Get to know the algorithms that that enables computers to perceive

Introduction

The word computer vision means the ability of a computer to see and perceive the surrounding. A lot of application holds for computer vision to cover — Object detection and recognition, self driving cars, facial recognition, ball tracking, photo tagging, and many more. Before diving in the technical jargons, first let’s discuss the entire computer vision pipeline.

The entire pipeline is divided into 5 basic steps, each with a specific function. Firstly, the input is needed for the algorithm to process that can be in the form of an image or stream of image (image frames). The next step is pre-processing. In this step, functions are applied to the incoming image(s) so that the algorithm can better understand the image.

Some of the functions involve noise reduction, image scaling, dilation, and erosion, removing color spots, etc. The next step is selecting the area of interest or the region of interest. Under this lies the object detection and image segmentation algorithms. Further, we have feature extraction that means retrieving relevant information/features from the images that are necessary for accomplishing the end goal.

The final step is recognition or prediction, where we recognize objects in a given frame of images or predict the probability of the object in a given image frame.

Example

Let’s look at a real world application of the computer vision pipeline. Facial expression recognition is an application of computer vision that is used by a lot of research labs to get an idea of what effect a particular product has on its users. Again, we have input data to which we apply the pre-processing algorithms.

The next step involves detecting faces in a particular frame and cropping that part of the frame. Once this is achieved, facial landmarks are identified like mouth, eyes, nose, etc. — key features for emotion recognition.

In the end, a prediction model( trained model) classifies the images based on the features extracted in the intermediary steps.

Algorithms

Before I start mentioning the algorithms in computer vision, I want to stress the term ‘Frequency’. The frequency of an image is the rate of change of intensity. High-frequency images have large changes in intensity. A low-frequency image is relatively uniform in brightness or the intensity changes slowly.

On applying Fourier transform to an image we get a magnitude spectrum that yields the information of the image frequency. Concentrated point in the center of the frequency domain image means a lot of low frequency components are present in the image. High frequency components include — edges, corners, stripes, etc. We know that an image is a function of x and y f(x,y). To measure the intensity change, we just take the derivative of the function f(x,y).

**Sober Filter **

The Sobel operator is used in image processing and computer vision for edge detection algorithms. The filter creates an image of emphasizing edges. It computes an approximation of the slope/gradient of the image intensity function. At each pixel in the image, the output of the Sobel operator is both the corresponding gradient vector and the norm of this vector.

The Sobel Operator convolves the image with a small integer-valued filter in the horizontal and vertical directions. This makes the operator inexpensive in terms of computation complexity. The Sx filter detects edges in the horizontal direction and Sy filter detects edges in the vertical direction. It is a high pass filter.

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