What are the core computer vision algorithms?

There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and knowledge. Of course, this answer will only give you a flavour of the type of algorithm or model that you will find while solving CV tasks.

For example, there are algorithms that are used to extract keypoints and descriptors (which are often collectively called features, although the descriptor is the actual feature vector and the keypoint is the actual feature, and in deep learning this distinction between keypoints and descriptors does not even exist, AFAIK) from images, i.e. feature extraction algorithms, such as SIFT, BRISK, FREAK, SURF or ORB. There are also edge and corner detectors. For example, the Harris corner detector is a very famous corner detector.

Nowadays, convolutional neural networks have basically supplanted all these algorithms in many cases, especially when enough data is available. Rather than extracting the typical features from an image (such as corners), CNNs extract features that are most useful to solve the task that you want to solve by taking into account the information in the training data (which probably includes corners too!). Hence CNNs are often called data-driven feature extractors. There are different types of CNNs. For example, CNNs that were designed for semantic segmentation (which is a CV task/problem), such as the u-net, or CNNs that were designed for instance segmentation, such as mask R-CNN

There are also algorithms that can be used to normalize features, such as the bag-of-features algorithm, which can be used to create fixed-size feature vectors. This can be particularly useful for tasks like content-based image retrieval.

There are many other algorithms that could be considered CV algorithms or are used to solve CV tasks. For example, RanSaC, which is a very general algorithm to fit models to data in the presence of outliers, can be used to fit homographies (matrices that are generally used to transform planes to other planes) that transform pixels of one image to another coordinate system of another image. This can be useful for the purpose of template matching (which is another CV task), where you want to find a template image in another target image. This is very similar to object detection.

There are also many image processing algorithms and techniques that are heavily used in computer vision. For example, all the filters (such as Gaussian, median, bilateral, non-local means, etc.) that can be used to smooth, blur or denoise images. Nowadays, some deep learning techniques have also replaced some of these filters and image processing techniques, such as denoising auto-encoders.

All these algorithms and models have something in common: they are used to process images and/or get low- or high-level information from images. Most of them are typically used to extract features (i.e. regions of the images that are relevant in some way) from images, so that they can later be used to train a classifier or regressor to perform some kind of task (e.g. find and distinguish the objects, such people, cars, dogs, etc. in an image). The classifiers/regressors are typically machine learning (ML) models, such as SVMs or fully-connected neural networks, but there's a high degree of overlap between CV and ML because some ML tools are used to solve CV tasks (e.g. image classification).