Abstract: A method for programming a robot in a vision base coordinate is provided. The method includes the following steps. A robot is drawn to an operation point. The coordinates of the operation point in a photo operation are set as a new point. A teaching image is captured and a vision base coordinate system is established. A new point is added according to the newly established vision base coordinate system. When the robot is operating, the robot is controlled to capture an image from a photo operation point. A comparison between the captured image and a teaching image is made. The image being the same as the teaching image is searched according to the comparison result. Whether the vision base coordinate system maintains the same corresponding relation as in the teaching process is checked. Thus, the robot can be precisely controlled.

Abstract: An electronic apparatus and an overvoltage protection structure thereof are provided. The overvoltage protection structure includes a first signal transmission end and a second signal transmission end. The first signal transmission end has at least one first side, and couples to a protected component through a conductive wire. The second signal transmission end has at least one second side, wherein the at least one second side corresponds to the at least one first side and is adjacent to the at least one first side. Therein, there is at least one gap between the at least one second side and the at least one first side, and the gap is positively related to a threshold voltage of the overvoltage protection structure.

Abstract: A method for teaching a robotic arm to pick or place an object includes the following steps. Firstly, the robot arm is pushed until a target appears within a vision. Then, an appearance position of the target is set as a visual point. Then, a first image is captured. Then, the robot arm is pushed to a target position from the visual point. Then, the target position is set as a pick and place point. Then, an automatic movement control of the robot arm is activated. Then, the robot arm automatically picks and places the object and returns to the visual point from the pick and place point. Then, a second image is captured. Then, a differential image is formed by subtracting the second image from the first image, the target image is set according to the differential image, and image characteristic of the target are automatically learned.

Abstract: A method for teaching a robotic arm to pick or place an object includes the following steps. Firstly, the robot arm is pushed until a target appears within a vision. Then, an appearance position of the target is set as a visual point. Then, a first image is captured. Then, the robot arm is pushed to a target position from the visual point. Then, the target position is set as a pick and place point. Then, an automatic movement control of the robot arm is activated. Then, the robot arm automatically picks and places the object and returns to the visual point from the pick and place point. Then, a second image is captured. Then, a differential image is formed by subtracting the second image from the first image, the target image is set according to the differential image, and image characteristic of the target are automatically learned.

Abstract: A wireless network device including a substrate, a plurality of antennas, a central antenna and a radio frequency (RF) transceiving module is provided. The antennas are disposed near an edge of the substrate. The central antenna is disposed near a central point of the substrate. The RF transceiving module is coupled to the antennas and the central antenna. In a first mode of the wireless network device, the RF transceiving module receives and transmits signals through the antennas. In a second mode of the wireless network device, the RF transceiving module receives and transmits the signals through a part of the antennas and the central antenna.

Abstract: A wireless network device including a substrate, a plurality of antennas, a central antenna and a radio frequency (RF) transceiving module is provided. The antennas are disposed near an edge of the substrate. The central antenna is disposed near a central point of the substrate. The RF transceiving module is coupled to the antennas and the central antenna. In a first mode of the wireless network device, the RF transceiving module receives and transmits signals through the antennas. In a second mode of the wireless network device, the RF transceiving module receives and transmits the signals through a part of the antennas and the central antenna.

Abstract: A teaching device and a teaching method for a robotic arm are disclosed. The teaching device comprises a robotic arm, a control device and a gesture recognition module. The gesture recognition module detects a control gesture signal and transmits the detected control gesture signal to the control device. After receiving the control gesture signal, the control device teaches the robotic arm to move and switches between an arm movement mode and a hand movement mode. In an arm movement mode, the control device, aided by an arm ambient image shown on an eye frame and an arm control gesture, teaches an arm unit to move to a target at a high velocity. In the hand movement mode, the control device, aided by a hand vicinity image shown on an eye-in-hand frame and a hand control gesture, teaches a hand unit to move the processing target at a low velocity.

Abstract: A method for automatically recalibrating a robotic arm is provided. Firstly, the recalibration of a robotic arm is automatically activated at a pre-determined time. Then, an eye-in-hand (EIH) camera of the robotic arm is controlled and moved to one or a plurality of pre-determined orientations to focus and capture an image of an identifiable simple pattern, which is fixed with respect to a body of the robotic arm. Then, the detecting orientation of a movable portion of the robotic arm is calculated according to the captured image, and an error between the detecting orientation and the pre-determined orientation is obtained through comparison. Lastly, the error of the robotic arm is calibrated.

Abstract: An encoded calibrating plate is fixed on the working environment of the robot arm and has a chessboard pattern with each square in the chessboard pattern being an encoding. The encoding indicates direction or position on the encoded calibration plate. A visual system of the robot arm captures an image of the encoded calibrating plate, calculates the coordinates encoding of the encoded calibration plate, and positions the visual system to calibrate a positioning error of the robot arm and the visual system.

