When you see a flower and instantly recognize it without any conscious effort, you have millions of years of evolutionary context and an incredibly powerful visual cortex working in your favor. When a machine sees a flower, it does not see a flower. When an algorithm is exposed to an image, it receives a large array of integer values representing intensities across the color spectrum. It appears that mimicking human vision is way easier for a computer than making sense of it.
From pixels to insights
Computer vision is one of the main pillars of AI-based process augmentation. Deep neural networks powered by big data and superior computational power have brought image and video analytics into the industrial purview. But how does a machine perceive visual information?
Image recognition with convolutional neural networks
A convolutional neural network breaks an image down into smaller groups of pixels. Each of these groups is called a filter. So, each filter is a matrix of pixels. The network performs a series of calculations on these pixels, assigning them integer values based on their intensity. Then these pixels are compared to pixels in a specific pattern that the network is looking for. Initially, the CNN or the convolutional neural network can determine high-level patterns like the rough edges and the curves.
With each convolution, the network can detect more specific details, ultimately recognizing the object.
How does computer vision know what to look for?
The deep neural network is trained with very large amounts of labeled data. It takes thousands of images of the same object from different angles, in different lighting conditions, and with every possible variation. When the CNN starts working, all the filter values are randomized. Hence, the initial results usually make little sense. Each time the CNN makes a prediction against the labeled data, it uses an error function to understand how close its prediction was to the image’s actual label. Based on that, it updates the filter values and starts again. The CNN achieves more accuracy with each iteration of the process.
Successful image recognition goes a long way in face recognition, biometrics, quality control in manufacturing units, invoicing, and augmented medical diagnosis, just to name a few. Imagine a CNN that has been trained with millions of X-Ray images of a particular body part. It will augment the medical professional’s knowledge in terms of recognizing an anomaly. The same goes for manufacturing units, where image analysis can be used for quality assessment of the outbound products.
The video hurdles
A video is a series of image frames. Hence, no stable pixels to focus on for the CNNs. Moreover, there are contextual shifts between the two frames of a video. Now, while a convolutional neural network can analyze spatial data, it cannot see through temporal data. This makes video analysis a lot more challenging. Then again, when it comes to computer vision applications, video analysis is the centerpiece, be it for autonomous cars or AI-powered surveillance.
Recurrent Neural Networks (RNN) to the rescue
Recurrent Neural Networks can retain information about the data that has already been processed and use that information while making further decisions. Hence, they can make sense out of the contextual shifts.
Image and video analytics are powering some incredible projects around the world. From helping autonomous vehicles sense the shape and speed of any object on its track to assisting critical surgeries, computer vision applications are spread far and wide. In fact, we encounter computer vision whenever Facebook runs facial recognition, or the camera on our smartphones detects a smiling face.
Capabilities like computer vision, image and video analysis, and object identification provide a lot of girth to an enterprise’s security efforts. However, these technologies are complex, and their success depends on exposure to humongous amounts of labeled training data. It is not something you can develop overnight by putting a rag-tag in-house team together. It is always a good idea to consult the experts and get some help.
Algoscale a leading data consulting company has helped many enterprises solve computer vision challenges in real-world scenarios. We have many success stories in a variety of industries, from healthcare to hospitality.
To learn more about Computer Vision Development Services, contact us at askus@algoscale.com.
Also Read: 5 Intriguing Applications of Computer Vision
