Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

Journal of Bionic Engineering 8 (2011) 440–448

Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly Haipeng Chen1,2, Xuanjing Shen1,2, Xiaofei Li1,2, Yushan Jin1,2 1. College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China 2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China

Abstract To satisfy the requirements of real-time and high quality mosaics, a bionic compound eye visual system was designed by simulating the visual mechanism of a fly compound eye. Several CCD cameras were used in this system to imitate the small eyes of a compound eye. Based on the optical analysis of this system, a direct panoramic image mosaic algorithm was proposed. Several sub-images were collected by the bionic compound eye visual system, and then the system obtained the overlapping proportions of these sub-images and cut the overlap sections of the neighboring images. Thus, a panoramic image with a large field of view was directly mosaicked, which expanded the field and guaranteed the high resolution. The experimental results show that the time consumed by the direct mosaic algorithm is only 2.2% of that by the traditional image mosaic algorithm while guaranteeing mosaic quality. Furthermore, the proposed method effectively solved the problem of misalignment of the mosaic image and eliminated mosaic cracks as a result of the illumination factor and other factors. This method has better real-time properties compared to other methods. Keywords: bionic compound eye, panoramic image, image mosaic, direct mosaic algorithm Copyright © 2011, Jilin University. Published by Elsevier Limited and Science Press. All rights reserved. doi: 10.1016/S1672-6529(11)60049-8

1 Introduction In recent years, image mosaic technology[1] has been widely studied to obtain panoramic images with large fields of view and high resolution[2–4]. With the development of anatomy and morphology, researchers began to study the structure of vision systems. For example, Wang et al. proposed a bionic machine eye model according to a biological vision system[5]. Mueller and Labhart researched the vision system of a spider and established a bionic imaging system[6]. In nature, the superposition compound eye of a fly contains a large number of small single eyes, and each small eye captures an image independently. All the images captured by the small eyes are merged into a panoramic image with a large field of view[7]. Although the field of view of each single eye is small, the field of view of the entire compound eye comprised of a large number of these single eyes is much wider than the human eyes. Furthermore, the compound eye has small size, light Corresponding author: Yushan Jin E-mail: [email protected]

weight, and high sensitivity. The horizontal field of view of some insect eyes can reach 240 degrees, and the vertical field of view can be up to 360 degrees. In contrast, a person’s field of view scope is only 180 degrees[8]. Inspired by the large field of view of an insect’s vision system, in 2003, Hidetoshi proposed a projection reconstruction method to collect images according to the intensity changes on the surface of the small eye in the compound eye visual system[9]. In 2004, Wernet and Desplan proposed a bionic image mosaic method based on the large field of view and the high sensitivity of the fly’s compound eye[10]. In 2005, Horridge built a bionic model of a detector based on compound eye imaging, which has high precision[11]. Li et al. made a bionic reconstruction of images collected by compound eyes based on a super-resolution algorithm, which improved the image quality[12]. Zhang et al. researched the cambered compound eye image system[13]. This research introduced an array of the cambered field lens, which enlarged the field of view and improved the image

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quality in the marginal field, although this entire system is still in the modeling stage. Based on the compound vision, Sun et al. proposed a bionic method for collecting images of mobile objects from multiple angles in a continuous field of view[14]. Huang and Xu also designed a bionic compound eye measurement system for a mobile object[15], which accomplished image synthesis of seven video groups utilizing the relativity. Thus, the image with a large field of view was synthesized through mosaic. However, due to the higher mosaic complexity of forming the panorama image with a large field of view, the speed of the system was too slow. Based on these factors, to increase the speed of the mosaic algorithm and thus to enhance the real-time performance of the system, we take the improvement of mosaic technology from the perspective of improving the hardware into consideration. The compound eye of the fly can receive multi-angle views simultaneously, and based on this mechanism, a bionic compound eye vision system was proposed and designed in this study. The system applied a direct mosaic method for a panoramic image by imitating the vision mechanism of the fly’s compound eye. In this method, through processing multiple visual channels in parallel, the images or video information obtained by several CCD cameras can quickly synthesize the panoramic image with the large field of view, which improves the real-time properties of the system.

or a superposition compound eye. Each small eye of the former forms independent partial images of an entire object and the whole compound eye creates a “mosaic image” of an object. For the superposition compound eye, the image that each small eye captures is an overlapping image of the object. The resolution of the image formed by a paratactic compound eye is higher than a superposition compound eye. However, superposition compound eye has a higher utilization rate in terms of energy and sensitivity. Therefore, it is suitable to make a mosaic of a panoramic image with a large field of view.

