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Optics Express

Optics Express

  • Editor: Michael Duncan
  • Vol. 11, Iss. 13 — Jun. 30, 2003
  • pp: 1577–1584
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Display of polarization information by coherently moving dots

K. M. Yemelyanov, M. A. Lo, E. N. Pugh, Jr., and N. Engheta  »View Author Affiliations


Optics Express, Vol. 11, Issue 13, pp. 1577-1584 (2003)
http://dx.doi.org/10.1364/OE.11.001577


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Abstract

It is known that human eyes are effectively polarization-blind. Therefore, in order to display the polarization information in an image, one may require exhibiting such information using other visual cues that are compatible with the human visual system and can be easily detectable by a human observer. Here, we present a technique for displaying polarization information in an image using coherently moving dots that are superimposed on the image. Our examples show that this technique would allow the image segments with polarization signals to “pop out” easily, which will lead to better target feature detection and visibility enhancement.

© 2003 Optical Society of America

1. Introduction

However, since the human eye cannot “see” the polarization information, when this information is captured by a polarimetric imaging system, it has to be displayed into some form of visual cues/information that can be detectable by a human observer. In other words, some form of “sensory substitution” should be exploited for representing polarization “signals”. In our group’s earlier work, we have shown one such bio-inspired mapping, in which polarization information was pseudo color-coded, based on the opponent-colors model of human vision [9

9. J. S. Tyo, E. N. Pugh, Jr., and N. Engheta, “Colorimetric representation for use with polarization-difference imaging of objects in scattering media,” J. Opt. Soc. Am. A 15, 367–374 (1998). [CrossRef]

]. The results established several promising imaging strategies for displaying in a natural way the contrast enhancement of polarization imagery of objects in scattering media. Such mapping of polarization information into pseudo-colors ignores the “true” colors of the scene, namely, the spectral information contained in the scene. So if one wants to preserve the spectral and luminance information in an image, one will need to map the polarization into visual cues other than the color and brightness.

We are interested to explore and investigate certain bio-inspired display methodologies for mapping polarization information into visual information that can be readily perceived by the human visual system. In this Letter, we describe one such mapping, namely representation of polarization information by a set of coherently moving dots superimposed on an image. It is known that human vision is capable of motion perception, which includes coherent motion detection, form from motion, and biological motion (e.g., [10

10. W. Curran and O. J. Braddick, “Speed and direction of locally-paired dot patterns,” Vision Res. 40, 2115–2124 (2000). [CrossRef] [PubMed]

]–[12

12. E. D. Grossman and R. Blake, “Perception of coherent motion, biological motion and form-from-motion under dim-light conditions,” Vision Res. 39, 3721–3727 (1999). [CrossRef]

]). We exploit the sensitivity of coherence detection in human eye in displaying polarization information in an image. One of the motivations behind using coherently moving dots in such mapping is to preserve, by and large, various features of the image, such as color and luminance, while “displaying” the polarization information.

2. Mapping of polarization-difference (PD) signal into coherently moving dots

In our earlier work, we introduced the concept of polarization-difference imaging [7

7. M. P. Rowe, E. N. Pugh, Jr., J. S. Tyo, and N. Engheta, “Polarization-difference imaging: a biologically inspired technique for observation through scattering media,” Opt. Lett. 20, 608–610 (1995). [CrossRef] [PubMed]

]. In this technique, at every pixel of the image, the intensities of the two orthogonal polarization components of the light reaching the camera, I (x,y) and I (x,y), are obtained, and then the sum and difference of these two quantities are evaluated

PSI(x,y)=I(x,y)+I(x,y),
(1)
PDI(x,y)=I(x,y)I(x,y),
(2)

where (x,y) identifies the pixel position in the image, ▯ and ⊥ indicate two orthogonal linear polarizations, PD stands for the “polarization-difference” signal, and PS for the “polarization-sum” signal. If an ideal linear polarization analyzer is used to capture orthogonal polarization components, the PS image will be equivalent to a conventional intensity image.

