WO2020232985A1 - 基于颜色表优化的二维标量场数据可视化方法及系统 - Google Patents
基于颜色表优化的二维标量场数据可视化方法及系统 Download PDFInfo
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Definitions
- the present disclosure relates to the technical field of two-dimensional scalar field data visualization, and in particular to a two-dimensional scalar field data visualization method and system based on color table optimization.
- Two-dimensional scalar data is a common way of data expression in scientific simulations and applications.
- the visualization of two-dimensional scalar data is of great significance for scientists to discover features and distinguish values in data.
- color table mapping is an effective and commonly used method, which refers to the mapping process of assigning distinguishable colors to quantifiable data values.
- Different color tables have great differences in expressing the characteristics of the data. A good color table can make the changes contained in the data clearer, and a bad color table may hide the changes in the data. Therefore, choosing a good color table is a very important and challenging problem for the visualization of two-dimensional scalar field data.
- the present disclosure provides a two-dimensional scalar field data visualization method and system based on color table optimization
- the present disclosure provides a two-dimensional scalar field data visualization method based on color table optimization
- Two-dimensional scalar field image data visualization method based on color table optimization including:
- the present disclosure also provides a two-dimensional scalar field data visualization system based on color table optimization
- a two-dimensional scalar field image data visualization system based on color table optimization including:
- An input module which is configured to receive the input initial color table and two-dimensional scalar field data; calculate the main colors in the initial color table, and set the main colors as control points;
- a mapping module which is configured to use a piecewise linear function to calculate linear interpolation between two control points, generate a color table, and map the color table to two-dimensional scalar field data;
- the energy optimization equation establishment module is configured to establish an energy optimization equation for the coordinate position of the control point and the mapped two-dimensional scalar field data;
- the coordinate position of the control point is the index value in the color table corresponding to the control point Normalized to obtain a value between 0 and 1;
- the visualization module is configured to solve the energy optimization equation to obtain the optimized control point coordinate position, use the piecewise linear function and the optimized control point coordinate position to generate a new color table, and map the new color table To the two-dimensional scalar field data, the final visualization result is obtained.
- the present disclosure also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and running on the processor.
- an electronic device including a memory, a processor, and computer instructions stored in the memory and running on the processor.
- the computer instructions are executed by the processor, the computer instructions described in the first aspect are completed. Method steps.
- the present disclosure also provides a computer-readable storage medium for storing computer instructions that, when executed by a processor, complete the steps of the method described in the first aspect.
- the present disclosure proposes a data perception-oriented color table optimization method for two-dimensional scalar field data.
- the optimized color table By using the optimized color table, subtle data changes can be highlighted, and it has broad application prospects in the field of scientific visualization.
- the present disclosure proposes a color table optimization method that can highlight subtle data changes, maintain the original color table characteristics, and maximize the contrast of the front background for a two-dimensional scalar data field by organically fusing color table characteristics and data distribution characteristics, and defines multiple
- This interactive exploration method can fully meet the urgent needs for color table design and selection, interactive exploration and other aspects in the process of scientific data analysis and visualization.
- FIG. 1 is a flowchart of this embodiment
- Figure 2(a) is the initial grayscale color table and the color table control points
- Figure 2(b) is the color mapping data corresponding to the color table
- Figure 2(c) shows the revised gray color table and the corresponding color table control points
- Figure 2(d) is the color mapping data corresponding to the color table
- Figure 3(a) shows the input color table and corresponding color mapping data
- Figure 3(b) shows the local differences of the input color mapping data
- Figure 3(c) shows the visualization result of the boundary likelihood function
- Figure 3(d) shows the optimized color table and corresponding color mapping data
- Figure 3(e) shows the local differences of the optimized color mapping data
- Figure 4(a) shows the input color table and corresponding color mapping data
- Figure 5(a) shows the input color table and corresponding color mapping data
- Figure 6(a) is the input color table and corresponding color mapping data, the enlarged part is the visualization of the user's interest area;
- Figure 6(b) shows the normal optimized color table and its corresponding color mapping data; on this basis, the user selects the region of interest, as shown by the white dotted line.
- Figure 6(c) shows the optimization results and corresponding color mapping data of the color table selected by the user to constrain the ROI region of interest
- Figure 7 shows the process of using the background mask tool and the corresponding results of each step.