Abstract: A teaching device and a teaching method for a robotic arm are disclosed. The teaching device comprises a robotic arm, a control device and a gesture recognition module. The gesture recognition module detects a control gesture signal and transmits the detected control gesture signal to the control device. After receiving the control gesture signal, the control device teaches the robotic arm to move and switches between an arm movement mode and a hand movement mode. In an arm movement mode, the control device, aided by an arm ambient image shown on an eye frame and an arm control gesture, teaches an arm unit to move to a target at a high velocity. In the hand movement mode, the control device, aided by a hand vicinity image shown on an eye-in-hand frame and a hand control gesture, teaches a hand unit to move the processing target at a low velocity.

Abstract: An image-based detecting system for traffic parameters first sets a range of a vehicle lane for monitoring control, and sets an entry detection window and an exit detection window in the vehicle lane. When the entry detection window detects an event of a vehicle passing by using the image information captured at the entry detection window, a plurality of feature points are detected in the entry detection window, and will be tracked hereafter. Then, the feature points belonging to the same vehicle are grouped to obtain at least a location tracking result of single vehicle. When the tracked single vehicle moves to the exit detection window, according to the location tracking result and the time correlation through estimating the information captured at the entry detection window and the exit detection window, at least a traffic parameter is estimated.

Abstract: In one exemplary embodiment, a tracking system for region-of-interest (ROI) performs a feature-point detection locally on an ROI of an image frame at an initial time via a feature point detecting and tracking module, and tracks the detected features. A linear transformation module finds out a transform relationship between two ROIs of two consecutive image frames, by using a plurality of corresponding feature points. An estimation and update module predicts and corrects a moving location for the ROI at a current time. Based on the result corrected by the estimation and update module, an outlier rejection module removes at least an outlier outside the ROI.

Abstract: An object tracking method for a non-overlapping-sensor network works in a sensor network. The method may comprise a training phase and a detection phase. In the training phase, a plurality of sensor information measured by the sensors in the sensor network is used as training samples. At least an entrance/exit is marked out within the measurement range of each sensor. At least three characteristic functions including sensor spatial relation among the sensors in the sensor network, difference of movement time and similarity in appearance, are estimated by an automatically learning method. The at least three characteristic functions are used as the principles for object tracking and relationship linking in the detection phase.

Abstract: An apparatus and method for moving object detection computes a corresponding frame difference for every two successive image frames of a moving object, and segments a current image frame of the two successive image frames into a plurality of homogeneous regions. At least a candidate region is further detected from the plurality of homogeneous regions. The system gradually merges the computed frame differences via a morphing-based technology and fuses the merged frame difference with the at least a candidate region, thereby obtaining the location and a complete outline of the moving object.

Abstract: A pixel region-based image segmentation method is disclosed. When an input image is retrieved, pixels thereof are sequentially scanned row by row. Signs of unmarked pixels of the input image are determined according to region features of neighboring pixels of each pixel and pixel update information is recorded to generate a region sign update data table and a region sign feature data table. The pixels of the input frame are further scanned row by row to retrieve signs of the pixels and region signs of the pixels are determined and updated according to the region sign update data table.

Abstract: A method for tracking a moving object is provided. The method detects the moving object in a plurality of continuous images so as to obtain space information of the moving object in each of the images. In addition, appearance features of the moving object in each of the images are captured to build an appearance model. Finally, the space information and the appearance model are combined to track a moving path of the moving object in the images. Accordingly, the present invention is able to keep tracking the moving object even if the moving object leaves the monitoring frame and returns again, so as to assist the supervisor in finding abnormal acts and making following reactions.

Abstract: An image-based detecting system for traffic parameters first sets a range of a vehicle lane for monitoring control, and sets an entry detection window and an exit detection window in the vehicle lane. When the entry detection window detects an event of a vehicle passing by using the image information captured at the entry detection window, a plurality of feature points are detected in the entry detection window, and will be tracked hereafter. Then, the feature points belonging to the same vehicle are grouped to obtain at least a location tracking result of single vehicle. When the tracked single vehicle moves to the exit detection window, according to the location tracking result and the time correlation through estimating the information captured at the entry detection window and the exit detection window, at least a traffic parameter is estimated.

Abstract: A method for detecting a shadow of an object in an image is provided. A moving object in a plurality of continuous images is detected. A histogram of a color variation of the moving object in each of the images is calculated. The histograms of the color variation are accumulated to obtain a cumulative histogram. A distribution of the color variation in the cumulative histogram is estimated to obtain a shadow distribution function. Whether each pixel in a received image belongs to the shadow is determined by using the shadow distribution function.

Abstract: In one exemplary embodiment, a tracking system for region-of-interest (ROI) performs a feature-point detection locally on an ROI of an image frame at an initial time via a feature point detecting and tracking module, and tracks the detected features. A linear transformation module finds out a transform relationship between two ROIs of two consecutive image frames, by using a plurality of corresponding feature points. An estimation and update module predicts and corrects a moving location for the ROI at a current time. Based on the result corrected by the estimation and update module, an outlier rejection module removes at least an outlier outside the ROI.

Abstract: An apparatus and method for moving object detection computes a corresponding frame difference for every two successive image frames of a moving object, and segments a current image frame of the two successive image frames into a plurality of homogeneous regions. At least a candidate region is further detected from the plurality of homogeneous regions. The system gradually merges the computed frame differences via a morphing-based technology and interests with the at least a candidate region, thereby obtains the location and a complete outline of the moving object.