Local Phase Features in Chromatic Domain for Human Detection

2016 ◽  
Vol 4 (3) ◽  
pp. 52-72
Author(s):  
Hussin K. Ragb ◽  
Vijayan K. Asari
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Error Rates ◽  
Image Features ◽  
Human Detection ◽  
Phase Congruency ◽  
Illumination Changes ◽  
Gradient Based

In this paper, a new descriptor based on phase congruency concept and LUV color space features is presented. Since the phase of the signal conveys more information regarding signal structure than the magnitude and the indispensable quality of the color in describing the world around us, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the three-color image channels. The maximum phase congruency values are selected from the corresponding color channels. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the proposed descriptor. Results of the experiments performed on the proposed descriptor show that it has better detection performance and lower error rates than a set of the state of the art feature extraction methodologies.

Local Phase Features in Chromatic Domain for Human Detection

2019 ◽  
pp. 646-666
Author(s):  
Hussin K. Ragb ◽  
Vijayan K. Asari
Keyword(s):  
Color Image ◽  
Color Space ◽  
Error Rates ◽  
Image Features ◽  
Human Detection ◽  
Local Phase ◽  
Phase Congruency ◽  
Illumination Changes ◽  
Gradient Based

In this paper, a new descriptor based on phase congruency concept and LUV color space features is presented. Since the phase of the signal conveys more information regarding signal structure than the magnitude and the indispensable quality of the color in describing the world around us, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the three-color image channels. The maximum phase congruency values are selected from the corresponding color channels. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the proposed descriptor. Results of the experiments performed on the proposed descriptor show that it has better detection performance and lower error rates than a set of the state of the art feature extraction methodologies.

scholarly journals Variational Histogram Equalization for Single Color Image Defogging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Li Zhou ◽  
Du Yan Bi ◽  
Lin Yuan He
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Histogram Equalization ◽  
Degradation Model ◽  
Image Defogging ◽  
Hsi Color Space ◽  
General Variation ◽  
Bad Weather ◽  
Physical Degradation

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

Color Medical Image Enhancement Using Y-H Model and Wavelet Homomorphic Filtering

2013 ◽  
Vol 411-414 ◽  
pp. 1020-1024
Author(s):  
Hua Liang ◽  
Zhen Tao Zhou ◽  
Hao Feng ◽  
Li Jun Ding ◽  
Ju Ping Gu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Color Image ◽  
Color Space ◽  
Inverse Transformation ◽  
Homomorphic Filtering ◽  
Enhancement Method ◽  
Rgb Color Space ◽  
Medical Image Enhancement

Color medical images are widely used in the field of medical diagnosis. Image enhancement is one of the most important pretreatment methods which can enhance the quality of images. In this paper, a novel color image enhancement method using Y-H model and wavelet homomorhpic filtering is put forward. The chromaticity numbers matrix and intensity numbers matrix of color images are get using Young-Helmholtz (YH) transform. The chromaticity numbers matrix remains unchanged. Wavelet homomorphic filtering method is used to process intensity numbers matrix . The enhanced intensity numbers matrix and formerly chromaticity numbers matrix are processed by Y-H inverse transformation and disply in RGB color space. The method put forward in the paper is successfully used in color medical image enhancement. Experimental results show that the method have characteristics of nondistortion, better visual effect.

A COLOR IMAGE RETRIEVAL METHOD BASED ON COLOR MOMENT AND COLOR VARIANCE OF ADJACENT PIXELS

2002 ◽  
Vol 16 (01) ◽  
pp. 113-125 ◽  
Author(s):  
YUNG-KUAN CHAN ◽  
CHIN-CHEN CHANG
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Spatial Information ◽  
Color Image ◽  
Image Features ◽  
Experimental Results ◽  
Retrieval Method ◽  
Color Moment

This paper first introduces three simple and effective image features — the color moment (CM), the color variance of adjacent pixels (CVAP) and CM–CVAP. The CM feature delineates the color-spatial information of images, and the CVAP feature describes the color variance of pixels in an image. However, these two features can only characterize the content of images in different ways. This paper hence provides another feature CM–CVAP, which combines both, to raise the quality of similarity measure. The experimental results show that the image retrieval method based on the CM–CVAP feature gives quite an impressive performance.

scholarly journals ELECTOR: Evaluator for long reads correction methods

2019 ◽  
Author(s):  
Camille Marchet ◽  
Pierre Morisse ◽  
Lolita Lecompte ◽  
Arnaud Lefebvre ◽  
Thierry Lecroq ◽  
...  
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Error Rates ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Wide Range ◽  
Downstream Processes ◽  
Generation Sequencing