- this embodiment provides a method for visualizing two-dimensional scalar field image data based on color table optimization
- Figure 1 is a flow chart of the disclosure.
- Two-dimensional scalar field image data visualization method based on color table optimization including:
- S1 Receive the input initial color table and two-dimensional scalar field data; calculate the main color in the initial color table, and set the main color as the control point;
- the initial color table refers to a color table specified by a user or a system default, such as a black and white color table, a rainbow color table, and the like.
- the two-dimensional scalar field data refers to scalar data in a two-dimensional space, such as the temperature distribution on the earth grid plane.
- the main color in the initial color table is calculated and the main color is set as the control point.
- the specific steps are:
- each control point includes the coordinate position of the control point itself on the initial color table and the control point itself s color.
- p w is the coordinate position of the control point w on the color table (0 ⁇ p w ⁇ 1); c w is the color corresponding to the control point w, and l is the set of control points.
- optimization algorithm of this embodiment only adjusts the coordinate position of the control point, and does not change the color of the control point.
- a piecewise linear function is used to calculate the linear interpolation between two control points, generate a color table, and map the color table to two-dimensional scalar field data.
- the specific steps include:
- p represents the coordinate position of the control point
- c represents the color of the control point
- a new color table with multiple colors is generated; the new color table uses the color mapping process ⁇ to establish the mapping relationship between colors and data in subsequent applications.
- the color-mapping process uses , Where p represents the position of the control point, and x represents the value of the data point.
- the maximum-minimum value normalization method is used to normalize it to a parameter space of 0 to 1, and the mapping relationship between the color and the coordinate position of the control point is established by formula (2).
- an energy optimization equation is established for the coordinate position of the control point; the specific steps include:
- the energy optimization equation includes a boundary term, a comparison term, and a weighted sum of fidelity terms.
- x is the two-dimensional scalar field data after mapping
- Is the position of the control point to be optimized
- ⁇ , ⁇ , and ⁇ are the weight parameters
- Figures 5(a), 5(b), 5(c) and 5(d) illustrate the influence of different weighting factors on the results.
- the energy optimization equation is solved to obtain the optimized control point coordinate position; the specific steps include:
- the boundary term whose function is to ensure that the boundary distribution of the color mapping data is consistent with the boundary distribution of the two-dimensional scalar field data; the two-dimensional scalar field data boundary refers to the change between different values;
- the boundary term is defined as:
- q(x i ) represents the boundary likelihood function of the data point x i ;
- i is the label of any data point in the two-dimensional scalar field data, and x i represents the value of the data point;
- ⁇ represents the current data Neighbor points of point i, M represents all data points in the two-dimensional scalar field;
- the color mapping equation can get the corresponding color value of the data point x i ;
- ⁇ . ⁇ represents the Euclidean distance.
- f′(0) represents the maximum value of the first derivative of the two-dimensional scalar data
- f′′(- ⁇ ) represents the maximum value of the second derivative of the two-dimensional scalar data
- the first derivative of the two-dimensional scalar data is calculated by the gradient operator
- the second derivative of two-dimensional scalar data is calculated by Laplace operator
- the parameter ⁇ is the boundary weight, which is used to control the strength of the boundary effect;
- Figure 4(a), Figure 4(b), Figure 4(c) and Figure 4(d) illustrate different ⁇ Optimized results;
- ⁇ is the weight of the region of interest.
- Figure 6(a), Figure 6(b) and Figure 6(c) show the application scenario of this interactive control method in the field of medical image visualization.
- the two-dimensional scalar field data of this scenario is the head radiation dose data of brain tumor patients ( Hereinafter referred to as "radiation dose data")
- the default color table is a rainbow colormap (rainbow colormap).
- Figure 6(a) maps the rainbow color table to the radiation dose data, and superimposes the generated visualization results with the brain CT image with 75% opacity;
- Figure 6(b) shows the use of (4-1), (4) -2)
- the description technology optimizes the rainbow color table and maps it to the visualization result in the radiation dose data.
- the fidelity item :
- ⁇ is the arc length function corresponding to the initial color table; Represents the fidelity item; Indicates the value corresponding to the coordinate position of the optimized control point on the arc length function, ⁇ (p i ) represents the value corresponding to the coordinate position of the initial control point on the arc length function; ⁇ represents the maximum range of the derivative of the arc length function, which is set by default The maximum value of the arc length function.
