Computer Vision is a field of Artificial Intelligence that deals with images and pictures to solve real-life visual problems. The ability of the computer to recognize, understand and identify digital images or videos to automate tasks is the main goal that computer vision tasks seek to accomplish and perform successfully.
Construction of computer vision projects is one of the most fun experiences. However, the machine learning or deep learning models you build for computer vision needs to be built precisely and used effectively for producing better results.
The main objective of this article is to provide a solid foundation on how you can effectively construct your Computer Vision projects using the modules available to you with python or any programming language of your preference. And how you can implement them effectively on a smaller or larger platforming scale.
Let us get started with a brief understanding of computer vision, then proceed to understand how to construct these models, and finally, how we can make the best use of them.
So, without further ado, let us get started!
Brief Understanding of Computer Vision:

Humans have no problem to identify the objects and the surroundings around them. However, it is not so easy for computers to identify and distinguish the various patterns, visuals, images, and objects in the environment.
The reason for this difficulty arises because the interpretability of the human brain and eyes differ from computers, which interpret most of the outputs in either 0’s or 1’s i.e. in binary.
The images are often times converted in arrays of three dimensions consisting of the colors red, blue, green. They have a range of values that can be computed from 0 to 255 and using this conventional means of arrays, we can write code exclusive to identify and recognize images.
With the rising technology and advancements in machine learning, Deep Learning, and computer vision, modern computer vision projects can solve complicated tasks like image segmentation and classification, object detection, face recognition, and so much more.
Computer Vision is perhaps the most intriguing and fascinating concept in artificial intelligence. Computer Vision is an interdisciplinary field that deals with how computers or any software can learn a high-level understanding of the visualizations in the surroundings. After obtaining this conceptual perspective, it can be useful to automate tasks or perform the desired action.
How to effectively build computer vision projects?

For every specific computer vision task, a variety of Machine Learning or deep learning algorithms can be used accordingly for the processing of your tasks at hand.
You can perform a face recognition task with the deep learning of a machine learning algorithm like support vector machines (SVMs) or construct a model to perform the same task with deep learning and convolutional neural networks.
Experimenting and trying out a variety of models and ideas is the best way to attain suitable results for these complex tasks. Let us understand this with an example of the human emotion and gesture recognition project.
This project uses computer vision and deep learning to detect the various faces and classify the emotions of that particular face. Not only do the models classify the emotions but also detects and classifies the different hand gestures of the recognized fingers accordingly.
After distinguishing the human emotions or gestures a vocal response is provided by the trained model with the accurate prediction of the human emotion or gesture respectively. The best part about this project is the wide range of data set choices you have available to you.
More details on this project can be obtained by following the below link. Feel free to check it out for a more detailed explanation.
Human Emotion and Gesture Detector Using Deep Learning: Part-1
You can see that I worked out a variety of models to find a decent and suitable example for solving this complex problem. It is only with hit-and-trial methods you can achieve the best possible results. The projects built can effectively differentiate the various emotions and gestures. However, it is not perfect by any means and there is still a lot of room for improvement.
In the next section, we will understand the improvements that can be made for effectiveness and efficiency.
How do you effectively Utilize your computer vision models and projects?

The most essential aspect of any computer vision project is to effectively utilize them to work efficiently and produce results regardless of the quality of tasks they are performing and the device they are made to work on.
Post-training analysis sometimes also referred to as post-mortem analysis plays a major role in the optimization of models. The business models built and trained need to be optimized in order for them to work efficiently on lower-end devices and embedded systems, like the raspberry pi.
One of the principal components of building and evaluating models is examining the predictive capabilities and performance quality of the model. A more paramount concept is understanding the limitations of your machine learning or deep learning model.
Overcoming these limitations is the key to a successful model. In the field of computer vision, especially while performing real-time and real-life it becomes significant to accomplish these tasks with higher precision and accuracy.
An example of this is a face recognition model constructed using deep learning and computer vision. Check out the article below for further details on the implementation of such a similar project from scratch.
Let us understand the improvements and fixes that can be made respectively to the implementation of this model for even more effective performance.
- One-shot learning and training methods can be used for reducing the training time for each face. Since the current model recognizes only one face, if we want to add more faces we need to re-train the entire model. For this reason methods like one-shot learning needs to be considered for improving the quality and performance of the models.
- Alternatives to haarcascade_frontalface_default.xml can be found to improve the accuracy of the detection of the faces at any particular angle. An alternative can be to make a custom XML file for both front and side faces.
- To make it run on embedded devices changes can be made on the memory constraints like converting to tf.float(32) and also changes can be made on the model by considering the use of tflite.
This example was to provide the viewers with a basic understanding of the vast improvements that can consistently be made for accomplishing complicated tasks and how you can effectively utilize your computer vision projects.

Conclusion:
With this, we have come to the end of this article. I hope this guide was helpful to all of you to strengthen your basics and understand the significant aspects of the implementation of computer vision projects.
Understanding how things work internally is crucial in computer vision because it helps you figure out how exactly the computer analyzes and processes the data as well as appreciate the beauty behind its methodologies.
In case you have any queries, issues, or problems related to the same, then feel free to hit me up and let me know what you could not understand. I will try my best to explain it to you and help you conceptually solve it.
Check out some of my other articles that you might enjoy reading!
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!