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1. INTRODUCTION

Color plays an important role in daily life as a fundamental visual feature that gives richness to the world. Color may seem to be only for decoration, but it is frequently used as the primary means of informing people how to interpret information. Color works well for most people, but about 8% of males and less than 1% of females have faulty color perception from birth. To prepare for the coming barrier-free society, we need to take into account people who have faulty color perception. According to statistics on types of color blindness, the main types are red defective and green defective. Thus we mainly focus on a vision compensation system that enables color-blind people to see green and red colors. In order to compensate color for color-blind people, we developed a color compensation vision system. In this system, the captured image in RGB color space is converted to HLS color space which enables the defective color range to be easily avoided. The validity of proposed method is confirmed by the developed camera and display color compensation system.

2. DESCRIPTION OF THE PROBLEM

A major problem for color-blind people is recognizing traffic signals. Because red is commonly used for cautions and warnings, depending on the background color of the traffic signal, color-blind people may not be able to recognize the color difference.

Photo 1 Stop color of a traffic signal

Photo 2 shows a simulated image of the red-green defective type of color blindness by using the color simulation software called “Toyo Ink: UDing”. As shown, it is difficult to distinguish the red color. Photo 2 Appearance of traffic signal to color-blind

people

A Color Compensation Vision System for Color-blind People

Tomoyuki Ohkubo1 and Kazuyuki Kobayashi2

1 Graduate School of Engineering, Hosei University, Tokyo, Japan (Tel: +81-423-87-6260; E-mail: ohkubo@ikko-lab.k.hosei.ac.jp)

2 Faculty of Engineering Hosei University, Tokyo, Japan (Tel: +81-423-87-6267; E-mail: ikko@k.hosei.ac.jp)

Abstract: In this paper, we propose a color compensation vision system for color-blind people. About 8% of males and less than 1% of females have faulty color perception from birth. The degree to which a person may possess abnormal color vision ranges from slight difficulty in recognizing shades of color to total loss of color vision. Most types of defective color blindness can be classified into two categories: green color defective and red color defective. The population with the blue color defective type is less than 1%. Thus we mainly focus on a color vision compensation system that enables color-blind people to see the colors of green and red. In order to compensate image color for color-blind people, the image in RGB color space is converted to HLS color space which enables the defective color range to be easily avoided. The validity of the proposed method is confirmed by the developed camera and display color compensation system. Keywords: image processing, color-blind people,

Red

Dark yellow

SICE Annual Conference 2008August 20-22, 2008, The University Electro-Communications, Japan

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In this study, we examined the following problems:

How to make the color of red visible? How to evaluate the system?

3. PROPOSED COLOR VISION ASSIST

SYSTEM Figure 1 shows a schematic diagram of the proposed

color vision assist system. Inherent color blindness is caused by the base array of the opsin gene on the chromosome. One approach to assist color-blind people is an eye-glass color conversion display system to enhance color difference, for which we developed a color compensation algorithm. The developed system consists of a small camera, image processing unit and wearable display which can monitor real-time like a single eye-glass.

The captured image in RGB color space can be converted to HLS color space to enhance color difference depending on the defective color type. Figure 2 shows the relation of HLS color visible area for a normal person and a defective color person. For the defective area in HLS color space, we transform the defective color space to visible color space by:

(1)

We use “Data Glass 2” (Shimadzu Corporation)

which is a head mounted display (HMD) for wearable computers.

The transformed image is displayed on the HMD in real-time.

Fig. 1 Block diagram of system

Fig. 2 Color conversion model in HLS color space

Fig. 3 View of wearing the system

3. EXPERIMENTS We applied the developed color assist system to the realistic situation of a recognizing the traffic signal shown in Photo 1.

Photo 3 shows the transformed color image for a red-green defective type person which is also displayed on the HMD in the developed system.

Photo 3 After image processing Photo 4 shows a simulated transformed color image that a red-green defective type person can recognize.

Photo 4 Appearance of photo 3 for a person with red-green color vision

In Photo 4, the rightmost light appears to be on.