- the foreground and background contrast items are identical as one or more embodiments.
- the so-called background refers to the data that the user does not want to observe; the background color is represented by a single color C b .
- (6-2) middle Indicates the distance between any numerical point and its nearest background point.
- i represents a certain data point in two-dimensional scalar data, Indicates the closest point found by data point i from the background;
- H(x i ) represents the number of data points whose value is equal to x i in the input data of step S1.
- FIG. 7 shows the application scenario of this interactive control method in the field of medical image visualization.
- the two-dimensional scalar field data of this scenario is the radiation tomography data (Positron Emission Tomograph, hereinafter referred to as "PET data") of lung tumor patients.
- PET data radiation tomography data
- the image with subscript (f) in Figure 7 shows a new color table obtained by optimizing the method described in (6-4) for a richer background area, and mapping it to PET data After the visualization results.
- the input data of the present disclosure specifically includes: any two-dimensional scalar field data, including but not limited to two-dimensional surface temperature field, seawater surface velocity, seawater salinity, tumor radiation dose, UAV electromagnetic simulation data, etc.
- the present disclosure proposes a color table mathematical method that includes boundary terms, fidelity terms, and comparison terms.
- This method innovatively introduces data changes into the evaluation and optimization process of the color table, and adds the fidelity and previous comparison of the original color table. Constraints of background contrast.
- the mathematical expression is highly extensible, and it is easy to expand to add more domain-related prior constraints.
- the present disclosure proposes a region of interest (ROI) interactive exploration and optimization method. Based on the ROI region specified by the user, the optimization algorithm will only optimize the region. In addition, an interactive exploration and optimization method based on background replacement is also proposed, which can help users only pay attention to the changes in the region of interest and the differences in the background.
- ROI region of interest
- This embodiment proposes an optimization algorithm for data perception to design a color table, which is used to highlight continuous features related to data changes in the visualization of a two-dimensional scalar field.
- the core idea of the present disclosure is to transform the color table adjustment into a nonlinear constrained optimization problem, and obtain the optimal color table by iteratively adjusting the parameter positions of the control points in the color table.
- this embodiment also provides a two-dimensional scalar field data visualization system optimized based on a color table
- a two-dimensional scalar field image data visualization system based on color table optimization including:
- An input module which is configured to receive the input initial color table and two-dimensional scalar field data; calculate the main colors in the initial color table, and set the main colors as control points;
- the mapping module is configured to use the piecewise linear function to calculate the linear interpolation between the two control points, generate the color table, and map the color table to the two-dimensional scalar field data; Figure 2(a), Figure 2( b), the control points shown in Figure 2(c) and Figure 2(d);
- the energy optimization equation establishment module is configured to establish an energy optimization equation for the coordinate position of the control point and the mapped two-dimensional scalar field data;
- the coordinate position of the control point is the index value in the color table corresponding to the control point Normalized to obtain a value between 0 and 1;
- the visualization module is configured to solve the energy optimization equation to obtain the optimized control point coordinate position, use the piecewise linear function and the optimized control point coordinate position to generate a new color table, and map the new color table To the two-dimensional scalar field data, the final visualization result is obtained.
- the third embodiment also provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor.
- an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor.
- the computer instructions are executed by the processor, each of the methods is completed. The operation, for the sake of brevity, will not be repeated here.
- the electronic device may be a mobile terminal and a non-mobile terminal.
- the non-mobile terminal includes a desktop computer.
- the mobile terminal includes a smart phone (Smart Phone, such as an Android phone, an IOS phone, etc.), smart glasses, smart watches, smart bracelets, and tablet computers. , Notebook computers, personal digital assistants and other mobile Internet devices that can communicate wirelessly.
- Smart Phone such as an Android phone, an IOS phone, etc.
- smart glasses smart watches, smart bracelets, and tablet computers.
- notebook computers personal digital assistants and other mobile Internet devices that can communicate wirelessly.
- the processor may be a central processing unit CPU, the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-made programmable gate array FPGAs or other Programming logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
- the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
- the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor.
- a part of the memory may also include a non-volatile random access memory.
- the memory can also store device type information.
- the steps of the above method can be completed by hardware integrated logic circuits in the processor or instructions in the form of software.
- the steps of the method disclosed in combination with the present disclosure may be directly embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor.
- the software module may be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers.
- the storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.