The brightness/intensity of the dots must be chosen such that they are easily detectable against the background image. Here we suggest two different recipes; (1) the intensity of each pixel of a dot in each cell is selected to be in “contrast” with the PS signals of the pixels the dot is replacing. Specifically, if PSI(x,y) of each of the pixels which are being replaced by the m×m-pixel dot is greater than 128 pixel intensity in an 8-bit display system, the intensity of each pixel of the dot in that cell will be assigned as Idot(x,y)=PSI(x,y)-128. However, if PSI(x,y)<128, then we will choose Idot(x,y)=PSI (x,y)+128. In this scheme, which we call the “contrast scheme”, the dot intensity is “complemented” against the background intensity in each cell; (2) in this method, the intensity of each pixel in a dot in each cell is chosen to be M% less than the PS signal in the image pixel which it is replacing. So Idot(x,y)=(1-M/100) PSI(x,y). This approach, we call “percentage scheme”.

3. Results

To demonstrate this mapping strategy, we develop the above algorithm in the MATLAB® environment. The frame sequences generated using this algorithm are presented as movies with 20 frames/second in the .avi format. These movies can be viewed by any player, e.g., Microsoft Windows Media Player, Apple QuickTime Player, or the likes.

Finally, Fig. 5, when clicked on appropriate links, illustrates a similar mapping of polarization into moving dots. The difference here, however, is that the dots form short lines with time-varying lengths as they move, providing additional cues for perception of polarization direction. This mapping, of course, results in higher number of pixels to be replaced with dots and lines, but it may provide stronger cues for polarization representation.

Fig. 1. (2.5 MB movie) A collection of randomly located dots on a dark background. Click here to start the movie. One can see that the region with coherently moving dots can be easily “popped out” against the background having randomly moving dots. Other information: image size: 316×316 pixels, cell size: 7×7, dot’s pixel intensity: 255, frame rate: 20frames/sec, dot’s speed in the region with coherently moving dots: 20 pixels/sec. (12.5 MB version)
Fig. 2. (a) PS image and (b) PD image of the target to which we apply the polarization-to-moving-dots mapping strategy introduced here. These target images were originally obtained and used in our previous study of polarization difference imaging (PDI) reported in (Fig. 1 in[9]). The light scattered from the two square patch areas are slightly partially polarized parallel to the direction of abrasion on these patches. The goal here is to map the polarization information contained in the PD image into the PS image by using coherently moving dots. Image size: 512×479 pixels.
Fig. 3. (2.5 MB and 1.5 MB movies.) Implementation of the mapping technique described here on the image of target shown in Fig. 2. Polarization information from the affine transformed PD image (Fig. 2(b)) is mapped as moving dots onto the PS image (Fig. 2(a)). Here the threshold value is chosen to be δ=32 for the affine transformed PD values. In (a), the dot intensity is prescribed using the “contrast scheme,” while in (b) it is chosen using the “percentage scheme” with M=30%. Viewing the moving dots, our visual system can distinguish among the regions with PD>δ, PD<-δ, and -δ<PD<δ PD signals. Other information: Image size: 340×316 pixels, cell size: 7×7 pixels, frame rate: 20frames/sec. ((a) 14.9 MB version, (b) 10.3 MB version).
Fig. 4. (2.54 MB and 1.51 MB movies.) Similar to Fig. 3, except here the threshold value is chosen to be δ=48 for the affine transformed PD values. We note that the higher threshold value results in having smaller regions with coherently moving dots, thus highlighting the patch areas where the PD signal has higher absolute values. Other information: Image size: 340×316 pixels, cell size: 7×7 pixels, frame rate: 20 frames/sec. ((a) 12.7 MB version, (b) 10.4 MB version).
Fig. 5. (2.58 MB and 1.2 MB movies.) Similar description as Fig. 3, except here the moving dots form short line with time-varying lengths, resulting in additional cues to visualize polarization information from the PD image given in Fig. 2(b). Other information: Image size: 340×316 pixels, cell size: 7×7 pixels, frame rate: 20 frames/sec. ((a) 12.9 MB version, (b) 6.15 MB version).

