Abstract: An image obtaining method comprises: by a projecting device, separately projecting an image acquisition light and a reference light onto a target object, wherein the light intensity of the image acquisition light is higher than the light intensity of the reference light; by an image obtaining device, obtaining a first image and a second image, both the first image and the second image comprising the image of the target object, with the target object of the first image being illuminated by the image acquisition light, and the target object of the second image being illuminated by the reference light, wherein the first image has a first area including a part of the target object, and the second image has a second area including the part of the target object; and by a computing device, performing a difference evaluation procedure to obtain a required light intensity based on a required amount.

Abstract: A multi-modal image alignment method includes obtaining first points corresponding to a center vertex of a calibration object and second point groups corresponding to side vertices of the calibration object from two-dimensional images, obtaining third points corresponding to the center vertex from three-dimensional images, performing first optimizing computation using a first coordinate system associated with the two-dimensional images, the first points and the third points to obtain a first transformation matrix, processing on the three-dimensional images using the first transformation matrix to generate firstly-transformed images respectively, performing second optimizing computation using the firstly-transformed images, the first points and the second point groups to obtain a second transformation matrix, and transforming an image to be processed from a second coordinate system associated with the three-dimensional images to the first coordinate system using the first transformation matrix and the second t

Abstract: A calibration method for a robotic arm system is provided. The method includes: capturing an image of a calibration object fixed to a front end of the robotic arm by a visual device, wherein a pedestal of the robotic arm has a pedestal coordinate system, and the front end of the robotic arm has a first relative relationship with the pedestal, the front end of the robotic arm has a second relative relationship with the calibration object; receiving the image and obtaining three-dimensional feature data of the calibration object according to the image by a computing device; and computing a third relative relationship between the visual device and the pedestal according to the three-dimensional feature data, the first relative relationship, and the second relative relationship to calibrate a position error between a physical location of the calibration object and a predictive positioning-location generated by the visual device.

Abstract: An image obtaining method comprises: by a projecting device, separately projecting an image acquisition light and a reference light onto a target object, wherein the light intensity of the image acquisition light is higher than the light intensity of the reference light; by an image obtaining device, obtaining a first image and a second image, both the first image and the second image comprising the image of the target object, with the target object of the first image being illuminated by the image acquisition light, and the target object of the second image being illuminated by the reference light, wherein the first image has a first area including a part of the target object, and the second image has a second area including the part of the target object; and by a computing device, performing a difference evaluation procedure to obtain a required light intensity based on a required amount.

Abstract: An image monitoring apparatus including an image sensing module and a processor is provided. The image sensing module is configured to obtain an invisible light dynamic image of an objective scene. The invisible light dynamic image includes a plurality of frames. The processor is configured to perform operations according to at least one frame of the invisible light dynamic image to determine a status of at least one live body corresponding to the objective scene to be one of a plurality of status types and determine at least one status valid region of the invisible light dynamic image, and set scene information of each pixel of the at least one status valid region to be one of a plurality of scene types according to the status type of the at least one live body. An image monitoring method is also provided.

Abstract: A method for measuring a hole provided in a workpiece is provided and the method comprises: obtaining a three-dimensional point cloud model of the workpiece and a two-dimensional image of the workpiece, defining a first contour in the three-dimensional point cloud model based on an intensity difference of the two-dimensional image, defining a second contour and a third contour respectively based in the first contour, bounding a data point testing region between the second contour and the third contour, respectively defining data point sampling regions along a plurality of cross-section directions of the data point testing region, respectively sampling data points in the data point sampling regions to obtain a turning point set comprising turning points, wherein each of the turning points has the largest turning margin, connecting the turning points which are distributed in the turning point set along a ring direction to obtain an edge of the hole.

Abstract: A method for measuring a hole provided in a workpiece is provided and the method comprises: obtaining a three-dimensional point cloud model of the workpiece and a two-dimensional image of the workpiece, defining a first contour in the three-dimensional point cloud model based on an intensity difference of the two-dimensional image, defining a second contour and a third contour respectively based in the first contour, bounding a data point testing region between the second contour and the third contour, respectively defining data point sampling regions along a plurality of cross-section directions of the data point testing region, respectively sampling data points in the data point sampling regions to obtain a turning point set comprising turning points, wherein each of the turning points has the largest turning margin, connecting the turning points which are distributed in the turning point set along a ring direction to obtain an edge of the hole.

Abstract: A controlling system and a controlling method for virtual display are provided. The controlling system for virtual display includes a visual line tracking unit, a space forming unit, a hand information capturing unit, a transforming unit and a controlling unit. The visual line tracking unit is used for tracking a visual line of a user. The space forming unit is used for forming a virtual display space according to the visual line. The hand information capturing unit is used for obtaining a hand location of the user's one hand in a real operation space. The transforming unit is used for transforming the hand location to be a cursor location in the virtual display space. The controlling unit is used for controlling the virtual display according to the cursor location.

Abstract: A calibration method for a robotic arm system is provided. The method includes: capturing an image of a calibration object fixed to a front end of the robotic arm by a visual device, wherein a pedestal of the robotic arm has a pedestal coordinate system, and the front end of the robotic arm has a first relative relationship with the pedestal, the front end of the robotic arm has a second relative relationship with the calibration object; receiving the image and obtaining three-dimensional feature data of the calibration object according to the image by a computing device; and computing a third relative relationship between the visual device and the pedestal according to the three-dimensional feature data, the first relative relationship, and the second relative relationship to calibrate a position error between a physical location of the calibration object and a predictive positioning-location generated by the visual device.