- the units that is, the algorithm steps
- the units can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
- the disclosed system, device, and method can be implemented in other ways.
- the device embodiments described above are merely illustrative.
- the division of the units is only a division of logical functions. In actual implementation, there may be other divisions, for example, multiple units or components can be combined. Or it can be integrated into another system, or some features can be ignored or not implemented.
- the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
- the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
- the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code .
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Abstract
本公开公开了基于颜色表优化的二维标量场数据可视化方法及系统,接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
Description
本公开涉及二维标量场数据可视化技术领域,特别是涉及基于颜色表优化的二维标量场数据可视化方法及系统。
本部分的陈述仅仅是提到了与本公开相关的背景技术,并不必然构成现有技术。
在实现本公开的过程中,发明人发现现有技术中存在以下技术问题:
二维标量数据是科学模拟与应用中常见的数据表达方式,针对二维标量数据的可视化对于科学家发掘数据中的特征、分辨数值有着非常重要的意义。在众多的二维标量数据可视化方法中,颜色表映射是一种有效且常用的方法,它指的是将可辨别颜色赋予可量化数据值的映射过程。不同颜色表对于表达数据的特征存在较大的差异,好的颜色表能够让数据里蕴含的变化更为清晰,不好的颜色表可能隐藏数据中的变化。因此,选取一个好的颜色表对于二维标量场数据可视化而言是一个非常重要且极具挑战的问题。
在实践中,科学家经常使用一些可视化工具选择现有的颜色表,并通过颜色映射方案(如线性映射)将其应用于数据。这个过程中的一个问题是,数据往往分布不均匀,而颜色表往往是均匀分布的,两者之间存在不一致性,导致数据中用户感兴趣的细微变化可能被隐藏。为了揭示潜在的数据特征,科学家们通常通过不断试错过程选择和调整颜色表,该过程往往耗时较长且对领域经验要求较高。
目前,在可视化领域中有众多定量和定性的指导规则可用于颜色表的自动评估和设计。Bujack等人最近提出了一套对颜色表综合评价的框架,它对现有的设计规则(如顺序、均匀性等)进行了分类以及数学建模,可进一步应用于颜色表的量化和自动选取。然而,这一类工作主要关注于颜色表本身而没有考虑数据。此外,领域内有学者提出根据统计元数据或直方图均衡化来调节颜色表中的控制点,然而这类方法不能表达全局数据范围内的连续特征。
发明内容
为了解决现有技术的不足,本公开提供了基于颜色表优化的二维标量场数据可视化方法及系统;
第一方面,本公开提供了基于颜色表优化的二维标量场数据可视化方法;
基于颜色表优化的二维标量场图像数据可视化方法,包括:
接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;
利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;
对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;
对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
第二方面,本公开还提供了基于颜色表优化的二维标量场数据可视化系统;
基于颜色表优化的二维标量场图像数据可视化系统,包括:
输入模块,其被配置为接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;
映射模块,其被配置为利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;
能量优化方程建立模块,其被配置为对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;