Polarization is a manifestation of the vectorial nature of optical waves, and the display method employed here is designed to visualize the polarization information in an image. However, it is important to point out that the same display technique can be used to represent other vector quantities in images. Applied to optical polarization, we have specifically used this technique to map the “polarization-difference (PD)” signal, since it was shown by Tyo [13

13. J. S. Tyo, “Optimum linear combination strategy for an N-channel polarization-sensitive imaging or vision system,” J. Opt. Soc. Am. A. 15, 359–366 (1998). [CrossRef]

] that the PD signal is indeed uncorrelated or “orthogonal”, in the information theoretic sense, to the “polarization-sum (PS)” signal, i.e., to the conventional intensity signals. Thus, PD and PS channels constitute together an optimum basis of the polarization components. Since we want to superimpose PD information on a PS (i.e., intensity) image, we use coherently moving dots to represent the PD information arising in optical polarization. In this context it bears emphasis that for a circularly-polarized light, the PD value is zero and thus no coherently moving dots are associated with it. Such neglect of circular polarization is justifiable, since in any optical imaging system in which the phase information between the two orthogonal polarization components is not available, a circularly-polarized light would be “equivalent” to an unpolarized light, thus resulting in a zero PD value. Were circular polarization information available, one could conceivably employ a set of dots that are coherently moving in a circular pattern to represent the local circular polarization. This requires further investigation.

5. Conclusions

We have introduced a visualization technique for mapping polarization information into an image without essentially altering the information contents of the original image. Dots have been superimposed on the image, and the motion of such dots has been implemented. Regions with PD>δ and PD<-δ signals are given dots that move coherently in two orthogonal directions representing main directions of partial polarization, and the regions with -δ<PD<δ signals are assigned randomly moving dots. Since human vision can sense and perceive coherent motion, segments of the image with coherently moving dots can be readily detected and distinguished against the region with random motion. In this way, the segments with various ranges of PD signals “pop out”. Such polarization representation can lead to visibility enhancement, better target detection and feature extraction.

Acknowledgements

This work was supported in part by the U.S. Air Force Office of Scientific Research (AFOSR), through grants F49620-01-1-0470 and F49620-02-1-0140. We thank Dr. J. S. Tyo currently of the University of New Mexico for his effort in gathering the target image originally shown in Fig. 1 of [9

9. J. S. Tyo, E. N. Pugh, Jr., and N. Engheta, “Colorimetric representation for use with polarization-difference imaging of objects in scattering media,” J. Opt. Soc. Am. A 15, 367–374 (1998). [CrossRef]

], now in Fig. 2 here, while he was in our research group at the University of Pennsylvania working on the colorimetric polarization-difference imaging reported in [9

9. J. S. Tyo, E. N. Pugh, Jr., and N. Engheta, “Colorimetric representation for use with polarization-difference imaging of objects in scattering media,” J. Opt. Soc. Am. A 15, 367–374 (1998). [CrossRef]

].

References and Links

1.

K. von Frisch, “Die polarisation des himmelslichtes als orientierender faktor bei den tanzen der bienen,” Experientia 5, 142–148 (1949). [CrossRef]

2.

T. Labhart, “Polarization opponent interneurons in the insect visual system,” Nature 331, 435–437 (1988) [CrossRef]

3.

R. Wehner, “Neurobiology of polarization vision,” Trends in Neurosciences 12, 353–359 (1989). [CrossRef] [PubMed]

4.

T. H. Waterman, “Polarization sensitivity,” in The Handbook of Sensory Physiology vol. VII/6B Vision in Invertebrates, edited by H. Autrum (Springer-Verlag, New York, 1981).

5.

R. Wehner, “Polarized-light navigation by insects,” Scientific American 235, 106–114 (1976). [CrossRef] [PubMed]

6.

J. N. Lythgoe and C. C. Hemmings, “Polarized Light and Underwater Vision,” Nature 213, 893–894 (1967). [CrossRef] [PubMed]

7.

M. P. Rowe, E. N. Pugh, Jr., J. S. Tyo, and N. Engheta, “Polarization-difference imaging: a biologically inspired technique for observation through scattering media,” Opt. Lett. 20, 608–610 (1995). [CrossRef] [PubMed]

8.

J. S. Tyo, M. P. Rowe, E. N. Pugh, Jr., and N. Engheta, “Target detection in optically scattered media by polarization-difference imaging,” Appl. Opt. 35, 1855–1870 (1996). [CrossRef] [PubMed]

9.

