Object Extraction Using Probabilistic Maps of Color, Depth, and Near-Infrared Information - Muhammad Attamimi; Kelvin Liusiani; Astria Nur Irfansyah; Hendra Kusuma; Djoko Purwanto (JAREE (Journal on Advanced Research in Electrical Engineering), 2020)
Object extraction is one of the important and chal-lenging tasks in the computer vision and/or robotics ? elds.This task is to extract the object from the scene using anypossible cues. The scenario discussed in this study was the objectextraction which considering the Space of Interest (SOI), i.e.,the three dimensional area where the object probably existed.To complete such task, the object extraction method based onthe probabilistic maps of multiple cues was proposed. Thanksto the Kinect V2 sensor, multiple cues such as color, depth, andnear-infrared information can be acquired simultaneously. TheSOI was modeled by a simple probabilistic model by consideringthe geometry of the possible objects and the reachability of thesystem acquired from depth information. To model the color andnear-infrared information, a Gaussian mixture models (GMM)was used. All of the models were combined to generate theprobabilistic maps that were used to extract the object fromthe scene. To validate the proposed object extraction, severalexperiments were conducted to investigate the best combinationof the cues used in this study.Keywords: color information, depth information, near-infrared information, object extraction, probabilistic maps.