Abstract: A body information analysis apparatus (1) and method of analyzing hand skin by using same are provided. The method includes activating an image fetching module (12) of the body information analysis apparatus (1) to record an external image (61); activating a processing unit (10) of the body information analysis apparatus (1) to recognize one of a plurality of hand images (81, 82, 83, 84, 85, 86, 87, 88) in the external image (61); recognizing an image in one of the hand images (81, 82, 83, 84, 85, 86, 87, 88) corresponding to a defect; marking one of the hand images (81, 82, 83, 84, 85, 86, 87, 88) based on a location of the image having a defect; and activating a display module (111) of the body information analysis apparatus (1) to show the marked one of the hand images (81, 82, 83, 84, 85, 86, 87, 88).

Abstract: A body information analysis apparatus (1) and a method of simulating face shape by using same are provided. The method includes reading a predetermined value of a face shape variable from a memory (17) wherein the face shape variable corresponds to one of a plurality of target face shapes; causing an image fetching module (12) to take a picture and converting same into a digital image; causing a processing unit (10) to identify a face (6) in the digital image and position a plurality of parts of the face (6); determining an original face shape of the face (6) based on the plurality of parts thereof; performing deformation on the face (6) based on the original face shape thereof to generate a simulated face image (80) wherein a face shape variable of the simulate face image (80) complies with the predetermined value; and outputting the simulated face image (80).

Abstract: A body information analysis apparatus (1) and a method of detecting face shape by using same are provided. The apparatus (1) includes an image fetching module (12); a display module (111); a processing unit (10) including a face analysis module (500) for positioning two eyebrows, a mouth and first features (711, 712) of a face contour in response to an identified face wherein the first features (711, 712) are at two sides of the face respectively; a first calculation module (501) for calculating a vertical distance between the mouth and either eyebrow and taking same as a first distance (D1), calculating a horizontal distance between the first features (711, 712) and taking same as a second distance (D2), and calculating a first ratio of the first distance (D1) to the second distance (D2); a face detection module (511) for determining the face shape; and an information creation module (504).

Abstract: A body information analysis apparatus (1) and method of analyzing hand skin by using same are provided. The method includes activating an image fetching module (12) of the body information analysis apparatus (1) to record an external image (61); activating a processing unit (10) of the body information analysis apparatus (1) to recognize one of a plurality of hand images (81, 82, 83, 84, 85, 86, 87, 88) in the external image (61); recognizing an image in one of the hand images (81, 82, 83, 84, 85, 86, 87, 88) corresponding to a defect; marking one of the hand images (81, 82, 83, 84, 85, 86, 87, 88) based on a location of the image having a defect; and activating a display module (111) of the body information analysis apparatus (1) to show the marked one of the hand images (81, 82, 83, 84, 85, 86, 87, 88).

Abstract: The invention provides a skin aging state assessment method and an electronic device. The method includes: acquiring a first image and a second image; acquiring a characteristic parameter of the first image and a characteristic parameter of the second image; acquiring an aging parameter according to the characteristic parameter of the first image and the characteristic parameter of the second image; and deciding an aging assessment result corresponding to the first image according to the aging parameter. The skin state detection method of the invention makes it possible to use two face images captured at different times to acquire the skin aging condition of the face images.

Abstract: The invention provides a method for makeup trial information adapted for an electronic apparatus having a display. The display displays a facial image of a person trying makeup before or after editing. The method includes steps as follows: displaying a makeup trial part selecting frame through the display; displaying a makeup trial type selecting frame through the display in response to receiving a first operation corresponding to the makeup trial part selecting frame; editing the facial image in response to receiving a second operation corresponding to the makeup trial type selecting frame; and displaying a makeup trial result frame through the display, and recording an editing sequence and an edited parameter of the facial image, wherein the makeup trial result frame includes the facial image after editing. In addition, an electronic apparatus is also provided. The invention provides quick, accurate, and convenient makeup trial information and functions.

Abstract: A face similarity evaluation method and an electronic device are provided. The method includes: obtaining a first image; obtaining a plurality of feature factors respectively corresponding to the first image and at least one second image; obtaining an overall similarity score corresponding to the at least one second image based on the feature factors respectively corresponding to the first image and at least one second image, and generating an evaluation result based on the overall similarity score corresponding to the at least one second image; and outputting an inform message based on the evaluation result.

Abstract: A skin condition detection method and an electronic device are provided. The method includes: acquiring a first image; performing at least one skin condition detection on the first image to acquire at least one first characteristic value; acquiring at least one second characteristic value acquired through the skin condition detection in the second image; determining whether a skin condition in the first image has changed with respect to a skin condition in the second image according to the first characteristic value and the second characteristic value; and if the skin condition in the first image has changed with respect to the skin condition in the second image, outputting notification information to indicate that the skin condition in the first image has changed with respect to the skin condition in the second image. The invention makes it possible to effectively and clearly determine change of the skin condition.

