values. To sup-press the effect of noise blow-up for unstable color invariant

Cited by 0

Color Quantization by Morphological Histogram Processing

withdrawn author name · Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2007

In a previous paper it was proposed a graylevel quantization method by morphological histogram processing. This paper introduces the extension of that quantization method to color images. Considering an image under the RGB color space model, this extension reduces the number of colors in the image by partitioning an 3-D histogram, similar to the RGB color space, in rectangular parallelepiped regions, through a iterative process. Such partitioning is done, in each interation, by application of the graylevel quantization method to the longest dimension of the current region which have the greatest volume. The final classified color space is used to quantize the image. This paper also shows the comparison of the proposed method to the classical median cut one.

Cited by 0

Color Histogram Equalization Through Mesh Deformation

Eric Pichon Marc, Marc Niethammer · 2003

In this paper we propose an extension of grayscale histogram equalization for color images. For aesthetic reasons, previously proposed color histogram equalization techniques do not generate uniform color histograms. Our method will always generate an almost uniform color histogram thus making an optimal use of the color space. This is particularly interesting for pseudo-color scientific visualization. The method is based on deforming a mesh in color space to fit the existing histogram and then map it to a uniform histogram. It is a natural extension of grayscale histogram equalization and it can be applied to spatial and color space of any dimension.

Cited by 0

Color histogram diffusion for image enhancement

Taemin Kim · Zenodo (CERN European Organization for Nuclear Research) · 2011

Various color histogram equalization (CHE) methods have been proposed to extend grayscale histogram equalization (GHE) for color images. In this paper a new method called "histogram diffusion" that extends the GHE method to arbitrary dimensions is proposed. Ranges in a histogram are specified as overlapping bars of uniform heights and variable widths which are proportional to their frequencies. This diagram is called the "vistogram." As an alternative approach to GHE, the squared error of the vistogram from the uniform distribution is minimized. Each bar in the vistogram is approximated by a Gaussian function. Gaussian particles in the vistoram diffuse as a nonlinear autonomous system of ordinary differential equations. CHE results of color images showed that the approach is effective.

Cited by 1Open access

Unsupervised histogram based color image segmentation

K. Chenaoua, Ahmed Bouridane, Fatih Kurugöllü · 2004

In this paper, a new technique is proposed for the segmentation of color images. The technique is based on the use of the sigma filter and multithresholding of 2-band histograms. A sigma filter is first applied to smooth out regions while keeping edges. Histograms are then computed, smoothed and downsampled. A peak picking algorithm finds the predominant peaks in the histograms. A concordance process between the dominant peaks is performed to determine the corresponding peaks in the histograms. Labels are assigned to corresponding peaks. The resulting histograms are partitioned into regions having the right labels. The partitioned histograms are used to segment the R, G and B bands. Finally, the segmented bands are fused together to give the final segmented image.

Cited by 9

Color histogram equalization through mesh deformation

Éric Pichon, Marc Niethammer, Guillermo Sapiro · 2004

In this paper we propose an extension of grayscale histogram equalization for color images. For aesthetic reasons, previously proposed color histogram equalization techniques do not generate uniform color histograms. Our method will always generate an almost uniform color histogram thus making an optimal use of the color space. This is particularly interesting for pseudo-color scientific visualization. The method is based on deforming a mesh in color space to fit the existing histogram and then map it to a uniform histogram. It is a natural extension of grayscale histogram equalization and it can be applied to spatial and color space of any dimension.

Cited by 39

Histogram-based Color Transfer for ImageStitching

Tian, Qi-Chong, Cohen, Laurent D. · Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine) · 2017

Color inconsistency often exists between the images to be stitched and will reduce the visual quality of the stitching results. Color transfer plays an important role in image stitching. This kind of technique can produce corrected images which are color consistent. This paper presents a color transfer approach via histogram specification and global mapping. The proposed algorithm can make images share the same color style and obtain color consistency. There are four main steps in this algorithm. Firstly, overlapping regions between a reference image and a test image are obtained. Secondly, an exact histogram specification is conducted for the overlapping region in the test image using the histogram of the overlapping region in the reference image. Thirdly, a global mapping function is obtained by minimizing color differences with an iterative method. Lastly, the global mapping function is applied to the whole test image for producing a color-corrected image. Both the synthetic dataset and real dataset are tested. The experiments demonstrate that the proposed algorithm outperforms the compared methods both quantitatively and qualitatively.

Cited by 0

Color Image Arrangement Using Histogram Matching

Yusuke Kawakami, Tetsuo Hattori, R.P.C. Janaka Rajapakse · Proceedings of International Conference on Artificial Life and Robotics · 2024

For the arrangement processing of image using its histogram, we previously have presented a Histogram Matching method based on Gaussian Distribution (HMGD).However, in the case where the brightness histogram of input image has multiple peaks, the HMGD processing does not always bring good results.In this paper, we present an improved histogram matching method using the reference histogram that is made by appropriate moving average (HMMA) processing over the histogram of input image.Also in this paper, we show the experimental results.

Cited by 1Open access