Cradle for creating a color cloud

Abstract

The cradle can be used to capture video of an object, such as hair. The video can then be used to extract and visualize color information associated with the material using a color cloud. The color cloud includes all colors present in one or more frames of the video. The cradle includes a video recording device that allows the material to be viewed through a window on the cradle. A smartphone can be used with the cradle to act as the video recording device as well as to provide lighting.

Description

Technical Field

[0001] Cross - reference to related applications This application claims the benefit of U.S. Patent Application No. 17 / 107,073, filed on November 30, 2020, and French Patent Application No. 2101813, filed on February 25, 2021, and the entire contents of each of these applications are incorporated herein by reference.

Background Art

[0002] Spectrophotometers are used to read the color of hair samples, but they provide only a single digital representation of the material in the form of a single RGB, L*a*b*, LCh, or HSV triplet and typically read only an area of less than 1 cm × 1 cm. Further, when comparing two materials, the analysis is limited to the Euclidean distance in the color space between the three spaces (e.g., delta - E1976 =

Numbers

Summary of the Invention

[0003] The present disclosure is directed to an apparatus for creating a color cloud, the apparatus comprising a cradle having a first end, a second end having a window, and at least one wall connecting the first end to the second end, and a video recording device at the first end of the cradle configured to capture video of a material visible through the window. In another aspect, the present disclosure is also directed to a system for creating a color cloud of an object, the system comprising a cradle having a first end, a second end having a window, and at least one wall connecting the first end to the second end, an image sensor attached to the first end of the cradle configured to capture a plurality of digital images of an object visible through the window, and a processing circuit operably coupled to the image sensor, the processing circuit configured to generate a three-dimensional virtual representation of color information associated with the plurality of digital images of the object, identify one or more important characteristics of the object, and generate on a graphical user interface display one or more virtual instances indicative of the identity of the important characteristics of the object based on one or more inputs associated with the three-dimensional virtual representation of the color information of the object.

[0004] In one embodiment, the video recording device is a camera on a mobile device.

[0005] In one embodiment, the apparatus further comprises a light source attached to at least one of the first end, the second end, and the at least one wall.

[0006] In one embodiment, the light source is a flashlight on a mobile device.

[0007] In one embodiment, the apparatus further includes a processing circuit connected to the video recording device and configured to create a color cloud from the video of the material.

[0008] In one embodiment, the apparatus further comprises a holder attached to the first end of the cradle for holding a video recording device.

[0009] In one embodiment, the second end is colored with a plurality of colors adjacent to the window for color separation.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] The present disclosure describes a physical device and an original algorithm that can quantify the optical reflections of any material, such as a human head hair sample and real-life hair, in a highly reproducible, portable, and operable manner in any lighting environment. A device called a cradle can be used on a material (such as hair), and the material is analyzed with a color cloud algorithm to obtain an overall digital representation of the material, which is defined herein as a color cloud. This color cloud can be compared with other reflective materials at different times, or other color clouds of the same reflective material (e.g., colored hair before and after washing). The embodiments disclosed herein extract the existing colors from the capture of a video of the material (and do not manipulate them in any arithmetic way to produce a net result). This technique can use a set of robust colors as a basis for detecting subtle color changes.

[0012] Figure 1 shows a use case in one embodiment. User 101 picks up cradle 102 and drags it over hair 103. When cradle 102 is dragged over the user's hair 103, a video of hair 103 can be obtained. This video is analyzed using a color cloud algorithm, and a color cloud is generated.

[0013] Figure 2 is a flowchart passing through one embodiment. At S202, a cradle is used to obtain a video of a material, such as the user's hair. For example, to create a color cloud for the user's hair, the user can move the cradle along the entire length of the hair (i.e., from the root to the tip of the hair). At S204, a color cloud is created from one or more frames of the video. Basically, all the colors present in one or more frames are extracted and displayed in the color cloud. At S206, the color cloud is compared with other color clouds. This comparison can be used for color matching and / or color differentiation.