Abstract: A skin care evaluation method, which is adapted for an electronic device to evaluate a skin care procedure including a plurality of skin care behaviours. The skin care evaluation method includes following steps. A plurality of image information is sequentially captured at a plurality of image capturing time. A plurality of user actions corresponding to the image information is analyzed, where a plurality of consecutive image information of the image information corresponds to one of the user actions. The image information is classified to the skin care behaviours according to the user action corresponding to each of the image information. Each of the skin care behaviours corresponds to a plurality of the user actions. A duration of each of the skin care behaviours is calculated according to the image capturing time of the image information classified to each of the skin care behaviours. An electronic device using the skin care evaluation method is also provided.

Abstract: An image registration method for registering images into a coordinate system includes the following steps: (a) performing image normalization on a source image and generating a normalized image; (b) retrieving at least one color-deconvoluted image from color-deconvoluting the source image; (c) determining at least one image feature from the at least one color-deconvoluted image; (d) comparing the at least one image feature of the at least one color-deconvoluted image with a target image, and generating a relative matching structural feature result; and (e) transforming the normalized image into a registered image according to the matching structural feature result.

Abstract: An image registration method for registering images into a coordinate system includes the following steps: (a) performing image normalization on a source image and generating a normalized image; (b) retrieving at least one color-deconvoluted image from color-deconvoluting the source image; (c) determining at least one image feature from the at least one color-deconvoluted image; (d) comparing the at least one image feature of the at least one color-deconvoluted image with a target image, and generating a relative matching structural feature result; and (e) transforming the normalized image into a registered image according to the matching structural feature result.

Abstract: A real-time cloud imaging system includes at least a frontend device and a backend system. The frontend device generates an instruction message and a ROI (Region of Interest) message, and the backend system is coupled to the frontend device. The backend system has at least a raw image, wherein the raw image has a plurality of images of different resolutions. Each of the images and the raw image are composed of a plurality of tiles. The ROI message corresponds to a region of interest respectively within each of the images and the raw image. The backend system, according to the instruction message and the ROI message, selectively provides a grouping of the tiles within the region of interest of the raw image or one of the images to the frontend device.

Abstract: An image recognition method and an image recognition system can be applied to fetal ultrasound images. The image recognition method includes: (a) adjusting the image with a filter operator to decrease noise and to homogenize an image expression level of the pixel units within an individual object structure; (b) analyzing the image by a statistic information function, determining a foreground object pixel unit and a background pixel unit according to a max information entropy state of the statistic information function; and (c) searching by a profile setting value and recognizing a target object image among the foreground object pixel unit. The image recognition method can not only increase the efficiency of identifying the object of interests within the image and measuring the object of interests, but also improve the precision of measurements of the object of interests.

Abstract: An image segmentation system for performing image segmentation on an image data includes an image preprocessing module, a motion analyzing module, a detection module, a classification module, and a multi-dimensional detection module. The image data has a plurality of image stacks ordered chronologically that respectively have a plurality of images sequentially ordered according to spatial levels, wherein one spatial level is designated as a first stack. The image preprocessing module transforms the images into binary images while the motion analyzing module finds a repeating pattern in the binary images in the first stack and accordingly generates a repeating motion result. The classification module generates a classification result based on a spatial and an anatomical assumption to classify objects. The multi-dimensional detection module generates segmentation results for stacks above and below the first stack using spatial and temporal consistency of geometric layouts of object structures.

Abstract: A real-time cloud imaging system includes at least a frontend device and a backend system. The frontend device generates an instruction message and a ROI (Region of Interest) message, and the backend system is coupled to the frontend device. The backend system has at least a raw image, wherein the raw image has a plurality of images of different resolutions. Each of the images and the raw image are composed of a plurality of tiles. The ROI message corresponds to a region of interest respectively within each of the images and the raw image. The backend system, according to the instruction message and the ROI message, selectively provides a grouping of the tiles within the region of interest of the raw image or one of the images to the frontend device.

Abstract: An artificial intelligence analysis, pattern recognition and prediction method is implemented with software installed in computer hardware to create a system. The method has a classified data inputting act, a first learning act, a building act, an unclassified data inputting act, an analyzing act, a comparing act, an ending act, a transferring act and a second learning act. The comparing act is the comparing of an actual classifier of a testee with a predicted classifier by the system, and results in conformity or nonconformity between the actual class label and the predicted class label. The second learning act is the learning of the new data by the machine learning algorithm when nonconformity is the result of the comparing act. The refining act is the refining of the rules and patterns. The method concludes a predicted result and refines itself when the predicted result is different from an actual result.