The AI Revolution: AI Image Recognition & Beyond

The AI Revolution: AI Image Recognition & Beyond

Understanding Image Recognition and Its Uses

image recognition in artificial intelligence

It is often the case that in (video) images only a certain zone is relevant to carry out an image recognition analysis. In the example used here, this was a particular zone where pedestrians had to be detected. In quality control or inspection applications in production environments, this is often a zone located on the path of a product, more specifically a certain part of the conveyor belt. A user-friendly cropping function was therefore built in to select certain zones. In many administrative processes, there large efficiency gains to be made by automating the processing of orders, purchase orders, mails and forms.

  • The control over what content appears on social media channels is somewhere that businesses are exposed to potentially brand-damaging and, in some cases, illegal content.
  • AI-based image recognition is the essential computer vision technology that can be both the building block of a bigger project (e.g., when paired with object tracking or instant segmentation) or a stand-alone task.
  • Recent research has also demonstrated that while RT-PCR is negative, chest CT can reveal lung abnormalities [12, 13].

For example, with the AI image recognition algorithm developed by the online retailer Boohoo, you can snap a photo of an object you like and then find a similar object on their site. This relieves the customers of the pain of looking through the myriads of options to find the thing that they want. After designing your network architectures ready and carefully labeling your data, you can train the AI image recognition algorithm. This step is full of pitfalls that you can read about in our article on AI project stages.

Medical Applications

Through complex architectures, it is possible to predict objects, face in an image with 95% accuracy surpassing the human capabilities, which is 94%. However, even with its outstanding capabilities, there are certain limitations in its utilization. Datasets up to billion parameters require high computation load, memory usage, and high processing power. As we can see, this model did a decent job and predicted all images correctly except the one with a horse. This is because the size of images is quite big and to get decent results, the model has to be trained for at least 100 epochs. But due to the large size of the dataset and images, I could only train it for 20 epochs ( took 4 hours on Colab ).

image recognition in artificial intelligence

Apart from image recognition, computer vision also consists of object recognition, image reconstruction, event detection, and video tracking. Building a diverse and comprehensive training dataset involves manually labeling images with appropriate class labels. This process allows the model to learn the unique features and characteristics of each class, enabling accurate recognition and classification.

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When networks got too deep, training could become unstable and break down completely. With social media being dominated by visual content, it isn’t that hard to imagine that image recognition technology has multiple applications in this area. Bag of Features models like Scale Invariant Feature Transformation (SIFT) does pixel-by-pixel matching between a sample image and its reference image.

image recognition in artificial intelligence

Social media networks have seen a significant rise in the number of users, and are one of the major sources of image data generation. These images can be used to understand their target audience and their preferences. We have seen shopping complexes, movie theatres, and automotive industries commonly using barcode scanner-based machines to smoothen the experience and automate processes. It is used in car damage assessment by vehicle insurance companies, product damage inspection software by e-commerce, and also machinery breakdown prediction using asset images etc. Once the dataset is ready, there are several things to be done to maximize its efficiency for model training.

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The image learning method, segmentation and applications in lung diseases are the research hotspots of AI in medical imaging with high clinical application potential [30]. Deep learning has been applied to detect and differentiate between bacterial and viral pneumonia on pediatric chest radiographs [31]. In this study, we proposed to build a severe COVID-19 early warning model based on the deep learning network of Mask R-CNN and chest CT images and patient clinical characteristics. We hope to make early predictions of severe COVID-19 patients by this model. A face recognition algorithm widely used in the era before convolutional neural networks, it works by scanning faces and extracting features that are then passed through a boosting classifier.

  • One challenge is the vast amount of data required for training accurate models.
  • Machine learning is a subset of AI that strives to complete certain tasks by predictions based on inputs and algorithms.
  • This is because our brains have been trained unconsciously with the same set of images that has resulted in the development of capabilities to differentiate between things effortlessly.
  • Computer vision (and, by extension, image recognition) is the go-to AI technology of our decade.
  • While animal and human brains recognize objects with ease, computers have difficulty with this task.

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