J. S. Tyo, E. N. Pugh, Jr., and N. Engheta, “Colorimetric representation for use with polarization-difference imaging of objects in scattering media,” J. Opt. Soc. Am. A 15, 367–374 (1998). [CrossRef]

10.

W. Curran and O. J. Braddick, “Speed and direction of locally-paired dot patterns,” Vision Res. 40, 2115–2124 (2000). [CrossRef] [PubMed]

11.

W. A. van de Grind, A. J. van Doorn, and J. J. Koenderink, “Detection of coherent motion in peripherally viewed random-dot patterns,” J. Opt. Soc. Am. 73, 1674–1683 (1983). [CrossRef] [PubMed]

12.

E. D. Grossman and R. Blake, “Perception of coherent motion, biological motion and form-from-motion under dim-light conditions,” Vision Res. 39, 3721–3727 (1999). [CrossRef]

13.

J. S. Tyo, “Optimum linear combination strategy for an N-channel polarization-sensitive imaging or vision system,” J. Opt. Soc. Am. A. 15, 359–366 (1998). [CrossRef]

OCIS Codes
(110.0110) Imaging systems : Imaging systems
(230.5440) Optical devices : Polarization-selective devices
(260.5430) Physical optics : Polarization
(330.1880) Vision, color, and visual optics : Detection

ToC Category:
Research Papers

History
Original Manuscript: May 20, 2003
Revised Manuscript: June 21, 2003
Published: June 30, 2003

Citation
K. M. Yemelyanov, M. A. Lo, E. N. Pugh, Jr., and N. Engheta, "Display of polarization information by coherently moving dots," Opt. Express 11, 1577-1584 (2003)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-11-13-1577


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References

  1. K. von Frisch, “Die polarisation des himmelslichtes als orientierender faktor bei den tanzen der bienen,” Experientia 5, 142–148 (1949). [CrossRef]
  2. T. Labhart, “Polarization opponent interneurons in the insect visual system,” Nature 331, 435–437 (1988) [CrossRef]
  3. R. Wehner, “Neurobiology of polarization vision,” Trends in Neurosciences 12, 353–359 (1989). [CrossRef] [PubMed]
  4. T. H. Waterman, “Polarization sensitivity,” in The Handbook of Sensory Physiology vol. VII/6B Vision in Invertebrates, edited by H. Autrum (Springer-Verlag, New York, 1981).
  5. R. Wehner, “Polarized-light navigation by insects,” Scientific American 235, 106–114 (1976). [CrossRef] [PubMed]
  6. J. N. Lythgoe, C. C. Hemmings, “Polarized Light and Underwater Vision,” Nature 213, 893–894 (1967). [CrossRef] [PubMed]
  7. M. P. Rowe, E. N. Pugh, Jr., J. S. Tyo, N. Engheta, “Polarization-difference imaging: a biologically inspired technique for observation through scattering media,” Opt. Lett. 20, 608–610 (1995). [CrossRef] [PubMed]
  8. J. S. Tyo, M. P. Rowe, E. N. Pugh, Jr., N. Engheta, “Target detection in optically scattered media by polarization-difference imaging,” Appl. Opt. 35, 1855–1870 (1996). [CrossRef] [PubMed]
  9. J. S. Tyo, E. N. Pugh, Jr., N. Engheta, “Colorimetric representation for use with polarization-difference imaging of objects in scattering media,” J. Opt. Soc. Am. A 15, 367–374 (1998). [CrossRef]
  10. W. Curran, O. J. Braddick, “Speed and direction of locally-paired dot patterns,” Vision Res. 40, 2115–2124 (2000). [CrossRef] [PubMed]
  11. W. A. van de Grind, A. J. van Doorn, J. J. Koenderink, “Detection of coherent motion in peripherally viewed random-dot patterns,” J. Opt. Soc. Am. 73, 1674–1683 (1983). [CrossRef] [PubMed]
  12. E. D. Grossman, R. Blake, “Perception of coherent motion, biological motion and form-from-motion under dim-light conditions,” Vision Res. 39, 3721–3727 (1999). [CrossRef]
  13. J. S. Tyo, “Optimum linear combination strategy for an N-channel polarization-sensitive imaging or vision system,” J. Opt. Soc. Am. A. 15, 359–366 (1998). [CrossRef]

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