Difference between feature detection and object detection

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 shape开发者_JAVA百科s 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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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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Normally objects are collections of features. A feature tends to be a very low-level primitive thing. An object implies moving the understanding of the scene to the next level up.

A feature might be something like a corner, an edge etc. whereas an object might be something like a book, a box, a desk. These objects are all composed of multiple features, some of which may be visible in any given scene.

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Invariance, speed, storage; few reasons, I can think on top of my head. The other method to do would be to keep the complete image and then check whether the given image is similar to glass images you have in your database. But if you have a compressed representation of the glass, it will need lesser computation (thus faster), will need lesser storage and the features tells you the invariance across images.

Both the methods you mentioned are essentially the same with slight differences. In case of Haar, you detect the Haar features then you boost them to increase the confidence. Boosting is nothing but a meta-classifier, which smartly chooses which all Harr features to be included in your final meta-classification, so that it can give a better estimate. The other method, also more or less does this, except that you have more "sophisticated" features. The main difference is that, you don't use boosting directly. You tend to use some sort of classification or clustering, like MoG (Mixture of Gaussian) or K-Mean or some other heuristic to cluster your data. Your clustering largely depends on your features and application.

What will work in your case : that is a tough question. If I were you, I would play around with Haar and if it doesn't work, would try the other method (obs :>). Be aware that you might want to segment the image and give some sort of a boundary around for it to detect glasses.