Computer Vision (CV) is a fast developing branch of Machine Learning, that uses images and videos to extract knowledge about the world. Because the sense of sight is so important for humans and we have a lot of actions dependent on it, Computer Vision applications will become crucial in future automation, visual intensive works like RTG luggage inspection, finding criminals with public cameras, or preventing financial fraud using face recognition. This domain will open new areas of development and help to create new industries.
Below we will try to explain some of the real Computer Vision applications.
Computer Vision applications
Object Detection – the most popular computer vision applications
Object Detection is a part of Computer Vision that focuses on detecting various objects on photos like cats, dogs, cars, bikes, humans, etc., by extracting features from pixels and applying deep learning to recognize patterns.
One of the main areas of Object Detection is face recognition.
Image and Video Pre-Processing
Advanced CV with the use of neural networks can perform image transformations not available for traditional image processing algorithms. As an example, we can artificially increase the number of trees or remove them without noticing an artificial change.
It is possible to generate missing parts of the photo or change the sky’s appearance from Earth to Mars. Possibilities of image enhancing and transformation are limitless and require just creating a specialized model for a given task.
Traditionally, to detect an object on an image it used to be sufficient to just select its position by the rectangle. Now, an improvement of this technique is outlining the given object (for example by a slight change of its color) and in that way segment image on different objects where the result is obtaining an image very similar to the stained glass. This technology will be extensively used in autonomous navigation and radiology (outlining cancerous changes in tissue).
Video and Image Content Indexing
A model trained to detect objects on photos can extract its content and prepare tags automatically. Nowadays, the inference is so fast that videos can be processed in real-time. This technology can be used in personalized advertisements (for example screens in public space) where ads are chosen basing on your clothes and things you carry.
3D Scene Reconstruction
Algorithms of Computer Vision are able to reconstruct 3D objects from 2D imagery taken from different angles. As an example, we can acquire a city model from images gathered by drones. We may even create a model of the cave based on a movie recorded inside it.
Deep Learning in Building Computer Vision Applications
Deep Learning (DL) originates its name from a large number of layers in neural networks. Thanks to the constant development of computing power in recent years, we are now available to train more and more complex neural networks with an increased number of NN layers. Such sophisticated models better generalize truths hidden in data than “shallow” neural networks.
Computer Vision uses special types of neural networks called Convolutional Neural Networks. They use convolutional layers which are 2D surfaces learning from correlations between image pixels. CNN watches images multiple times, tweaking its parameters constantly to improve the outcome.
Real-life Examples of Computer Vision Applications
Retail Shelf analysis
Automatic product detection allows recognizing missing and misplaced products on shelves with comparison to the planogram. Aggregated information about shop conditions gives the opportunity to improve the quality of customer service.
Computer Vision can also automate the process of discovering illicit items in luggage during customs inspection on the airports. Such a mundane task is ideal for Convolutional Neural Networks taking into consideration the huge size of the available data-set.
Automatic video tagging for real-time marketing
This technology will improve the advertisement industry, making it more personalized. For example, after tagging customer’s favorite brands and gaining deep insights into their preferences, we can recommend products with a higher probability of being chosen. It is a win-win situation for both customers (more relevant ads) and e-commerce (higher income).
Real estate valuation
Having real estate imagery data with its value, we can create a model that will predict value from new real estate photos. It allows a fast comparison of given and predicted prices in order to find investment gems or to find undervalued rent occasions.
Recognizing faces in security systems
Make identification easier for security officers and ordinary people – no more need for additional cards or keys. Also, there is a possibility to determine when somebody is a wanted criminal.
Automatic reading of personal information from identity cards
This technique protects from misspelling and it is much faster than reading information manually. It has the potential to simplify maintaining a customer database and improve the quality of data.
CV techniques use data from cameras to visually check the condition of assets, for example, valves and pipes, and compare it with optimal conditions. This information can be transferred to a remote maintenance crew, that checks anomalies. This type of solution saves time, as there is no need to manually check everything, and makes work easier.
Computer Vision uses data gathered from sensors to drive a car safely from point A to B. It can automate our commuting habits and make life a lot easier especially for elderly and disabled people. Although this technology can increase car usage (hence increase traffic), it can also prevent accidents and reduce the number of cars by automating taxi system self-driving cars, so there will be no need to own a private car.
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Python – The Best Open-source Tool for Computer Vision Applications
Training Convolutional Neural Networks using Python has become easier thanks to a great abundance of libraries to choose from. Below we present the most popular ones:
Caffe is a framework built especially to be used in deep learning. Developed in Berkeley, it is one of the best libraries for CV where models are not defined in code but in configuration files that can be a drawback for some of us. It isn’t developed in Python but it provides bindings to it. Caffe is known to be fast, it can inference an image in 1ms and learn from it in 4ms if used e.g. on Nvidia K40 GPU.
Theano is one of the oldest Python libraries built for operating on multi-dimensional arrays and that allow training neural networks. It is integrated with NumPy, it has efficient symbolic differentiation, possibilities to evaluate expressions faster thanks to dynamic C code generation, and can automatically diagnose many types of errors. Its development has finished in late 2017 but it is still a decent library to use for your project.
TensorFlow was designed by Google Brain Team and released as an open-source library for abstract (using tensors) numerical computation. It is a low-level library, old enough to have many sophisticated projects using it as a backbone, decent documentation, and vast community. TensorFlow’s main advantage (over Theano) is multi-GPU support. It has two API: low-level (original), and high-level Keras.
Lasagne is built on top of Theano with the intention to be simple to understand, use, and easy to directly process and return Theano expression or NumPy data types. Lasagne allows defining Convolutional Neural Networks, Recurrent Neural Networks, and its combinations. It supports CPU and GPU thanks to Theano’s compiler. In terms of library level, it is medium – somewhere between low-level libraries like TensorFlow or Theano and high-level libraries like Keras.
Keras is a high-level library that uses TensorFlow, CNTK, or Theano as a back-end. It is officially supported by Google (TensorFlow) which has intercepted its development. Keras positions itself as a CV API for “human beings”. It focuses on simplicity so creating networks is fast and intuitive. Model architecture is divided on fully-configurable modules like neural layers, optimizers (Adam, RMSProp), cost functions, etc. It includes built-in models like ResNet50, InceptionV3, or MobileNet. Keras can be used on multi-GPU systems but it requires more time to configure with using both Keras and Tensorflow API.
MXNet allows using many GPUs in distributed systems. It is also easy to manage where every piece of data should be stored in the systems. This library has also built-in methods for fast derivative calculations. Every coded layer has been optimized and now MXNet is one of the fastest available CV libraries. However, it takes it more time to start modeling comparing to Keras.
Real-life Computer Vision Applications
Check our Case Studies to understand better real-world use cases of Computer Visions and Deep Learning. Contact us and arrange a consultation on how Computer Vision is implemented in real business cases.