[0014] The cradle is a device that enables the capture of a video of a material so as to create a color cloud of the material. An example of the cradle is shown in FIG. 3 (the solid line represents the exterior of the cradle and the dashed line represents the interior). The cradle has a first end 301, a second end 302, a window 303 on the second end 302, and a wall 304 connecting the first end 301 to the second end 302. In one embodiment, the first end 301 and the second end 302 are at opposite ends of the cradle. An image sensor, such as a video recording device 305, is attached to the first end 301 inside the cradle and can capture a video of any material 306 (e.g., hair) visible through the window 303. Further, the cradle has a processing circuit 308 connected to the video recording device 305 and can be configured to create a color cloud from the video of the material 306 and compare the created color cloud with a previous color cloud. The second end 302 of the cradle can be colored with a set 309 of predetermined colors adjacent to the window 303 for color separation when captured by the video recording device 305. The video can capture both the material 306 through the window 303 and the predetermined color 309 adjacent to the window 303, and the predetermined (i.e., known) color can then be used as a reference when determining the color of the material 306. Light 307 can also be attached to the cradle to provide a sufficiently bright field of view from the video recording device 305 to the window 303. The cradle may be used to maintain a consistent distance between the video recording device 305 and the material 306 when taking a video. The cradle can also have a hand grip 310.

[0015] The processing circuit 308 can include a color cloud unit and a key characteristic unit. The color cloud unit can include a circuit coupled to an image sensor and is configured to generate a three-dimensional virtual representation of color information associated with a plurality of digital images of an object, such as a region indicating the color appearance frequency, color variation, or color intensity distribution associated with the object. The key characteristic unit can include a circuit for identifying one or more key characteristics of the object and generating one or more virtual instances on a graphical user interface display indicating the identity of the key characteristics of the object based on one or more inputs associated with the three-dimensional virtual representation of the color information of the object. In one embodiment, the object can be hair, and the three-dimensional virtual representation can be hair color information and / or hair characteristic information associated with a plurality of digital images of the hair. Further, the three-dimensional virtual representation can include voxels of various intensities to indicate the color appearance frequency of specific characteristics associated with the imaged object. Examples of characteristic information include object identification data, object characteristic data, color appearance frequency data, color variation data, color intensity data, presence or absence of color data, color analysis information for each frame or each pixel, etc. Non-limiting examples of color information can include hue, saturation, tone, shade, etc. In one embodiment, the saturation of a basic color is a lighter version of that color, and the shade is a darker version. In one embodiment, the tone refers to the lightness (saturation) or darkness (shade) of a basic color. When the image object is hair, the three-dimensional virtual representation can include hair characteristic information including voxels indicating the range of gray hair, gloss, shine, and uniformity associated with a plurality of digital images of the hair. Examples of hair characteristics include the damaged state of the hair, the concentration of artificial coloring agents, color distribution, the range of gray hair, texture, gloss, distribution density, scalp characteristics, etc.

[0016] The body of the cradle of FIG. 3 was essentially a hollow rectangular prism with a window 303 at one end. In one embodiment, when the window 303 is covered, the only light within the body of the cradle can be from the light 307. This allows the lighting conditions within the cradle to be made consistent. Further, in other embodiments, the cradle can have different shapes such as cylindrical, cubic, trapezoidal prism, etc. The cradle can also have a screen that can be used to visualize the color cloud.

[0017] In one embodiment, the first end of the cradle can be configured to use the camera of a mobile device (e.g., smartphone) as a video recording device. As shown in FIG. 4 by way of example, the first end 401 of the cradle can be an attached smartphone 402. The camera 403 on the smartphone 402 can capture video of the material 404 through the window 405 on the second end 406. Further, using the processing circuitry already present in the smartphone 402, a color cloud can be created. The color cloud and other related information can be visualized directly on the screen of the smartphone. Also, the flashlight 407 of the smartphone can be used to provide a sufficiently bright field of view from the camera 403 of the smartphone to the window 405. The cradle can also have a holder 408 that allows the smartphone 402 to be properly attached to the cradle during use and removed from the cradle when not in use.

