# Spherical Box-Counting: Combining 360° Panoramas with Fractal Analysis

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## Abstract

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## 1. Introduction

#### 1.1. Aesthetic Qualities, Fractal Geometry, and Architecture

#### 1.2. Architecture and Fractals

#### 1.3. Spherical Perspective Systems

## 2. Materials and Methods

#### 2.1. Box-Counting Method and Complexity

#### 2.1.1. Standard Box-Counting and Influences on the Method

#### 2.1.2. Differential and Improved Differential Box-Counting

- $M=m$ × $\epsilon $ and $N=n$ × $\epsilon $, indicating an even partitioning in the x- and y-directions;
- $M>m$ × $\epsilon $ and $N=n$ × $\epsilon $, indicating an uneven partitioning in the x-direction;
- $M=m$ × $\epsilon $ and $N>n$ × $\epsilon $, indicating an uneven partitioning in the y-direction;
- $M>m$ × $\epsilon $ and $N>n$ × $\epsilon $, indicating an uneven partitioning in the x- and y-directions.

#### 2.1.3. Differential Box-Counting for Color Images

#### 2.1.4. Implementation

#### Grayscale Images

- Grayscale DBX M × M (m × m): The original photograph is reduced and cropped to a section of 512 × 512 pixels ($M$ × $M$). In the case of a rectangular original image, the section can be freely selected in one direction using a slider. Because the original image is a square of 512 × 512 side lengths, the boxes of the grid are also square ($m$ × $m$).
- Grayscale DBX M × N (m × n): With this method, the original photograph is scaled to an image with a maximum side length of 512 pixels while maintaining the aspect ratio ($M$ × $N$) and portion. Box sizes are based on the shorter side of the image. The longer side is divided by this box size and rounded down to get an integer number of boxes. The aspect ratio of a box in the grid can then also be rectangular ($m$ × $n$) and not square.
- Grayscale DBX M × N (m × n) (shifting boxes): This method is the same as number 2 but with moved stacks of cubes.
- Grayscale DBX M × N (m × m) (shifting boxes) (images will be distorted to 512 × 256): This method was developed in the year 2022 to meet the requirements of measuring spherical images. Here, the original image is adjusted to a rectangle of 512 × 256 pixels ($M$ × $N$; this corresponds exactly to the aspect ratio of 1:2 of a spherical image). The box sizes, in turn, are square ($m$ × $m$).

#### Color Images

- Color DBX M × M (m × m) (shifting boxes): Analogously to method 3 of the grayscale images, a square section ($M$ × $M$) is examined with a square grid ($m$ × $m$).
- Color DBX M × N (m × n) (shifting boxes): Analogously to method 4 of the grayscale images, the aspect ratio of the original image is preserved ($M$ × $N$) and examined with a rectangular grid ($m$ × $n$).
- Color DBX M × N (m × m) (shifting boxes) (images will be distorted to 512 × 256): Analogously to method 5 of the greyscale images, the original image is deformed to a size of 512 × 256 pixels and examined with a square mesh.

#### 2.1.5. Surroundings and Vegetation

#### 2.2. Spherical Representation

#### 2.3. Special Implications of the Method

## 3. Results

#### 3.1. Test Cases

#### 3.1.1. Simple Parametrical CAD Models

#### 3.1.2. Spherical Photographs

- Figure 12a: “Chicago Skyline” with a large portion of greenery, ${D}_{B}=2.340$;
- Figure 12b: Federal Center Plaza in Chicago with the “Flamingo” by Alexander Calder from 1974, with a large portion of a uniform-colored plaza and a red colored sculpture in the foreground, ${D}_{B}=2.375$;
- Figure 12j: the Wingspread or Herbert F. Johnson House by Frank Lloyd Wright from 1937 in Wind Point, Wisconsin, with a considerable amount of greenery, ${D}_{B}=2.361$;
- Figure 12l: Quadracci Pavilion by Santiago Calatrava from 2001 in the Milwaukee Art Museum, Wisconsin, with a large portion of uniform floor design and similarly colored building areas, ${D}_{B}=2.338$;
- Figure 12m: the same white-colored building with a large portion of blue sky and uniform floor design, ${D}_{B}=2.338$.

## 4. Discussion

## 5. Conclusions and Outlook

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Partial spherical grid produced on the basis of Formula (1) [34].

**Figure 3.**Four straight–linear perspectives calculated in GoPro

^{®}VR player 3.0.5 software on the basis of a 360-degree spherical perspective image created with Blender

^{®}3.3.0 software.

**Figure 4.**Differential box-counting method for grayscale images. The yellow highlighted stack of cubes shows the difference between the brightest and darkest pixel for one specific cell (i;j) of a certain grid size.

