Abstract: A system for automatically applying a bead of sealant within a peripheral groove defined between two elements coupled to each other and having dimensions that are not strictly predetermined. In one example, a manipulator robot includes a sealant dispensing head including a dispensing nozzle and a profilometer. An electronic controller moves the sealant dispensing head for a first pass along the peripheral groove to detect the profile of the facing lateral surfaces defining the peripheral groove. The correct amount of sealant is calculated based on the detected profile of the groove. The electronic controller moves the dispensing head for a second pass to dispense the calculated amount of sealant in the peripheral groove. In one example, the sealant is dispensed with a constant flow rate and the speed of movement of the dispensing nozzle varies to apply the calculated amount of sealant to each portion of the peripheral groove.

Abstract: A method for analysing the quality of a weld bead in a welding zone using a thermal camera. A thermal image (IMG) of a given area is divided into a plurality of sub-areas each having a respective temperature (Ti). During a learning step, the temperature evolution (Ti(t)) of each sub-area is monitored for different welding conditions. During a training step, the temperature evolutions (Ti(t)) are processed for training a classifier (304). For this purpose, a respective cooling curve is extracted (302) from each temperature evolution (Ti(t)), and parameters (F) are determined that identify the shape of each cooling curve. The parameters (F) are used as input features for the classifier (304). In normal operation the temperature evolution (Ti(t)) of each sub-area (Ai) is monitored and the classifier (304) estimates weld quality (S).

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient includes the steps of: receiving a first image data from an image capture device, wherein the first image data includes the first skin portion; identifying a first skin mask corresponding to the first skin portion; identifying one or more first keypoints within the first skin mask, wherein each first keypoint comprises an abnormality on a first body region within the first skin portion; receiving a second image data from an image capture device, wherein the second image data includes a second body region within a second skin portion, and wherein the first and second image data are sequentially captured; and comparing the first and second body regions of the first and second image data to match the skin mask of the first and second image data.

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient including the steps of receiving a first image data from an image capture device and identifying a first skin mask corresponding to the skin portion. The image data includes the skin portion and a first scaling element on the skin portion. The method further includes the steps of identifying one or more first keypoints within the first skin mask and classifying the one or more keypoints. Each first keypoint comprises an abnormality on the skin portion.

Abstract: A method of monitoring skin abnormalities on a skin portion of a patient including the steps of receiving a first image data from an image capture device and identifying a first skin mask corresponding to the skin portion. The image data includes the skin portion and a first scaling element on the skin portion. The method further includes the steps of identifying one or more first keypoints within the first skin mask and classifying the one or more keypoints. Each first keypoint comprises an abnormality on the skin portion.