[0018] The captured video can be of the material for which it is desired to generate a color cloud. The cradle should be held such that the video recording device can capture video of the material through the opening of the cradle provided by the window. The window can be held directly against the material so as to block external light from entering the interior of the cradle. An example of the material can be the hair on the head. In that case, the cradle can start by capturing video of the roots and then can be dragged along the hair up to the tips.

[0019] In one embodiment, a video comprising a plurality of frames may be at least 6 seconds long and at least 60 frames per second. When a video of human hair or a hair sample is captured, the hair may be straight or curly.

[0020] Obtaining a video of the material includes capturing a video of someone's hair. Examples are shown in FIGS. 5A and 5B. First, referring to FIG. 5A, a video of the entire length of the model's hair is captured, starting from the root 51a of the hair and proceeding downward toward the tip 52a of the hair. FIG. 5B shows each frame of the captured video (from FIG. 5A) laid out in chronological order, where the upper left frame 51b is the root 51a of the hair and the lower right frame 52b is the tip 52a of the hair. In another embodiment, obtaining a video of the material may include capturing a video of less than the entire length of someone's hair (e.g., capturing only half of someone's hair). Note that the video can be captured in a direction other than downward, such as upward or at an angle.

[0021] The color cloud algorithm can be used to create a color cloud from one or more frames of the video. The color cloud algorithm can extract color information from one or more frames. The color cloud includes all the colors present in one or more of the plurality of frames. In another embodiment, the color cloud includes all the colors present in one or more of the plurality of frames and the frequencies of all the colors present in one or more of the plurality of frames. In another embodiment, the color cloud includes all the colors present in one or more of the plurality of frames and the frequencies of all the colors present in one or more of the plurality of frames, and the frequencies exceed a minimum predetermined threshold.

[0022] In one embodiment, the color cloud algorithm can generate a color cloud by extracting all colors from a single frame of a video. For example, in FIG. 5B, an individual color cloud can be created for each frame. The color cloud created for each frame can include all the colors present in that single frame, and in another embodiment, the frequency of each color present in that single frame.

[0023] In another embodiment, the color cloud algorithm can generate a color cloud by extracting cumulative colors from a group of frames within a video (rather than from a single frame). For example, referring to FIG. 5B, a color cloud can be generated for each group of 10 frames, and the color cloud created for each group of 10 frames can include all the colors present within that specific group of 10 frames, and in another embodiment, the frequency of each color present in that group of 10 frames.

[0024] FIG. 6 shows an example of a color cloud, and the color cloud displays all the colors across all the frames of a video using a color space. Since the video is time-based, when a specific frame is selected, the color cloud can be created at a specific point in time and / or time range.

[0025] When the color cloud algorithm extracts all the colors from one or more frames of a video, the color cloud can be displayed in numerous forms. For example, the colors can be displayed in a color space, and the color space can convey color information using coordinates such as L*a*b*, RGB, LCh, HSV, etc.

[0026] In another embodiment, the color cloud can include the frequency (i.e., the number of times) that each color exists in one or more frames of the video. An example is shown in FIG. 7, where the brighter the pixel, the higher the frequency of the color represented by that pixel, and vice versa. The outer color cloud 71 (i.e., the full-color cloud) captures all the colors that were present in one or more frames, and the inner color cloud 72 (i.e., the optimized color cloud) captures the colors that were present with a higher frequency. The threshold for determining whether a color exists with a higher frequency can be adjusted according to the application. In one example, only the inner color cloud 72 is used, and low-frequency (i.e., low-information) colors are removed as noise.

[0027] In another embodiment, the color cloud can be used to identify characteristics of hair, such as the extent of gray hair, gloss, shine, uniformity, luster, etc. For example, the color and frequency information from the color cloud can be used to filter out "low-information" colors, enhance color detection, and extract the amount of gray hair that a person has. As another example, the gloss level of the hair can be calculated by looking at the mean, variance, and spatially connected clusters of L* values within the color cloud of the hair. High variability suggests a wide range of L* values from dark to light, and spatially connected high L* values suggest a band of gloss. When both pieces of information are combined, they are related to human perception of gloss.