可视化模块,其被配置为对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
第三方面,本公开还提供了一种电子设备,包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令,所述计算机指令被处理器运行时,完成第一方面所述方法的步骤。
第四方面,本公开还提供了一种计算机可读存储介质,用于存储计算机指令,所述计算机指令被处理器执行时,完成第一方面所述方法的步骤。
与现有技术相比,本公开的有益效果是:
本公开针对二维标量场数据提出了一种面向数据感知的颜色表优化方法。通过使用优化后的颜色表,能够凸显出细微的数据变化信息,在科学可视化领域具有广泛的应用前景。
本公开通过有机融合颜色表特性以及数据分布特性,针对二维标量数据场,提出一种能够凸显细微数据变化、保持原颜色表特征、最大化前背景对比度的 颜色表优化方法,并定义了多种交互式探索方式,能够充分满足科学数据分析与可视化过程中对于颜色表设计与选择、交互式探索等方面的迫切需求。
构成本申请的一部分的说明书附图用来提供对本申请的进一步理解,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。
图1为本实施例的流程图;
图2(a)为初始灰度颜色表和颜色表控制点;
图2(b)为颜色表对应的颜色映射数据;
图2(c)为修改后的灰度颜色表和对应颜色表控制点;
图2(d)为颜色表对应的颜色映射数据;
图3(a)为输入颜色表及对应颜色映射数据;
图3(b)为输入颜色映射数据的局部差异;
图3(c)为边界似然函数可视化结果;
图3(d)为优化后颜色表及对应颜色映射数据;
图3(e)为优化后颜色映射数据的局部差异;
图4(a)为输入颜色表及对应颜色映射数据;
图4(b)为η=100所对应的优化颜色表及其对应颜色映射数据;
图4(c)为η=1所对应的优化颜色表及其对应颜色映射数据;
图4(d)为η=0.01所对应的优化颜色表及其对应颜色映射数据;
图5(a)为输入颜色表及对应颜色映射数据;
图5(b)为α=1,β=0,γ=0所对应的优化颜色表及其对应颜色映射数据;
图5(c)为α=0,β=0,γ=1所对应的优化颜色表及其对应颜色映射数据;
图5(d)为α=1,β=0.5,γ=0.5所对应的优化颜色表及其对应颜色映射数据;
图6(a)为输入颜色表及对应颜色映射数据,放大部分为用户感兴趣区域可视化;
图6(b)为正常优化后的颜色表及其对应颜色映射数据;在此基础上,用户选择感兴趣的区域,如白色虚线所示。
图6(c)为用户选取约束感兴趣ROI区域的颜色表优化结果及其对应颜色映射数据;
图7为背景遮罩工具使用流程及每一步对应结果。
应该指出,以下详细说明都是示例性的,旨在对本申请提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本申请的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
实施例一,本实施例提供了基于颜色表优化的二维标量场图像数据可视化方法;
图1为本公开的流程框架图。基于颜色表优化的二维标量场图像数据可视化方法,包括:
S1:接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主 要颜色,将主要颜色设为控制点;
S2:利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;如图2(a)、图2(b)、图2(c)和图2(d)中所示的控制点;
S3:对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;
S4:对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
作为一个或多个实施例,所述初始颜色表,是指:用户指定的或者系统默认的颜色表,比如黑白颜色表、彩虹颜色表等。
作为一个或多个实施例,所述二维标量场数据,是指二维空间的标量数据,比如地球网格平面上的温度分布。
作为一个或多个实施例,计算初始颜色表中的主要颜色,将主要颜色设为控制点,具体步骤为:
设置聚类个数K,采用聚类算法计算初始颜色表中的主要颜色;并将主要颜色作为控制点,每个控制点包括控制点自身在初始颜色表上的坐标位置和控制点自身所对应的颜色。
应理解的,将主要颜色作为控制点具体表示为:
l={(p
1,c
1),(p
2,c
2),…(p
w,c
w)}; (1)
其中,p
w为控制点w在颜色表上的坐标位置(0≤p
w≤1);c
w为控制点w 所对应的颜色,l表示控制点集合。
需要注意的是,本实施例优化算法仅对控制点坐标位置进行调整,而不改变控制点颜色。
作为一个或多个实施例,利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据,具体步骤包括:
其中,p表示控制点的坐标位置;c表示控制点的颜色;
