Are you tired of reading the same articles on machine learning? Do you want to learn about how computer vision works and get your hands dirty with it? If so, this article is for you. In this series of blog post we'll go over what computer vision is, how it works and why it's important in today's world.

We've all had the experience of seeing something and recognizing it. It might be a face, or a bird, or even an object in your own home. But what's happening in your brain when you see these things? Recently, researchers have begun to try and answer this question by studying how human brains process visual information. In this article we'll explain some of the basics of how our brains process images, and why understanding these basic processes is important for building the next generation of computer vision systems.

Computer vision is the process of analyzing images and video in order to extract useful information about them. It's a very broad field, with many different applications and areas of focus. The most common applications include object recognition and tracking, image retrieval from large databases, visual searching engines like Google Images or Amazon products search (which uses computer vision to identify images as well as match product categories), and more!

Feature detection and matching are an essential component of many computer vision applications. In this section, we’ll explore how feature detection works and how it can be used to detect features in images.

• Feature detection is the process of finding points, lines, edges and corners in an image. A simple example would be finding all red objects in an image; this type of task is called “classification” because you want to classify each object as belonging to one class only (e.g., all red objects).

• On the other hand, feature matching refers to finding similarities between two different images so that you can identify those features shared by both images that were taken at different times or locations but with similar lighting conditions (e.g., two cameras).

Computer vision has been the subject of intense research over the past few decades, and is now a critical part of many algorithms used in modern applications. In this article, I'll discuss some important computer vision algorithms that you should know:

- Image Classification

Image classification is a computer vision task that involves labeling an image based on its content. It can be used to classify images into categories or parts of them.

Image classification has many applications in different fields, such as image search and object recognition. In this section we will focus on two popular algorithms: support vector machine (SVM) and neural network.

- Object Detection

Object detection is the problem of detecting the presence of objects in images, videos, or 3D data and localizing them in some coordinate system. The goal is to find an object even if there are no known features that distinguish it from background noise.

Object detection can be done with a variety of methods including:

• Random Forest (RF)

• Boosted Decision Tree (BDT), SVM, K-Nearest Neighbors (KNN)

- Semantic Segmentation

In this section, we will discuss the problem of semantic segmentation. Semantic segmentation is a task where a camera takes an input image and outputs one or more classes of objects in it. These classes can be used to label images with labels such as “person”, “car” or “animal”.

In order to solve this problem we use several algorithms:

Supervised Learning: In supervised learning we have one labeled dataset (labeled by humans) and another unlabeled dataset which we would like our algorithm to learn from. We will train our model on both datasets using cross-validation technique so that only part of our data gets labeled by humans while rest is left out for training purpose. Once trained well enough, these models can then be applied on new unseen datasets without any human intervention needed for labeling them correctly! This type of machine learning method requires preparation beforehand because it requires huge amount of time investment before results start coming out consistently across multiple runs due lack lack proper parameter tuning etc..

- Instance Segmentation

Instance segmentation is the process of identifying and labeling the pixels in an image that belong to a particular object. It is a type of image segmentation, which involves finding regions (or objects) in images. In other words, instance segmentation is used for detecting objects or sets of objects in images.

Instance segmentation can be applied to several types of applications and tasks like:

• Object detection: This technique tries to find all the instances (objects) present in an image and classify them as belonging to different classes such as fish, car etc., based on their characteristics such as color or texture etc..

Face recognition: Face recognition attempts at identifying faces from images using features like height/width ratio between eyes & nose tip position relative with respect to mouth area

- Optical Flow

Optical flow is a method to estimate the motion of objects in a video. It can be used for many computer vision applications such as video stabilization, object tracking, video segmentation and more. Optical flow can also be used to estimate the surface normal of an object by detecting small changes in images over time.

The algorithm works by measuring how much each pixel has changed since it was last captured (i.e., how far away it has moved). The algorithm then uses these measurements to track objects along their trajectories through space based on their relative positions between consecutive frames taken at different times (time series).

- Human Pose Estimation

Human Pose Estimation is the task of estimating the 3D position and orientation of a human body from a single image. It is used in robotics and gaming applications, as well as for other purposes such as recognition and tracking.

The goal of this task is to estimate the pose (orientation and position) of a human body from a single image.

- Super Resolution

Super Resolution is a computer vision technique that aims to improve the resolution of an image.

Super Resolution is also known as upscaling, interpolation, or reconstruction.

Super Resolution is used in many applications such as satellite imaging, microscopy and security cameras.

- Style Transfer/Image-to-Image Translation/Domain Adaptation/etc.

This is an algorithm that can be used to transfer the style of one image to another. It’s commonly used in artistic images or for stylizing a photo.

The idea behind style transfer is simple, but it’s not easy to achieve: you take an original image and create a copy that looks like it was taken by someone else—but not too much like them! For example, imagine if you wanted your neighbor's dog (who happens to be black) on your white couch with a yellow blanket draped over its back & your goal would be to get something more like this.

I suggest that you also visit this site to know more about CV algorithms and their applications. The site is a collection of papers on CV algorithms, which have been categorized into different topics. Each paper has been well written, easy to understand and peer reviewed by experts in the field.

Conclusion

If you are planning to use computer vision for your product, then it is important that you have a good understanding of the different algorithms available in the market. This will enable you to choose the most suitable algorithm for your application. We have tried to cover all the major algorithms used by researchers toda