[0028] The color cloud can be compared with other color clouds to identify similarities and differences. This enables the detection of color changes and color matching. An example of comparing with other color clouds is shown in FIG. 8. The color cloud is generated for two unwashed samples and the same two samples after washing. The overlapping parts of the color clouds indicate color similarity (i.e., the colors present in both samples), and the non-overlapping parts indicate color differences (i.e., the colors not shared between the samples). This technique quantifies the overlap and non-overlap of color groups in color space (e.g., the number of groups, group similarity, group average color).

[0029] Furthermore, the similarities and differences in hair color can be visualized by returning to the video. An example of generating color clouds for samples before and after washing is shown in FIG. 9. Dark portions of the hair show common colors, and light portions show unique colors. The similarities and differences between these color clouds can be directly identified and visualized on the sample. In other words, the user can play one of the videos used to capture the hair, and the similarities / differences can be shown directly on the hair in the video. This is done by simply matching the color of the video pixels to the color contained in either two different color clouds or the overlap of two different color clouds. For example, the color of video pixels that match any color within the overlap or intersection of the color clouds is colored dark in the visualization video.

[0030] In another embodiment, the atlas of hair samples can have a directory of color clouds created for each hair sample. Next, upon any subsequent capture of hair, a color cloud is generated and compared to the samples in the atlas to determine the closest match (i.e., the most similar color). For excellent differentiation, the sample needs to be more similar to itself than to other samples.

[0031] The technologies referred to in this specification can capture a wide range of reflections (e.g., from a sample of hair and from the head of hair), accurately and robustly measure inhomogeneous materials such as color, and accurately and robustly measure changes in hair color. The application of these technologies includes measuring color changes more accurately and reproducibly than state-of-the-art instruments (e.g., spectrophotometers), measuring the color of human hair (as done by a hair colorist), measuring the extent of gray hair (e.g., measuring the amount of gray hair at the roots of someone to indicate how much hair color product to use), measuring the health of hair (e.g., based on the level of shine, where too little shine indicates dullness and suggests damaged hair), and dynamic visualization of hair before and after coloring and / or washing, etc. The technologies referred to in this specification can also enable color evaluation by home consumers, in-store displays and visualizations, and recommendations for home consumer products. For example, a consumer can download an app onto a mobile phone, capture a video, and create a color cloud. The app can further recommend hair products based on specific characteristics of the hair (e.g., recommend a specific hair color product based on the user's hair color, gray hair level, and the survey performed by the app). Further, the cradle can be made portable (e.g., a foldable cardboard cradle, a 3D printed cradle, etc.).

Claims

**Claim 1** A system for creating a color cloud of a user's hair, comprising: A cradle having a first end, a second end having a window, and at least one wall connecting the first end to the second end; An image sensor attached to the first end of the cradle and configured to capture a video of the user's hair visible through the window, the video including a plurality of frames captured by the image sensor as the cradle moves across the user's hair; an image sensor; A processing circuit operably coupled to the image sensor, the processing circuit being configured to: Generate a three-dimensional virtual representation of the color information associated with the plurality of frames of the video, the three-dimensional virtual representation including all colors present in the plurality of frames of the video and the frequency at which each color is present in the plurality of frames; and Identify one or more important characteristics of the hair based on one or more inputs associated with the three-dimensional virtual representation of the hair color information and generate one or more virtual instances indicative of the identity of the important characteristics of the hair on a graphical user interface display; A system in which colors having a frequency of occurrence in the plurality of frames below a predetermined threshold are removed as noise from the three-dimensional virtual representation. **Claim 2** The system of claim 1, wherein the image sensor is a camera on a mobile device attached to the first end of the cradle. **Claim 3** The system of claim 1, further comprising a light source attached to at least one of the first end, the second end, and the at least one wall. **Claim 4** The system of claim 3, wherein the light source is a flashlight on a mobile device attached to the first end of the cradle. **Claim 5** The system of claim 1, further comprising a holder attached to the first end of the cradle for holding the image sensor. **Claim 6** The system of claim 1, wherein the second end is colored with a plurality of colors adjacent to the window for color separation.

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