2 The design of the bionic compound eye vision system

(b) The compound eye under the microscope

2.1 The imaging mechanism of a fly’s compound eye An insect’s compound eye is a combination of many small eyes in the form of a cambered array, with the number of small eyes ranging from several hundreds to tens of thousands[16]. The appearance of the compound eyes of a fly is shown in Fig. 1. The fly’s eye is composed of many small eyes with a hexagram structure. Each compound eye of a fly has probably 3000 – 3200 small eyes[17]. Because these small eyes assume a cambered array distribution, thus the compound eye has a tremendous spectrum of view angles, and may nearly achieve the entire field of view of 360 degrees. Fig. 2 shows qualitatively the contrast between a single eye and a compound eye in terms of resolution and field of view. A compound eye is either paratactic compound eye

(a) The appearance of fly’s compound eye

Fig. 1 Compound eyes of the fly. Resolution

Single eye Compound eye

í180

í90

0 90 Angle of view field (Û)

180

Fig. 2 Comparison between the compound eye and the single eye.

Fig. 3 is the schematic diagram of superposition compound eye, where m stands for the cornea, n for the encone, r for the rhabdome of the optical receiver, L for the overlap aperture, CZ for the vacant area, which is also called the transparent area.

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CCD2

L

CCD3

Arm is free to turn

Arm(can expand and contract and change direction.)

CCD1 m

Pole(the length can be adjusted)

n

CZ

CCD4 Cloud terrace

r

 Fig. 4 Bionic compound eye vision system. Fig. 3 Schematic diagram of superposition in the compound eye.

Each small eye has its own view scope, the light outside is sent to the rhabdome and enter into the vision nervous system through the cornea lens and the encone. The rhabdome behind each small eye can receive the light of refraction from each small eye. In other words, each optical receiver may receive light that is within the scope of the field of view corresponding to many cornea lenses. 2.2 Design of a bionic compound eye vision system According to the mechanism of a fly’s compound eye, a bionic compound eye vision system is designed that simulates the fly’s compound eye vision mechanism. The individual small eyes of a compound eye are mostly hexagonal, and each corneal lens is usually also a hexagon. Firstly, when imitating a single aperture contour’s unique feature, there are 7 CCD cameras in the designed vision system, with 6 CCD that are arranged around the central CCD at the 6 vertices of a hexagon. Thus, the whole field of view can be expanded. However, a CCD’s photosensitive surface area is a rectangle. Even if a CCD is specially made into a hexagon, the overlapping situation between sub-images will be very complex because it does not have enough theoretical support to obtain non-overlapping sub-images. To avoid the above question, when we splice with rectangular sub-images, only 4 CCDs are used for the arrangement of spatial rectangle, because the sub-image is a rectangle, as shown in Fig. 4. The system is mainly composed of 4 CCD cameras[18,19], with an arm for mounting CCD cameras and a cloud terrace.

The system has the following characteristics: (1) There are 4 CCD cameras, which are similar to a fly’s small eye and are responsible for collecting images. (2) The length of the support arm can be adjusted, and the support arm can revolve around the fixed end of the support arm. This configuration guarantees that the vision system can collect images of the two neighboring fields of view. (3) The length of the pole can be adjusted, which makes the gathering of images free from the limitation of the capture height. (4) The pole is fixed on the cloud terrace, which can adjust the level of rotation and the angle of pitching. This structure guarantees precise adjustment of the width and height when gathering the image’s field of view. The spatial positions of the 4 CCD cameras form a rectangle, and have strict rectangular relationships in the space. In other words, the images obtained are in strict alignment. This configuration guarantees that the overlap area between sub-images is regular; therefore, these images can be used to allow the direct mosaic of the panoramic image with large field of view, which will be analyzed in detail in section 3.

3 The direct mosaic of a panoramic image based on a bionic compound eye vision system 3.1 The direct mosaic algorithm Differences in the image registration algorithms determine the quality of image mosaic. However, image registration algorithms at present require substantial amounts of computation. Thus, the mosaic technology relies excessively on the algorithm with large number of