AbstractMotivationIn the last few years, the error rates of third generation sequencing data have been capped above 5%, including many insertions and deletions. Thereby, an increasing number of long reads correction methods have been proposed to reduce the noise in these sequences. Whether hybrid or self-correction methods, there exist multiple approaches to correct long reads. As the quality of the error correction has huge impacts on downstream processes, developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need. Since error correction is often a resource bottleneck in long reads pipelines, a key feature of assessment methods is therefore to be efficient, in order to allow the fast comparison of different tools.ResultsWe propose ELECTOR, a reliable and efficient tool to evaluate long reads correction, that enables the evaluation of hybrid and self-correction methods. Our tool provides a complete and relevant set of metrics to assess the read quality improvement after correction and scales to large datasets. ELECTOR is directly compatible with a wide range of state-of-the-art error correction tools, using whether simulated or real long reads. We show that ELECTOR displays a wider range of metrics than the state-of-the-art tool, LRCstats, and additionally importantly decreases the runtime needed for assessment on all the studied datasets.AvailabilityELECTOR is available at https://github.com/kamimrcht/[email protected] or [email protected]

scholarly journals Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

2019 ◽  
Vol 224 ◽  
pp. 04010
Author(s):  
Viacheslav Voronin
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Satellite Images ◽  
Color Image ◽  
State Of The Art ◽  
Satellite Image ◽  
Detailed Model ◽  
Global Contrast ◽  
Enhancement Algorithm

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

scholarly journals Chroma Enhancement in CIELAB Color Space Using a Lookup Table

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 32
Author(s):  
Tadahiro Azetsu ◽  
Noriaki Suetake
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Lookup Table ◽  
Color Gamut ◽  
Human Visual Perception ◽  
Cielab Color Space ◽  
Rgb Color Space ◽  
Cielab Color

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

scholarly journals Auto Colorization of Gray-Scale Image Using YCbCr Color Space

2020 ◽  
pp. 3379-3386
Author(s):  
Eman Saleem ◽  
Nidhal K. El Abbadi
Keyword(s):  
Color Image ◽  
Color Space ◽  
Quality Measures ◽  
One Dimension ◽  
Reference Image ◽  
Three Dimension ◽  
Gray Image ◽  
Ycbcr Color Space ◽  
True Colors

The process of converting gray images or videos to color ones by adding colors to them and transforming them from one-dimension to three-dimension is called colorization. This process is often used to make the image appear more visually appealing. The main problem with the colorization process is the lack of knowledge of the true colors of the objects in the picture when it is captured. For that, there is no a unique solution. In the current work, the colorization of gray images is proposed based on the utilization of the YCbCr color space. Reference image (color image) is selected for transferring the color to a gray image. Both color and gray images are transferred to YCbCr color space. Then, the Y value of the gray image is combined with the Cb and Cr values of the reference image, based on the Euclidian distance between them. The quality of the resulted image was measured based on several quality measures, which indicated very good results. The proposed algorithm is simple, efficient, and fast.

scholarly journals A Quality Evaluation of Single and Multiple Camera Calibration Approaches for an Indoor Multi Camera Tracking System

2014 ◽  
Vol XL-5 ◽  
pp. 9-15
Author(s):  
M. Adduci ◽  
K. Amplianitis ◽  
R. Reulke
Keyword(s):  
Camera Calibration ◽  
Quality Evaluation ◽  
State Of The Art ◽  
Tracking System ◽  
Point Clouds ◽  
Human Detection ◽  
Sensor Calibration ◽  
Single Camera ◽  
Detection And Tracking

Human detection and tracking has been a prominent research area for several scientists around the globe. State of the art algorithms have been implemented, refined and accelerated to significantly improve the detection rate and eliminate false positives. While 2D approaches are well investigated, 3D human detection and tracking is still an unexplored research field. In both 2D/3D cases, introducing a multi camera system could vastly expand the accuracy and confidence of the tracking process. Within this work, a quality evaluation is performed on a multi RGB-D camera indoor tracking system for examining how camera calibration and pose can affect the quality of human tracks in the scene, independently from the detection and tracking approach used. After performing a calibration step on every Kinect sensor, state of the art single camera pose estimators were evaluated for checking how good the quality of the poses is estimated using planar objects such as an ordinate chessboard. With this information, a bundle block adjustment and ICP were performed for verifying the accuracy of the single pose estimators in a multi camera configuration system. Results have shown that single camera estimators provide high accuracy results of less than half a pixel forcing the bundle to converge after very few iterations. In relation to ICP, relative information between cloud pairs is more or less preserved giving a low score of fitting between concatenated pairs. Finally, sensor calibration proved to be an essential step for achieving maximum accuracy in the generated point clouds, and therefore in the accuracy of the produced 3D trajectories, from each sensor.

scholarly journals Low-Light Image Enhancement Based on Multi-Path Interaction

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4986
Author(s):  
Bai Zhao ◽  
Xiaolin Gong ◽  
Jian Wang ◽  
Lingchao Zhao
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Interaction Network ◽  
Feature Representation ◽  
Uniform Illumination ◽  
Feature Maps ◽  
Low Light ◽  
Low Contrast ◽  
Better Than

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding–decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.

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