The term object recognition is used somewhat broadly. It sometimes refers to the overall capability of a system to both detect and identify objects in an image (so including classification and detection). Other times, it’s used synonymously with image classification for objects (i.e., “recognize what object is present”). Historically in computer vision, “object recognition” was the field dealing with identifying object instances or categories in images, which encompassed things like template matching, keypoint matching for specific instances (recognizing a specific face or specific landmark building), and category recognition. Today, the term could encompass detection, classification, segmentation — any kind of recognition of object presence and identity. It’s a high-level term: for example, “object recognition in video” might mean detecting and tracking what objects appear. In summary, object recognition is about a machine seeing an image and knowing what objects are in it and where, akin to the human ability to see and recognize surroundings.
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