Abstract: A portable computing device equipped with at least one image capture device and/or a light source captures an image (or a video) of a portion of a surface of interest having the damage that is exposed to a light from the light source. The portable computing device converts the image to an output image that highlights the damage. If the damage is a dent, the image is converted to a false color image using a saliency algorithm. If the damage is a scratch, the image is converted to a colorspace stretched image using color stretching algorithms. The size of the damage is determined by capturing an image of a ruler placed adjacent to the damage and the portion of surface of interest having the damage. The ruler is then removed from the image. The resulting image is converted to the output image. The ruler is added to the output image.

Abstract: A portable computing device equipped with an image capture device captures an image of a vehicle dashboard of a vehicle. Then, the portable computing device identifies the location of one or more components of the vehicle dashboard in the captured image. Based on the location of the one or more components, the portable computing device segments the captured image to obtain an image of each of the one or more components. Further, the portable computing device processes the images of the one or more components using one or more machine learning models to determine a reading associated with each of the one or more components. An accuracy of the readings is verified and responsively, the portable computing device inputs the readings in respective data fields of an electronic form. The readings associated with the one or more components of the vehicle dashboard represent data associated with the vehicle.