45270360/' HH

CCD PC HMD

Convert toHLS

color space

Convert toRGB

color spaceImage

processing

Magenta

Blue

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4. EXPERIMENTAL RESULTS AND DISCUSSION

It is not easy to imagine which color combinations

are confusing for individuals affected by color vision deficiency. The Variantor is designed for people with normal color vision. The Variantor color vision deficiency simulation eye-glass is used to emulate the same colors as perceived by a color-blind person. 4.1 Evaluation of SD method

In this study, we used the semantic differential method (SD method) to obtain Kansei data. Seventeen Kansei words are selected in the SD method, as shown in Table 1. These words are classified into perceptual words, image words and evaluation words, which are components of the hierarchy structural model.

The evaluation was obtained from ten subjects. Each subject was a male student in his early twenties.

Table 1 Terminology for assessment Easy to see (MIYASUI)

Hard to see (MINIKUI)

Not irritated (IRAIRA SHINAI)

Irritated (IRAIRA SURU)

Clear (HAKKIRI SHITA)

Vague (BONYARI SHITA)

Strong (TSUYOI)

Weak (YOWAI)

Beautiful (UTSUKUSHII)

Ugly (KITANAI)

Bright (AKARUI)

Dark (KURAI)

Not tired (TSUKARENAI)

Tired (TSUKARERU)

Sharp (SURUDOI)

Dull (NIBUI)

Clear (SUNDA)

Dirty (NIGOTTA)

Not flickering (CHIRATSUKANAI)

Flickering (CHIRATSUKU)

Standing out (UKIDETA)

Not standing out (SHIZUNDA)

Conspicuous (MEDATSU)

Inconspicuous (MEDATANAI)

Not glaring (GIRAGIRA SHINAI)

Glaring (GIRAGIRA SURU)

Comfortable (OCHITSUKU)

Uncomfortable (UWATSUITA)

Warm (ATATAKAI)

Cool (SUZUSHII)

Not spread (NIJIMANAI)

Spread (NIJIMU)

Vivid (AZAYAKA)

Quiet (JIMI)

4.2 Evaluation of normal person and color-blind person

In order to compare viewing between normal people and color-blind people, we used Variantor to simulate the view of color-blind people for evaluation consistency. Figure 4 shows the experimental setup for the evaluations. The evaluations were carried out by viewing traffic signals.

Fig. 4 Traffic viewing tests – case 1

To evaluate the traffic viewing test, we apply SD profile method which is shown in Figure 5. The result of this research has the good result of a normal person. However, the person who wearied the simulation glass has brought a bad result. This is because the red signal is changing, as the experiment showed.

normal person simulated person Fig. 5 SD profiles of case 1

Simulationeye-glass

Traffic signal

Defectivecolor view

Color-blindperson

Normalperson

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4.3 Evaluations with proposed color vision assist system According to the raw vision evaluation results, we examined our proposed color vision assist system. Figure 6 shows the configuration used to evaluate our system. The difference between previous experiments is whether our proposed system is used or not.

Fig. 6 Traffic viewing tests – case 2

The results of this research were converted into an SD profile as shown below.

normal person simulated person Fig. 7 SD profiles of case 2

The SD profile indicates that the evaluation results

are almost the same with or without Variantor. The proposed color vision assist system thus converts color vision consistently, regardless of normal and/or defective color, confirming the validity of our system.

5. CONCLUSIONS In this paper, we proposed a color compensation vision system for color-blind people. We confirmed its validity by using a camera with the display color compensation system.

REFERENCES [1] S. Muramatsu, Image & video signal processing

with MATLAB, CQ Pub., 2007. [2] Toyo Ink: Universal Design,

http://www.toyoink.co.jp/ud/index.html. [3] Seuttgi Ymg and Yong Man Ro, “Visual contents

adaptation for color vision deficiency”, Proc. of ICIP 2003, Vol. 1, pp. I-453-456, 2003.

[4] Yau-Hwang Kuo and Jang-Pong Hsu, “MCFC-R: a fuzzy connectionist model for color-blindness plate recognition”, Proc of ICNN’96, Vol. 2, pp. 718-723, 1996.

CCD

HMD

Simulationeye-glass

CCD

HMD

Traffic signal

Color-blind person

Normal person

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