Deep Learning Moving the Needle in Computer Vision

Over time computer vision has slowly shifted from statistical methods to the neural network methods of deep learning. Since the earliest days of artificial intelligence, researchers and computer scientists have been working toward creating machines that can see, process information, and ultimately mimic human thought processes. These efforts have led to significant developments in computer vision, and the processing of visual information is now common within today’s business environment.

With the advent of deep learning, which processes unstructured data and exhibits advantages in feature extraction, the field of computer vision is transforming and, in many ways, is replacing traditional machine learning with deep learning. Deep learning may be viewed as a set of algorithms that process large datasets imitating the human thought process. Algorithms such as

1. Deep neural networks (DNN) are considered a classic model suitable for a complex non-linear relation. The network contains a multilayer perceptron or a hidden layer between input and output, where all the layers are connected. A few applications of DNN include social media filtering, machine translation, medical diagnosis, and games.
2. Recurrent neural networks (RNN) are used when there is sequential data or time-series, where RNN performs the same function for each input while the output differs as it depends on past calculations. RNNs are applied in intelligent transportation systems, sentence evaluation, and linguistic data processing.
3. Boltzmann machines (BM) are used for classification, dimensionality reduction, and solving computational problems like search, optimization, and learning. The BM is a network that is a uniformly attached unit responsible for making decisions stochastically about whether to be off or on.
4. Restricted boltzmann machines (RBM) are used for features learning, collaborative filtering, dimensionality reduction, and classification involve two layers of visible units and hidden units, where there is no connection between the visible and the hidden units.
5. Convolutional neural networks (CNN) are widely applicable for remote sensing, computer vision, audio and text processing. These networks consist of multiple layers that are delicately connected to the input layer and each other.

Applications of deep learning for computer vision

Deep learning models have proven to achieve remarkable results for computer vision applications such as image classification, object detection, face recognition, and many more.

Object detection

Automated object detection is the process of detecting instances of semantic objects in digital images and videos by locating, drawing bounding boxes, and classifying each object. The majority of challenges related to object detection leveraging deep learning are solved with a variety of convolutional neural networks and have uses and applications across various industries.

1. Object tracking involves tracking an object, including a person, the motion of a ball during a match, vehicle tracking during traffic monitoring, etc.
2. Crowd counting, also known as people counting, helps dissect a crowd and measure any particular group of people.
3. Self-driving vehicles can safely navigate through streets only when they can detect all possible objects that can be encountered, such as pedestrians, other cars, road signs, trees, etc., to recognize and respond with an accurate course of action.
4. Anomaly detection is another valuable application of object detection. For instance, in the agriculture sector, object detection helps detect infected crops for farmers to take action early to ensure better crop yield. Another example is within the manufacturing industry, where object detection identifies defective parts consistently and more efficiently than humans.

Face recognition

Face recognition is the process of recognizing a face (identifying an individual face while ignoring other objects like trees, vehicles, buildings, etc., in the image or video) by applying deep learning techniques. Detecting a human face used to be a difficult task in computer vision since human faces are dynamic and have a high degree of variability in appearance. However, recent developments in computer vision technology have made facial recognition more practical to deploy in common business applications.

1. Face identification systems use facial recognition technology to establish authorized individuals rather than just checking a valid identification. The face recognition system also helps monitor and provide access to sensitive applications for specific individuals by recognizing their faces.
2. Security is a primary concern, and hence, many places like airports adopt facial recognition technology to alert the public safety officers when a flagged person enters the vicinity. Computer security also uses face recognition technology to authenticate authorized personnel to access machines and networks.
3. Image database systems apply face recognition to facilitate the search of images such as licensed drivers, missing people, benefit recipients, border security, etc.

Image classification

Automated image classification involves predicting and classifying the image into one or more classes and assigning a label to an entire image. A single image can be classified into countless categories or classes. The deep learning architecture for image classification usually involves multiple convolutional layers, thus making it a convolutional neural network.

1. Augmented reality combined with the gaming arena powered by deep learning use image classification technology to provide more realistic experiences to gamers.
2. Medical imagery applies image recognition technology to revolutionize diagnosis – making disease detection like cancer much more efficient, providing earlier detection, potentially saving countless lives, and significantly reducing the necessity for aggressive treatments.
3. Drones equipped with image classification capabilities provide vision-based automatic monitoring, inspection, and control of assets in remote areas.
4. The manufacturing sector inspects production lines, evaluates critical points regularly, and monitors product quality reducing defects.
5. Autonomous vehicles with image classification capabilities identify activities on the road and help initiate necessary actions. Robots help the logistics industry in locating and transporting objects from one place to another.

Deep learning in computer vision is expected to play a significant role as machine intelligence gains ground in surpassing human intelligence in the future. Further, combining this technology with robotics is expected to initiate the next wave of technological innovation.

If you would like to learn more about implementing computer vision in your business, send us your query to intellect2@intellect2.ai. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.

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