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Asked: 2026-05-25T23:51:07+00:00

I know that most common object detection involves Haar cascades and that there are

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I know that most common object detection involves Haar cascades and that there are many techniques for feature detection such as SIFT, SURF, STAR, ORB, etc… but if my end goal is to recognizes objects doesn’t both ways end up giving me the same result? I understand using feature techniques on simple shapes and patterns but for complex objects these feature algorithms seem to work as well.

I don’t need to know the difference in how they function but whether or not having one of them is enough to exclude the other. If I use Haar cascading, do I need to bother with SIFT? Why bother?

thanks

EDIT: for my purposes I want to implement object recognition on a broad class of things. Meaning that any cups that are similarly shaped as cups will be picked up as part of class cups. But I also want to specify instances, meaning a NYC cup will be picked up as an instance NYC cup.

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  1. Editorial Team

    Object detection usually consists of two steps: feature detection and classification.

    In the feature detection step, the relevant features of the object to be detected are gathered.

    These features are input to the second step, classification. (Even Haar cascading can be used
    for feature detection, to my knowledge.) Classification involves algorithms
    such as neural networks, K-nearest neighbor, and so on. The goal of classification is to find
    out whether the detected features correspond to features that the object to be detected
    would have. Classification generally belongs to the realm of machine learning.

    Face detection, for example, is an example of object detection.

    EDIT (Jul. 9, 2018):

    With the advent of deep learning, neural networks with multiple hidden layers have come into wide use, making it relatively easy to see the difference between feature detection and object detection. A deep learning neural network consists of two or more hidden layers, each of which is specialized for a specific part of the task at hand. For neural networks that detect objects from an image, the earlier layers arrange low-level features into a many-dimensional space (feature detection), and the later layers classify objects according to where those features are found in that many-dimensional space (object detection). A nice introduction to neural networks of this kind is found in the Wolfram Blog article “Launching the Wolfram Neural Net Repository”.

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