Abstract: An electronic device, an iris recognition method and a non-volatile computer-readable medium are provided. A processor in the electronic device obtains a first and second iris images, and calculates a plurality of first and second feature mark boxes that are non-uniformly arranged according to the first and second iris images. The processor uses the first feature mark boxes to obtain a first and second image features from the first and second iris images respectively, and compares the first and second image features to obtain a first recognition result. The processor uses the second feature mark boxes to obtain a third and fourth image features from the second and first iris images respectively, and compares the third and fourth image features to obtain a second recognition result. The processor determines a similarity degree of the first and second iris images according to the first and second recognition results.

Abstract: A controlling system and a controlling method for virtual display are provided. The controlling system for virtual display includes a visual line tracking unit, a space forming unit, a hand information capturing unit, a transforming unit and a controlling unit. The visual line tracking unit is used for tracking a visual line of a user. The space forming unit is used for forming a virtual display space according to the visual line. The hand information capturing unit is used for obtaining a hand location of the user's one hand in a real operation space. The transforming unit is used for transforming the hand location to be a cursor location in the virtual display space. The controlling unit is used for controlling the virtual display according to the cursor location.

Abstract: An electronic device, an iris recognition method and a non-volatile computer-readable medium are provided. A processor in the electronic device obtains a first and second iris images, and calculates a plurality of first and second feature mark boxes that are non-uniformly arranged according to the first and second iris images. The processor uses the first feature mark boxes to obtain a first and second image features from the first and second iris images respectively, and compares the first and second image features to obtain a first recognition result. The processor uses the second feature mark boxes to obtain a third and fourth image features from the second and first iris images respectively, and compares the third and fourth image features to obtain a second recognition result. The processor determines a similarity degree of the first and second iris images according to the first and second recognition results.

Abstract: A method and an apparatus for reconstructing a three dimensional model of an object are provided. The method includes the following steps. A plurality of first depth images of an object are obtained. According to a linking information of the object, the first depth images are divided into a plurality of depth image groups. The linking information records location information corresponding to a plurality of substructures of the object. Each depth image group includes a plurality of second depth images, and the substructures correspond to the second depth images. According to the second depth image and the location information corresponding to each substructure, a local module of each substructure is built. According to the linking information, the local models corresponding to the substructures are merged, and the three-dimensional model of the object is built.

Abstract: A biomechanics analyzing system and a biomechanics computerized analyzing method for analyzing an organism when the organism performs an act by himself are provided. The biomechanics analyzing system includes an accelerometer, a low-pass filter, and a processing unit. The accelerometer is configured to be disposed on a surface of a muscle of the organism and is further configured to detect an acceleration signal. The low-pass filter is connected to the accelerometer and is configured for receiving the acceleration signal from the accelerometer and filtering the acceleration signal to produce a low-frequency signal. The processing unit is connected to the low-pass filter, and is configured for receiving the low-frequency signal from the low-pass filter and analyzing a frequency of a motion state of the organism according to the low-frequency signal.

Abstract: A system and method for determining individualized depth information in an augmented reality scene are described. The method includes receiving a plurality of images of a physical area from a plurality of cameras, extracting a plurality of depth maps from the plurality of images, generating an integrated depth map from the plurality of depth maps, and determining individualized depth information corresponding to a point of view of the user based on the integrated depth map and a plurality of position parameters.

Abstract: A method and an apparatus for reconstructing a three dimensional model of an object are provided. The method includes the following steps. A plurality of first depth images of an object are obtained. According to a linking information of the object, the first depth images are divided into a plurality of depth image groups. The linking information records location information corresponding to a plurality of substructures of the object. Each depth image group includes a plurality of second depth images, and the substructures correspond to the second depth images. According to the second depth image and the location information corresponding to each substructure, a local module of each substructure is built. According to the linking information, the local models corresponding to the substructures are merged, and the three-dimensional model of the object is built.

Abstract: A method for establishing a 3D object includes the following steps. Multiple featured patches, with different textured features, on the surface of an object are captured and stored. An image capture unit is utilized to detect the featured patches on the surface of the object. A processing unit is utilized to build a spatial relationship matrix corresponding to the featured patches according to detected space information of the featured patches. The processing unit is utilized to trace and describe the object according to the spatial relationship matrix.

Abstract: A mechanomyography (MMG) signal input device, a human-machine operating system and an identification method thereof are provided. The system includes a mechanomyography (MMG) signal input device, a signal processing unit, a motion database and a calculating unit. The MMG signal input device is mounted on a measuring portion of a testing body, wherein the measuring portion has a plurality of muscle groups. The signal processing unit is used for receiving the MMG signals of the muscle groups, and performing integration and pre-processing on the MMG signals to obtain a processed signal. The motion database is used for storing a motion mode. The calculating unit is used for performing signal intensity computation, data segmentation, feature vector calculation, and testing body's motion recognition, and outputting a corresponding control signal according to the result of recognition.

Abstract: A system and a method for analyzing biomechanics. The biomechanics analyzing system for analyzing a motion state of an organism includes a detecting unit, a low-pass filter, a high-pass filter and a processing unit. The detecting unit disposed on a surface of a muscle of the organism detects an acceleration signal. The low-pass filter filters the acceleration signal to produce a low-frequency signal. The high-pass filter filters the acceleration signal to produce a high-frequency signal. The processing unit analyzes a motion posture or a motion frequency of the motion state of the organism according to the low-frequency signal, and analyzes a motion strength of the motion state of the organism according to the high-frequency signal.