通过将分段线性函数应用于任意两相邻控制点,从而生成含有多个颜色的新的颜色表;新的颜色表在后续应用中利用颜色映射过程ξ建立颜色与数据之间的映射关系,从而生成颜色映射后的数据,颜色映射过程利用
表示,其中p表示控制点位置,x表示数据点的值。
对于任意二维标量场数据,利用最大最小值归一化法将其归一化至0到1参数空间,通过公式(2)建立颜色与控制点坐标位置之间的映射关系。
作为一个或多个实施例,颜色映射过程ξ:假设用C来表示一个含有n个颜色的颜色表,C={C
1,C
2,…,C
n},用T={T
1,T
2,…,T
n},0≤T≤1表示按升序排列的归一化到0-1之间的二维标量场数据的数值,通过将C分配给与T中具有相同下标的元素。
作为一个或多个实施例,对控制点的坐标位置建立能量优化方程;具体步骤包括:
所述能量优化方程,包括边界项、对比项以及保真项加权求和。
图5(a)、图5(b)、图5(c)和图5(d)示意了不同权值因子对于结果的影响。
作为一个或多个实施例,对能量优化方程进行求解,得到优化后的控制点坐标位置;具体步骤包括:
将控制点的坐标位置作为优化求解的变量,在序列二次规划优化算子中进行迭代优化;
计算边界项,其作用在于保证颜色映射数据的边界分布与二维标量场数据边界分布一致;二维标量场数据边界指的是不同数值之间的变化;
计算保真项,其作用在于保证优化后颜色表与初始颜色表之间的差异最小;
计算前景与背景对比项,其作用在于保证颜色映射后的数据的前景颜色与背景颜色对比度最大,以增强前景数据的可识别性;
得到优化后的控制点坐标位置。
作为一个或多个实施例,边界项定义为:
其中,
表示边界项,q(x
i)表示数据点x
i的边界似然函数;假设i为二维标量场数据中的任一数据点的标号,x
i表示该数据点的数值;Ω表示当前数据点i的邻居点,M表示二维标量场中的所有数据点;
表示所述的参数值到颜色值的映射,对于数据点x
i以及控制点位置集合
颜色映射方程可以得到数据点x
i相应的颜色值;‖.‖表示欧氏距离。
(4-1)对于二维数据场中的每一个数据点x
i,计算数据点x
i的边界似然函数:
如图3(b)所示;
(4-1-1)计算参数σ,σ为理想边界的模糊参数,定义为:
其中,f′(0)表示二维标量数据的一阶导数最大值,f″(-σ)表示二维标量数据的二阶导数最大值,二维标量数据的一阶导数通过梯度算子计算,二维标量数据的二阶导数通过拉普拉斯算子计算;
(4-1-2)计算参数h(x
i),h(x
i)表示数值等于x
i所对应数据点的二阶导数的平均值;
(4-1-3)计算参数g(x
i),g(x
i)表示数值等于x
i所对应数据点的一阶导数的平均值;
(4-1-4)参数η为边界权重,用于控制边界效果的强弱;图4(a)、图4(b)、图4(c)和图4(d)示意了不同η下的优化结果;如图3(a)、图3(b)、图3(c)、图3(d)和图3(e);
(4-2-1)对于任一颜色映射数据点x
i,设置领域大小为Ω;
(4-2-2)对于邻域内的任一数值点,求解该点颜色映射数值与x
i颜色映射数值之间的差;
(4-3)在(4-1)、(4-2)所涉及技术基础上提出一种用户感兴趣区域的交互控制方式。
(4-3-1)用户利用拉索工具选择数据中感兴趣的区域;
(4-3-2)利用以下公式增强用户选择的感兴趣区域内数值点所对应的边界似然函数:
其中ω为感兴趣区域权值。
图6(a)、图6(b)和图6(c)展示了该交互控制方式在医学图像可视化领域的应用场景,该场景二维标量场数据为脑肿瘤病人的头部放射剂量数据(以下简称“放射剂量数据”),默认颜色表为彩虹颜色表(rainbow colormap)。图6(a)将彩虹颜色表映射至放射剂量数据中,并将生成的可视化结果以75%不透明度与脑部CT图像叠加;图6(b)展示了利用(4-1)、(4-2)描述技术对彩虹颜色表进行优化后,并映射至放射剂量数据中的可视化结果,从该可视化结果中,医生观察到肿瘤病人脸部右侧高放射剂量区域有较大变化,便用拉索工具选中该区域以进一步探索其中所包含的数据信息;图6(c)展示了利用(4-3)中所描述的交互式方法对选中区域进行权值加强所得到的新的彩虹颜色表,以及将其映射至放射剂量数据后的可视化结果。
作为一个或多个实施例,保真项:
其中,ζ为初始颜色表所对应的弧长函数;
表示保真项;
表示 优化的控制点坐标位置在弧长函数上对应的数值,ζ(p
i)表示初始控制点坐标位置在弧长函数上对应的数值;ε表示弧长函数导数的最大值范围,默认设为弧长函数的最大值。
(5-1)颜色表弧长函数计算:
(5-1-1)将初始颜色表映射至三维色彩空间,形成一条三维连续颜色曲线;
(5-1-2)在颜色曲线上,对于每一个控制点,求解该点至初始控制点的弧长距离;初始控制点是从初始颜色表上选取的若干个主要颜色;
(5-1-3)根据每个控制点至初始控制点的的弧长距离,利用线性插值算法,生成弧长函数;
作为一个或多个实施例,前景与背景对比项:
所谓背景,指的是用户自定义的不想观察的数据;背景颜色用单一颜色C
b表示。
(6-2-1)计算前景数值点与背景数值点之间的欧式距离。
(6-2-2)对距离函数从大到小排序,找到各前景数值点所对应的最近的背景数值点。
(6-3)H(x
