Abstract

Disclosed is method for dynamic cropping of full body pose (FBP) image, method comprising: receiving in computing device FBP image from image resource, wherein FBP image comprises face; detecting in received FBP image area of face by bounding box; detecting coordinates of corner points of bounding box; performing distance approximation of detected area of face in received FBP image; cropping distance approximated area of face of FBP image; performing coordinates mapping transformation of cropped FBP image by mapping coordinates of cropped FBP image to received FBP image; segmenting region of hair of cropped FBP image; masking region of hair of cropped FBP image for obtaining hair polymask; and applying masked FBP image to received FBP image according to performed coordinates mapping for obtaining the dynamically cropped FBP image.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• G06T 3/10 - Selection of transformation methods according to the characteristics of the input images
• G06T 7/11 - Region-based segmentation
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

A method for hair analysis of a user. The method segmenting a hair region in an image of the user to obtain a segmented hair region image; identifying one or more dominant hair colour types in; determining RGB values of pixels of the one or more dominant hair colour types; implementing a hair colour dictionary with multiple tints of plurality of hair colours classified as different hair colour names, with each of the classified different hair colour names having a relative RGB value; comparing the determined RGB values of the pixels of the one or more dominant hair colour types and the relative RGB values for the classified different hair colour types to identify the relative RGB value with a minimum distance; and determining the hair colour name corresponding to the identified relative RGB value with the minimum distance.

IPC Classes  ?

• G06T 7/90 - Determination of colour characteristics
• G06T 7/11 - Region-based segmentation
• G06V 10/56 - Extraction of image or video features relating to colour
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• A45D 44/00 - Other cosmetic or toiletry articles, e.g. for hairdressers' rooms
• G06T 11/00 - 2D [Two Dimensional] image generation

Abstract

A method and a system for measuring a feature of persons. The method includes defining the feature of persons being measured. The method further includes measuring values of data points from a sample of digital images comprising the persons, according to the defined feature by a first algorithm. The method further includes generating a distribution curve of the measured values. The method further includes standardizing the measured values by implementing a hyperbolic tangent function transformation.

IPC Classes  ?

• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions

Abstract

A method for augmenting point of interest in AR video. The method includes: extracting first frame of AR video depicting object being tracked including point of interest, by first pipeline; locating, in first frame set of feature points forming boundary of object; transferring frame with determined set of feature points to second pipeline; and by second pipeline determining coordinate system of set of feature points; calculating first location parameters of feature points; selecting first and second reference points from feature points, wherein first location parameters of point of interest are defined by first location parameters of first and second reference points; transmitting first location parameters of point of interest from second pipeline to first pipeline; and augmenting in first pipeline AR video with first location parameters of point of interest received from second pipeline.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06T 15/00 - 3D [Three Dimensional] image rendering
• G06T 7/20 - Analysis of motion
• G06T 19/00 - Manipulating 3D models or images for computer graphics
• G06V 20/40 - ScenesScene-specific elements in video content

Abstract

A system for determining a skin tone of a user. Herein, a server arrangement obtains an input image capturing at least a face of the user; analyze the input image to identify at least one first region of the input image that corresponds to a sclera of the user; determine a representative pixel value of the sclera; determine at least one second region of the input image that corresponds to a skin of the user; normalize pixel values of pixels in the at least one second region; determine a representative pixel value of the skin from the normalized pixel values of the pixels in the at least one second region; and select, from amongst a palette of skin tones, a skin tone whose pixel value matches with the representative pixel value of the skin. Herein, the selected skin tone is determined as the skin tone of the user.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]
• A45D 44/00 - Other cosmetic or toiletry articles, e.g. for hairdressers' rooms
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/19 - Sensors therefor

Abstract

An eye segmentation system for determining a center point of an eye, a radius of an iris of the eye, and an area of interest from a white area (sclera) of the eye. The system includes at least one input interface that receives a facial image of a user, wherein the facial image is captured using an image capturing device and a data processing arrangement that receives the facial image of the user as input data and processes the facial image to segment an eye region of the facial image. The system determines an eye that is suitable for segmentation from a left eye and a right eye using at least one quality metric, determines a center point of the eye using at least one directional filter, determines, using the at least one directional filter, a radius of an iris of the eye from the center point of the eye.

IPC Classes  ?

• A61B 3/11 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for measuring interpupillary distance or diameter of pupils
• G06V 10/56 - Extraction of image or video features relating to colour
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/18 - Eye characteristics, e.g. of the iris
• A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions

Goods & Services

Software.

Abstract

A method of determining facial areas from an image of a face of a user. The method includes obtaining, using a processing device, a set of image analysis modules. Further, the method includes receiving, using a communication device, the image of the face of the user. Yet further, the method includes receiving, using a communication device, an indication of a facial area of interest from the user. Moreover, the method includes selecting, using a processing device, from the set of image analysis modules an image analysis module associated with the indicated facial area of interest. Further, the method includes analyzing, using a processing device, the image of the face of the user to locate the indicated facial area of interest using the selected image analysis module. Yet further, the method includes rendering, using a processing device, in a user interface the image of the face of the user with an indication of the found facial area. Moreover, the method includes receiving, using a communication device, a feedback with the user interface if the found facial area is indicated properly. Further, the method includes updating, using a processing device, the image analysis module based on the received feedback.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06T 7/00 - Image analysis