Top Things to Consider While Developing Your Computer Vision Project

Computer Vision is an interdisciplinary field that enables computers to extract useful information from visual inputs. Artificial intelligence assists computers in thinking, whereas computer vision assists them in perceiving and comprehending their environment. Computer vision simulates the human eye and is used to train machines to perform various functions using a camera, data, and algorithms rather than retinas, visual cortex, and optic nerves, and it has to do so in a shorter time.

Why are computer vision projects important?

Indeed computer vision is one of the hottest research fields these days. Technology has become a part of our daily life, and we all utilize various functionalities that involve computer vision techniques on the backend, whether we realize it or not.

There are vast purposes why one can take on a computer vision project. For instance, computer vision projects can assist in solving problems pertaining to our daily lives. They can even be used for fun purposes, i.e., developing a face fun app that swaps faces.

The combination of computer vision and machine learning especially can solve a multitude of problems, running from face recognition to monitoring diabetes. Yet, these days computer vision is also gaining popularity in the field of augmented reality and virtual reality.

Steps to build a simple computer vision project

Building a computer vision project can be complicated, yet it all starts making sense when you break it down into tiny tasks. Here are the common steps in building a computer vision project:

1. Data

Data is a basic building step in creating a computer vision project. It can be collected manually by using web scraping tools. Besides, it can also be gathered by using devices such as cameras or sensors. In the meantime, input data can be in the form of images, videos, or text.

2. Data preprocessing

Data preprocessing is done in order to remove unnecessary information and assist the model inefficiently in learning the features of the images. Moreover, it is used to verify that the data used is in the correct format. Normalization, data augmentation, image resizing, and blurriness are some data preprocessing techniques.

3. Algorithms

One should develop a thorough understanding of diverse image processing algorithms. SIFT and SURF algorithms are used to extract features from visual inputs so that these features can later be used to train a classifier to perform some tasks. Gaussian Image Processing, Edge Detection, and Convolutional Neural Networks are commonly used in image processing algorithms.

4. Training of model

Training a computer vision model is a complex and iterative process that can be intimidating. Train your model using a clean and organized dataset. Common Objects in Context – COCO dataset, PASCAL Visual Object Classes, and ImageNet are popular existing datasets that you can use to train a model.

5. Testing

Once the model is built using a training dataset, then it can be tested by feeding in a new unclassified image and checking if the model classifies or detects the images or not.

6. Know about the language

There are several programming language choices for computer vision – OpenCV using C++, OpenCV using Python, or using MATLAB. Most developers have a personal favorite, depending on the task they perform. Due to its flexibility, beginners frequently choose OpenCV with Python. Most programmers are familiar with this language, and it is quite popular among developers due to its versatility.

7. Know about IDE

It might be difficult to find the best IDE for your tasks with all the necessary features, especially when you are working in machine learning. Jupyter Notebooks, RStudio, PyCharm, and Spyder are the most efficient machine learning IDEs.

8. Libraries

Learn how to use OpenCV, Keras, and TensorFlow libraries. OpenCV is a real-time computer vision library that has multiple interfaces like C++, Phyton, Java, and MATLAB. Keras is a user-friendly Python library that provides backend support. TensorFlow is an open-source software library for machine learning which also supports interfaces like JavaScript, Java, and C++.

9. Filters

Filter plays an important part in computer vision. Different filters can be used to enhance or detect edges in the image. Filters themselves are the matrix of pixels, like the images. Most images are in RGB form, which means there are 3 channels, and each channel has its own value, which ranges from 0-255. There can be multiple types of filters, i.e., 3*3, 5*5, that depends on the users’ case. Gaussian, Box, Canny edges and Bilateral filters are well-known filters used in image processing.

Examples of computer vision tasks

Here we will explore some of the common computer vision tasks, including object detection, image segmentation, and image classification:

Image classification

With the rising necessity and uses of AI, areas like machine learning, deep learning, and neural networks have exploded in popularity. Software and tools such as classifiers are required for the training, as they feed large amounts of data, analyze them, and extract important features. Image classification is a subset of classification problems that is used to classify an image into a certain category. Convolutional Neural Network, K nearest neighbor, Decision Tree, and Support Vector machines are some popular algorithms used for classification.

Object detection

It’s an approach used in computer vision for the identification and location of things in visual inputs (images or videos). Object detection creates bounding boxes around identified items and allows us to see where they are in a scene. Moreover, it is widely used in computer vision in tasks that involve face recognition, crowd counting, object tracking, and much more. Moreover, the most used algorithms for object detection are Fast R-CNN, Faster R-CNN, Single Shot Detector (SSD), Spatial Pyramid Pooling (SPP-net), YOLO, etc.

Image segmentation

Image segmentation is another key topic in computer vision. Image segmentation is the method of separating an image into separate sections based on pixel characteristics to detect objects or boundaries, which in return simplify and analyze a picture more quickly. Segmentation has an impact on a variety of industries, from cinema to medical. Image segmentation techniques include Thresholding Method, Edge-Based and Region-Based. Mask R-CNN and U-Net are some of the famous segmentation algorithms in deep learning.

Key thoughts

For developing computer vision projects, one should have a clear idea of the language and libraries used. Data plays a vital role in developing a CV project as well. You should be familiar with the data preprocessing techniques in order to develop a scalable CV project. There are several types of projects one can use to develop a CV, including but limited to detection, classification, and segmentation.