i)表示的是步骤S1输入数据中数值等于x
i的数据点数量。
(6-4)本公开对(6-1)、(6-2)、(6-3)等技术进行了拓展,使其能够支持前景区域用户交互操作。具体而言,用户可以删除不感兴趣的前景区域,该部分区域将有背景区域替换;本公开将根据用户交互结果,依据下述公式对颜色表进行优化:
图7展示了该交互控制方式在医学图像可视化领域的应用场景,该场景二维标量场数据为肺部肿瘤病人的放射断层造影数据(Positron Emission Tomograph,以下简称“PET数据”),图7中下标为(a)的图像的颜色映射至PET数据中,并示意将生成的可视化结果以80%不透明度与胸部CT图像叠加的过程;图7中下标为(b)的图像展示了图7中下标为(a)的图像叠加后的可视化结果,以及相应区域的放大结果;图7中下标为(c)的图像展示了利用(6-1)、(6-2)、(6-3)中描述技术对彩虹颜色表进行优化后,并映射至PET数据中的可视化结果;图7中下标为(d)的图像示意了用户删除部分不感兴趣前景的过程;图7中下标为(e)的图像示例了删除不感兴趣部分后的前景PET数据与背景CT图像叠加的可视化结果,相比图7中下标为(c)的图像,更多地胸部CT图像呈现出来;图7中下标为(f)的图像展示了针对更为丰富 的背景区域,利用(6-4)中所描述的方式进行优化所得到的新的颜色表,以及将其映射至PET数据后的可视化结果。
作为一个或多个实施例,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表;具体步骤包括:
将优化后的控制点坐标位置输入到分段线性函数中,就得到新的颜色表。
本公开可以应用的具体的技术领域有:科学数据可视分析、医学数据可视分析、物理模拟等。
本公开的输入数据具体包括:任意二维标量场数据,包括但不限于二维表面温度场、海水表面流速大小、海水盐度、肿瘤辐射剂量、无人机电磁模拟数据等
本公开提出了一种包含边界项、保真项以及对比项的颜色表数学方法,该方法创新性地将数据变化引入颜色表的评估与优化过程中,并加入对原始颜色表保真性以及前背景对比性的约束。该数学表达可拓展性强,容易拓展增加更多领域先验相关的约束。
本公开提出了一种感兴趣区域(ROI)交互探索与优化方法,基于用户指定的ROI区域,该优化算法将仅针对该区域进行优化。此外,还提出了一种基于背景替换的交互探索与优化方法,可帮助用户仅关注感兴趣区域的变化以及前背景差异。以上交互探索工作对于科学数据分析有着非常重要的意义。
本实施例提出了一种面向数据感知的优化算法来设计颜色表,用于凸显二维标量场可视化中与数据变化相关的连续特征。本公开核心思想在于将颜色表调整转化成一个非线性约束的优化问题,通过迭代调整颜色表中控制点的参数位置以获取最优颜色表。
实施例二,本实施例还提供了基于颜色表优化的二维标量场数据可视化系统;
基于颜色表优化的二维标量场图像数据可视化系统,包括:
输入模块,其被配置为接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;
映射模块,其被配置为利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;如图2(a)、图2(b)、图2(c)和图2(d)中所示的控制点;
能量优化方程建立模块,其被配置为对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;
可视化模块,其被配置为对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
实施例三,本实施例还提供了一种电子设备,包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令,所述计算机指令被处理器运行时,完成方法中的各个操作,为了简洁,在此不再赘述。
所述电子设备可以是移动终端以及非移动终端,非移动终端包括台式计算机,移动终端包括智能手机(Smart Phone,如Android手机、IOS手机等)、智能眼镜、智能手表、智能手环、平板电脑、笔记本电脑、个人数字助理等可以进行无线通信的移动互联网设备。
应理解,在本公开中,该处理器可以是中央处理单元CPU,该处理器还算 可以是其他通用处理器、数字信号处理器DSP、专用集成电路ASIC,现成可编程门阵列FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
该存储器可以包括只读存储器和随机存取存储器,并向处理器提供指令和数据、存储器的一部分还可以包括非易失性随机存储器。例如,存储器还可以存储设备类型的信息。