**Figure 5.**Differential box-counting of the web application “FRACAM”, showing the castle of Ghent, Belgium. (

**Left top**,

**left middle**): results, with ${D}_{Red}$ = 0.471, ${D}_{Green}$ = 0.470, and ${D}_{Blue}$ = 0.466; with ${Correlation}_{Red}$ = 0.471, ${Correlation}_{Green}$ = 0.999949, and ${Correlation}_{Blue}$ = 0.999921; ${D}_{Average}$ = 2.469; ${D}_{col}$ = 3.408; (

**left bottom**): used box sizes; (

**right side**): RGB separation.

**Figure 6.**Spherical perspective rendering produced in Blender with an overlay of curves representing the first and third dimensions according to the formula in [34] (cylindrical roll-out) and the second dimension in vertical lines according to the torus-like roll-out of the grid in this direction.

**Figure 7.**Checkerboard measurement with the “Grayscale DBX M × M (m × m)” method. (

**Left**): Image section with a matching grid (${D}_{B}=2$ with a correlation of 1.0). (

**Right**): Image section that was moved relative to the grid (${D}_{B}=2.028$ with a correlation of 0.999983).

**Figure 8.**Measurement method “Grayscale DBX M × N (m × m) (shifting boxes)” with the grayscale conversion adapted to the perception of brightness (see Formula (13)) applied to a red-tinted (

**left**; ${D}_{B}=2.391$ with a correlation of 0.998568), green-tinted (

**center**; ${D}_{B}=2.356$ with a correlation of 0.998986), and blue-tinted façade (

**right**; ${D}_{B}=2.372$ with a correlation of 0.998868).

**Figure 9.**Measurement method “Grayscale DBX M × N (m × m) (shifting boxes)” with the grayscale conversion adapted to the perception of brightness (see Formula (13)) applied to a closed (

**left**; image “22_11_01 Hof03_w_White”; ${D}_{B}=2.363$ with a correlation of 0.998963) and an open square (

**right**; image “22_11_01 Hof02_w_White”; ${D}_{B}=2.332$ with a correlation of 0.999166).

**Figure 10.**Measurement method “Grayscale DBX M × N (m × m) (shifting boxes)” with the grayscale conversion adapted to the perception of brightness (see Formula (13)) applied to portrait windows of the opposite and right façades (

**left**; image “22_11_02 Hof01_w”; ${D}_{B}=2.360$), lower windows on the right façade (

**center**; image “22_11_02 Hof02_w”; ${D}_{B}=2.366$ ), and additional smaller windows of the opposite façade (

**right**; image “22_11_02 Hof03_w”; ${D}_{B}=2.367$ ).

**Figure 11.**Measurement method “Grayscale DBX M × N (m × m) (shifting boxes)” with the grayscale conversion adapted to the perception of brightness (see Formula (13)) applied to different heights of the opposite and right façades (

**left**: image “22_11_03 Hof01_w_White”, ${D}_{B}=2.370$;

**center**: image “22_11_03 Hof02_w_White”, ${D}_{B}=2.380$;

**right**: image “22_11_03 Hof03_w_White”; ${D}_{B}=2.387$ ).

**Figure 12.**Measurement method “Grayscale DBX M × N (m × m) (shifting boxes)” with the weighted grayscale conversion adapted to the perception of brightness (see Formula (13)) applied to different spherical photographs.

**Figure 13.**Measurement method “Color DBX M × N (m × m) (shifting boxes)” with the weighted grayscale conversion (see Formula (13)) applied to different spherical photographs.

**Figure 14.**Measurement method “Color DBX M × N (m × m) (shifting boxes)” with the standard grayscale conversion (see Formula (12)) applied to different spherical photographs.

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**MDPI and ACS Style**

Kulcke, M.; Lorenz, W.E.
Spherical Box-Counting: Combining 360° Panoramas with Fractal Analysis. *Fractal Fract.* **2023**, *7*, 327.
https://doi.org/10.3390/fractalfract7040327

**AMA Style**

Kulcke M, Lorenz WE.
Spherical Box-Counting: Combining 360° Panoramas with Fractal Analysis. *Fractal and Fractional*. 2023; 7(4):327.
https://doi.org/10.3390/fractalfract7040327

**Chicago/Turabian Style**

Kulcke, Matthias, and Wolfgang E. Lorenz.
2023. "Spherical Box-Counting: Combining 360° Panoramas with Fractal Analysis" *Fractal and Fractional* 7, no. 4: 327.
https://doi.org/10.3390/fractalfract7040327