Chen et al.: Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

operations, which causes poor real-time performance of the system. There are 4 CCD cameras that are responsible for capturing images in the bionic compound eye vision system, and the configuration of their positions in space is a rectangle. Because the 4 CCD cameras have strict rectangular placement relative to each other, the images obtained are strictly aligned. Therefore, image registration is not needed in the process of constructing an image mosaic. According to the formation principles of optical image, if a camera’s parameters and the object distance are specified, the visual scope of the field obtained by the camera is correspondingly determinate. If fields of vision among cameras overlap, the overlapping proportion is also determinate with respect to the two images. Suppose that the distance between two cameras is D, the axes of the two lenses are parallel, d is the length of the diagonal line of the CCD photosensitive surface within a camera, f is lens’ focal distance, l is the object’s distance, and ș is the photographic system’s angle of view. Suppose that the length, width and diagonal line length of scene be a, b and c, respectively. Then, E is the width of the field of view’s overlapping region between two cameras. It can be determined from the design principle of the CCD that the proportion between the length and the breadth of the CCD is 4:3[19]. According to the analog principle, the proportion for the length and width of the field of view is also 4:3. Thus, according to the Pythagorean theorem, it can be determined that a:b:c = 4:3:5. When two optical parameters are the same and the cameras capture images in an identical direction at certain distance, the field of view overlap configuration is shown in Fig. 5. From Fig. 5 we obtained following equations

c a

T

2 ˜ l ˜ tan , 2 4 c 5

(1)

8 T ˜ l ˜ tan . 5 2

(2)

Suppose that D > E (namely, E < a/2), then D E

a a E  (  E)  (  E) 2 2 aD

8 T ˜ l ˜ tan  D. 5 2

a  E,

(3) (4)

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If E > a/2, it can be proved that the above conclusion is invariable. In other words, if the camera’s angle of view, the object distance and the neighboring two cameras’ space for the bionic compound eye vision system are specified, the field of view’s overlapping proportion between two neighboring cameras is determinate. Therefore, the functional relationship of the overlapping proportion can be expressed as E

g (d , f , l ),

(5)

where d is the length of the diagonal line of the CCD photosensitive surface within the camera, which is a determined value. When the object distance l is fixed, the focal distance f is also determined. Furthermore, the overlapping width E can be obtained by Eq. (4). CCD1 diagonal

CCD2 diagonal

d

d f

f D

Lens 1

Lens 2 ș/2

ș/2 l

b

l

c Scene 1

Scene 2 E

a

Fig. 5 The analysis of two neighbors CCDs.

From the above analysis, we can conclude that there is an overlap in the scene information captured by 4 small eyes and images in the overlapping area are regular in the system designed here. When the entire vision system’s optical parameters and the object distance are specified, the overlapping proportion among images is determinate. Because the resolution of a single CCD is higher and the field of view is larger than the fly’s small eye, our system assures that the images obtained by two CCDs overlap mutually (CCD does not need to retain the same place). The images obtained by several CCDs are only needed to cut off the overlap of the neighboring two images, so that a panoramic image with large field of

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view can be mosaicked, which expands the field as well as guarantees a high resolution. 3.2 The direct mosaic method for a panoramic image After capturing images using the bionic compound eye vision system, we can perform direct mosaic assembly to produce a panoramic image. The direct mosaic method has three main aspects: the direct mosaic algorithm, the image’s misalignment adjustment and the mosaic crack elimination. As shown in Fig. 6, it is the concrete process. The bionic compound eye vision system

Collect images

Large field of view panorama image

The direct mosaic algorithm

Dislocation ? No

Elimination for mosaic crack

parts P and Q, where Q is the cut from the left view, and P is the cut from the right view. In the same way, for the overlap in the image on the top and bottom, M is the cut from the top image, and N is the cut from the bottom image. These images are mosaicked forming a panoramic image with a large field of view.

Yes Dislocation adjustment

Fig. 6 Assembly of a direct mosaic for a panoramic image.

Fig. 7 A schematic diagram of the direct mosaic algorithm.

 3.2.1 The direct mosaic algorithm The direct mosaic algorithm is an algorithm that cuts a series of original images and mosaics them into an image with large field of view. Only cutting is performed, and there is no need to search for the same content of in two neighboring images. Thus, we do not need an algorithm to determine the relative position of two neighboring images. Thus, the computational speed is greatly improved so that real-time computations can be performed effectively. The premise of using a direct mosaic algorithm is to obtain a group of images that have known overlapping proportions. The relationships of the positions of the 4 CCDs in space are rectangular in the bionic compound eye vision system for this study, with the 4 CCDs responsible for capturing the images. The positions in space of the 4 CCDs are strictly rectangular, which is guaranteed by the vision system. As a result, the images captured are strictly in alignment and an additional algorithm is not needed to store image in the process of mosaic. Fig. 7 shows a schematic diagram of the overlap in the field of view when the 4 CCDs work. The portion in the shadow is the overlapping section. Taking the above two images as an example, the crosswise overlapping section is carried on with an equal division for the two