在实现过程中,上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本公开所公开的方法的步骤可以直接体现为硬件处理器执行完成,或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器、闪存、只读存储器、可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器,处理器读取存储器中的信息,结合其硬件完成上述方法的步骤。为避免重复,这里不再详细描述。本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元即算法步骤,能够以电子硬件或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,上述描述的系统、装置和单元的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。
在本申请所提供的几个实施例中,应该理解到,所揭露的系统、装置和方 法,可以通过其他的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能的划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另外一点,所显示或讨论的相互之间的耦合或者直接耦合或者通信连接可以是通过一些接口,装置或单元的间接耦合或通信连接,可以是电性、机械或其它的形式。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本申请的优选实施例而已,并不用于限制本申请,对于本领域的技术人员来说,本申请可以有各种更改和变化。凡在本申请的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本申请的保护范围之内。
Claims (10)
- 基于颜色表优化的二维标量场图像数据可视化方法,其特征是,包括:接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
- 如权利要求1所述的方法,其特征是,计算初始颜色表中的主要颜色,将主要颜色设为控制点,具体步骤为:设置聚类个数K,采用聚类算法计算初始颜色表中的主要颜色;并将主要颜色作为控制点,每个控制点包括控制点自身在初始颜色表上的坐标位置和控制点自身所对应的颜色。
- 如权利要求3所述的方法,其特征是,颜色映射过程ξ:假设用C来表示一个含有n个颜色的颜色表,C={C 1,C 2,…,C n},用T={T 1,T 2,…,T n},0≤T≤1表示按升序排列的归一化到0-1之间的二维标量场数据的数值,通过将C分配给与T中具有相同下标的元素。
- 如权利要求5所述的方法,其特征是,对能量优化方程进行求解,得到优化后的控制点坐标位置;具体步骤包括:将控制点的坐标位置作为优化求解的变量,在序列二次规划优化算子中进行迭代优化;计算边界项,其作用在于保证颜色映射数据的边界分布与二维标量场数据边界分布一致;二维标量场数据边界指的是不同数值之间的变化;计算保真项,其作用在于保证优化后颜色表与初始颜色表之间的差异最小;计算前景与背景对比项,其作用在于保证颜色映射后的数据的前景颜色与背景颜色对比度最大,以增强前景数据的可识别性;得到优化后的控制点坐标位置。
- 如权利要求5所述的方法,其特征是,边界项定义为:其中, 表示边界项,q(x i)表示数据点x i的边界似然函数;假设i为二维标量场数据中的任一数据点的标号,x i表示该数据点的数值;Ω表示当前数据点i的邻居点,M表示二维标量场中的所有数据点; 表示所述的参数值到颜色值的映射,对于数据点x i以及控制点位置集合 颜色映射方程可以得到数据点x i相应的颜色值;‖.‖表示欧氏距离;保真项:其中,ζ为初始颜色表所对应的弧长函数; 表示保真项; 表示优化的控制点坐标位置在弧长函数上对应的数值,ζ(p i)表示初始控制点坐标位置在弧长函数上对应的数值;ε表示弧长函数导数的最大值范围,默认设为弧长函数的最大值;前景与背景对比项:所谓背景,指的是用户自定义的不想观察的数据;背景颜色用单一颜色C b表示。
- 基于颜色表优化的二维标量场图像数据可视化系统,其特征是,包括:输入模块,其被配置为接收输入的初始颜色表以及二维标量场数据;计算初始颜色表中的主要颜色,将主要颜色设为控制点;映射模块,其被配置为利用分段线性函数,计算两两控制点之间的线性插值,生成颜色表,并将颜色表映射至二维标量场数据;能量优化方程建立模块,其被配置为对控制点的坐标位置和映射后的二维标量场数据建立能量优化方程;所述控制点的坐标位置是将控制点对应的颜色表中的索引值进行归一化,得到的0至1之间的数值;可视化模块,其被配置为对能量优化方程进行求解,得到优化后的控制点坐标位置,利用分段线性函数和优化后的控制点坐标位置,生成新的颜色表,并将新的颜色表映射至二维标量场数据,得到最终可视化结果。
- 一种电子设备,其特征是,包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令,所述计算机指令被处理器运行时,完成权利要求1-7任一项方法所述的步骤。
- 一种计算机可读存储介质,其特征是,用于存储计算机指令,所述计算机指令被处理器执行时,完成权利要求1-7任一项方法所述的步骤。
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