Projector Camera System Presenting Color Information for the Color Vision Deficient

2016 ◽  
Vol 20 (1) ◽  
pp. 26-32
Author(s):  
Daiki Kawakami ◽  
◽  
Kaito Makino ◽  
Yuichi Kobayashi ◽  
Toru Kaneko ◽  
...  
Keyword(s):  
Color Vision ◽  
Real Life ◽  
Color Images ◽  
Color Information ◽  
Camera System ◽  
Projection System ◽  
Color Combinations ◽  
Color Conversion

There are people who cannot distinguish between specific colors easily. This paper presents an improvement to a system for the color vision deficient. The system consists of a camera that acquires an image of an object and a projector that projects light on that object. One of the features of the system is that it handles real-life objects. When objects have colors that cannot be distinguished, the system converts the color to a distinguishable one using the projector. The improvement in the proposed system is that it produces images with patterns and blinking light to handle conventional color conversion operations that cannot produce distinguishable color images because of excessive multiplicity of color combinations. We verify through experiments the effectiveness of the proposed color projection system with its patterns and blinking light.

Chromatic Vision Support System with Color Conversion Constraints

2013 ◽  
Vol 17 (2) ◽  
pp. 176-184
Author(s):  
Yu Shirashige ◽  
◽  
Hideaki Orii ◽  
Hideaki Kawano ◽  
Hiroshi Maeda ◽  
...  
Keyword(s):  
Support System ◽  
Color Blindness ◽  
Camera System ◽  
Cone Cells ◽  
Color Combinations ◽  
Color Conversion

The symptoms of “color blindness” are due to an innate lack or deficit of “cone cells” that recognize colors. People with color blindness have difficulty discriminating combinations of specific colors. In this study, we developed a system to support color blindness. In this system, the brightness of colors is modified using a projector-camera system. Images that contain combinations of specific colors are difficult to discriminate using a camera, so this task is performed by a projector. We conducted experiments, to validate our proposed system using various color combinations.

Processing Color Images, Brightness and Color Conversion

2019 ◽  
Author(s):  
Beknazarova Saida Safibullaevna ◽  
Mukhamadiyev Abduvali Shukurovich ◽  
Jaumitbayeva Mehriban Karamtdin kizi
Keyword(s):  
Color Images ◽  
Color Conversion

Improvement of Color Information in the Generation of 3D Models of Real Objects Using Range Intensity Images

2012 ◽  
Vol 523-524 ◽  
pp. 362-367
Author(s):  
Toru Takahama ◽  
Ryo Inomata ◽  
Kenji Terabayashi ◽  
Kazunori Umeda ◽  
Guy Godin
Keyword(s):  
3D Model ◽  
Geometric Model ◽  
Texture Mapping ◽  
3D Models ◽  
Correction Method ◽  
Image Sensor ◽  
Color Images ◽  
Range Image ◽  
Color Information ◽  
Intensity Image

Texture mapping on scanned objects, which is the method to map color images on a 3D geometric model measured by a range image sensor, is often used for constructing a realistic 3D model. Color images are affected by the illumination conditions. Therefore, discontinuities of seams occur when simply applying texture mapping. In this paper, we propose a method for correcting the discontinuities using a range intensity image. A range intensity image is a kind of intensity image that is related to the reflectance ratio of the measured points, simultaneously acquired with a range image using an active range sensor. The method estimates the color information that is not affected by the lighting environment using multiple color images and a range intensity image. As a result, the method is effective to construct a 3D model with seamless color images. The effectiveness of the correction method is illustrated by experiments with real-world objects.

scholarly journals A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huy Nguyen-Quoc ◽  
Vinh Truong Hoang
Keyword(s):  
Color Image ◽  
Real Life ◽  
Selection Method ◽  
Color Images ◽  
Human Detection ◽  
Maximum Magnitude ◽  
Color Component ◽  
Novel Approach ◽  
The Face ◽  
Benchmark Datasets

Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.

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