3.2.2 The image’s misalignment adjustment In the process of the vision system work with a mosaic algorithm, the direct mosaic image may have a trivial misalignment because two neighboring cameras are not strictly aligned. Moreover, the luminous intensity of the image samples will be different for the same camera at a different time and for the different cameras at the same time. Therefore, there may be a mosaic misalignment at the boundaries within the mosaic image. It is necessary to adjust the mosaic image to make it perfect. If two images align strictly, then the similarity in the gray intensity corresponds to the best in the mosaic place. In contrast, if the two images have a misalignment, then the more the misalignment is, the smaller the similarity is. Thus, the optimum matching position of image mosaic edge is searched based on the similarity in gray intensities. For two images that need mosaic, we take the 56+5 mosaic edge of one image as a reference and move the mosaic edge of the other image upward or downward progressively by one pixel. For each movement, the correlation coefficient of the two edges is calculated. The pixel value corresponding to the highest correlation coefficient is the value of the misalignment of the two images. If I3 and I4 are two images that are vertically

Chen et al.: Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

misaligned. The steps of adjusting the misalignment images are as follows: (1) Take the last row’s n/m length of the left view as row1, and the first row’s n/m length of the right view as row2 (n/m is determined by the overlapping proportion of the actual gathered images). (2) row2 is invariable; row1 decreases downward progressively by one pixel and obtains a correlation coefficient of row1 and row2. (3) The pixel value corresponding to the highest value of the correlation coefficient is the misalignment value of the two images. The image misalignment caused by system error is not large, and should be smaller than 4% for the entire pixel length. Thus, the length of searching may only take 4% of the entire pixel length. This method is quite fast because the searching scope is a one-dimensional array. 3.2.3 The reprocessing of mosaic image The mosaic may have an obvious splicing trace due to a difference in camera luminous intensities. To obtain a panoramic image that is seamless, it is necessary to eliminate the mosaic crack. The main methods for eliminating the mosaic crack focus on smoothing the luminous intensity at the crack for the removal of sudden change in intensities. Many such methods can be found at present, and three types of them are most commonly used: the wavelet transformation method[20], the mean filter method[21], and the weighted average method[22]. The wavelet transformation method can improve both the evenness and clarity for an image, but the algorithm is quite complex and involves a massive floating point calculation and a boundary treatment question. Thus, it occupies a large amount of memory and spends a lot of time. The processing speed of the mean filter method is faster, but results from the smooth decrease the resolution of the image. The weighted average method achieves smooth mosaic transition slowly from the left overlapping area to the right overlapping area. This method is simple, intuitive and faster, and thus it is the most commonly used method. In this study, the improved weight synthesis method is used to eliminate the mosaic crack. The main idea for this method is that the weight value changes along with the distance between the pixel and the boundaries of the overlapping region. Suppose that I1 and I2 be the images that need to be mosaicked, and I is the new image after connecting the original two images. Suppose that, in a

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specific position, I1 and I2 overlap at the point [x1, x2]. Then, the gray value for point x in I is:

I ( x)

I1 ( x) ˜ k1 ( x)  I 2 ( x) ˜ k2 ( x),

(6)

where I1(x) and I2(x) are the gray values at point x of the first image and the second image, respectively. k1(x) and k2(x) are the corresponding weight values of I1(x) and I2(x). Furthermore, these values are proportional to the change in the distance between the pixel x and the overlapping area. The weight values of k1(x) and k2(x) are calculated as follows: Suppose that point x in the overlap region be denoted [x1, x2], in the computational process, the differential value between x and x1 is first calculated, which is the distance from point x to the boundary of the overlapping region in image1; If the weight is proportional to this distance, then k1(x) is obtained. In the same way, the differential value between x and x2 is also calculated, that is, the distance form point x to the boundary of the overlapping region in image2, and then k2(x) can be obtained. The schematic diagram is as shown in Fig. 8.

Fig. 8 The schematic diagram of the mosaic crack elimination algorithm.

4 Results and discussion 4.1 The experimental results In the experiments, the camera has a CCD of 1/2.5 inches, a focal distance of 19.2 mm, the distance from object to the lens is 4.5 m. The images obtained by the bionic compound eye vision system designed in this study are shown in Figs. 9a, 9b, 9c and 9d, where the size of original image obtained by selecting the CCD camera is 2600 ×2000 pixels, and the overlapping portion is as follows: the overlapping of Fig. 9a and 9b is 8%, which is the same as the overlapping of Fig. 9c and 9d. The overlapping of Figs. 9a and 9c is 45%, which is the same as the overlapping of Figs. 9b and Fig. 9d. The overlapping sections of the field of view are cut off according to the overlap proportion obtained in the demarcation

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experiment, and then the direct mosaic is performed to get the panoramic image with a large field of view. The experimental result is shown in Fig. 9e, which shows that there is a small up and down misalignment. The image adjusting the misalignment is shown in Fig. 9f. Finally, the mosaic crack is eliminated to obtain the panoramic image, as shown in Fig. 9g.

