Finding Familiar Objects and their Depth from a Single Image

We present a classification-based method to identify objects of interest, and judge their depth in a single image. Our approach is motivated by a postulate of human depth perception that people can give a credible depth estimation for an object whose familiar size is known, even without using stereo vision. To emulate the mechanism, we categorize objects into the same class if they have similar sizes and shapes, and model the sense of discovering a familiar object by applying multiple kernel logistic regression to the conditional probability of feature types. The depth of a detected target can then be obtained by referencing its corresponding object category. Overall, the proposed algorithm is efficient in both the training and testing phases, and does not require a large amount of training images for good performances.