Abstract: The described embodiments relate generally to a camera system to enhance food and material identification. More particularly, the described embodiments relate to using a plurality of camera sensors and LED light sources, including visible image sensors, visible light source, near infrared (NIR) image sensors, two or more bands of infrared light sources, depth camera images, and synchronized light source. The visible image data, NIR image data, depth image data are processed to obtain combined image data. A machine learning algorithm is employed to automatically compute an output based on the combined image data. The output includes information about the identity and the location of the food item.

Abstract: A novel in-vivo image capture device for capsule endoscope and its method of operation are described. The device includes a wafer level camera module design, high sensitivity backside illumination pixel with high definition image output and LED's to provide illumination, which is synchronized with an image sensor strobe signal. A frame rate of the device can be adjusted based on an angular motion detection from a gyroscope sensor, in which a high frame rate mode is maintained during fast motion while a low frame rate is maintained during slow or no motion. The image capture device also includes machine learning based SOC for image processing, enhancement, and compression. The SOC can process and store zone average of images. The image capture device also includes a high density flash storage to store images in the device, thus no RF transmitter is needed, which make the system more convenient to use.