 (a) Left upper view

(c) Left lower view

(b) Right upper view

(d) Right lower view

(e) The panorama image obtained by direct mosaic.

(f) The panorama image through adjustment.

(g) The panorama image of eliminating mosaic crack.

Fig. 9 The mosaic of large field of view panorama image.

4.2 Comparison of the direct mosaic algorithm with the traditional mosaic algorithm To test the performance of algorithm in this work, we performed a contrastive analysis with the traditional image mosaic method. The traditional image mosaic technology is mostly based on image registration, and mainly includes two steps: the registration and the fusion. Firstly, this technique performs registration for the image step by step, and then it fuses the two images into a larger image. The image mosaic technology mainly considers three factors: time consumption, overlapping proportion requested and accuracy requirement. There-

fore, our contrasting experiments were performed under the same environment, and we obtained data to make the following comparative analysis: (a) Camera parameters: CCD is 1/2.5 inches, the focal distance is 19.2 mm, and the object distance is 4.5 m. (b) The size of the original image captured is 2600 × 2000 pixels, and we selected 200 images to test. (c) Run the two mosaic algorithms in the same environment on a Pentium CPU E5800 and OpenCV. Based on the results from the time consumption and the image mosaic quality, we made the following observations: (1) These two types of methods can both achieve the image mosaic. The image registration of the traditional image mosaic technology generally needs much more time. Discovery from experiments under similar conditions shows that the running time of the algorithm based on the template match is 60.421 s, while the running time of the direct mosaic algorithm is 3.984 s, which is only 6.6% of the traditional algorithm. Compared to the fast image mosaic algorithm based on the grid match, the direct method also greatly decreases the running time. The real-time performance of this system is enhanced. (2) If the traditional mosaic algorithms based on the registration achieve a good mosaic effect, the overlapping proportion requested should not be smaller than 30%. When the overlapping proportion is 50%, the mosaic effect is the best[23]. The direct mosaic algorithm has no restriction for the overlapping proportion, and the overlapping proportion does not influence the mosaic effect. Thus, under the premise of guaranteeing a mosaic effect, the panoramic image with the large field of view obtained by the traditional mosaic method for two images can be expanded at most by 50%, while images obtained by the bionic compound eye vision system in this study can be expanded by 100% at most. If all 4 CCDs take an image at the same time, the expanded proportion can approach 300%. (3) The direct mosaic algorithm requires higher alignment accuracy for multi-CCDs. This is a disadvantage of the direct mosaic algorithm in comparison to other image mosaic algorithms based on matching. However, focusing on the error in image capturing caused by poor alignment precision and the misalignment after that, a searching is conducted in our method

Chen et al.: Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

for the optimum matching position of the image mosaic edge through adjusting the relevant size of the mosaic location, which can compensate the above disadvantage in a certain degree. The compared results are shown in Table 1. Table 1 The comparison of traditional algorithm and algorithm in this study 

Traditional algorithm

Algorithm in this study

The quantity of experimental image

120

120

Image size

2600 × 2000 pixels

2600 × 2000 pixels

The average running time

56.312 s

3.826 s

The requirement of overlapping proportion

Not smaller than 30%

No request

The proportion of expanding field of view

Most 50%

Approach 300% for 4 CCD

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Science Grant (No.60973090), Natural Science Grant of Jilin Province (No.201115025), and Graduate Innovation Fund of Jilin University (No. 20111063).

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5 Conclusion Focusing on the low speed of existing mosaic algorithms in the panoramic image with a large field of view, and inspired by the fly’s compound eye, a bionic compound eye vision system was designed by imitating the visual mechanism of the fly’s compound eye, and a direct panoramic image mosaic algorithm was presented. Compared with the traditional mosaic algorithms, our algorithm directly mosaics several captured sub-images into a panoramic image with large field of view, without needing a registration algorithm that searches for a precise mosaic position. The experimental results showed the following advantages of our algorithm: (1) This algorithm directly mosaics several sub-images, which enhances the real-time performance of the system; (2) This algorithm has no requirements on the overlapping proportion of sub-images, which can expand the field of view with larger proportion; (3) A misalignment adjustment strategy was designed in this algorithm, which can also be applied to other mosaic algorithms for a higher precision. However, one disadvantage of this algorithm is that a high alignment precision is required between two neighboring CCD sub-images. Thus, how to decrease the high precision required in the alignment of CCD sub-images is the main point that needs improvement in our future study.

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