Algorithms, computation and visual data are the three pillars of computer vision (CV). Researchers, institutions and open source communities have produced sophisticated algorithms and open-sourced code; while global tech giants' supercharged cloud platforms provide all the computational power CV researchers require. However, efficiently sourcing visual data -- particularly images with high-quality annotations -- remains a challenge. Building large datasets is a time-consuming and labor-intensive task which challenges entities with limited budgets. There are hundreds of open visual datasets out there, but searching across them and their millions of entries is not a simple task.
Purpose: The aim of this work is to demonstrate that convolutional neural networks (CNN) can be applied to extremely sparse image libraries by subdivision of the original image datasets. Methods: Image datasets from a conventional digital camera was created and scanning electron microscopy (SEM) measurements were obtained from the literature. The image datasets were subdivided and CNN models were trained on parts of the subdivided datasets. Results: The CNN models were capable of analyzing extremely sparse image datasets by utilizing the proposed method of image subdivision. It was furthermore possible to provide a direct assessment of the various regions where a given API or appearance was predominant.
The field of AI and machine learning is arguably built on the shoulders of a few hundred papers, many of which draw conclusions using data from a subset of public datasets. Large, labeled corpora have been critical to the success of AI in domains ranging from image classification to audio classification. That's because their annotations expose comprehensible patterns to machine learning algorithms, in effect telling machines what to look for in future datasets so they're able to make predictions. But while labeled data is usually equated with ground truth, datasets can -- and do -- contain errors. The processes used to construct corpora often involve some degree of automatic annotation or crowdsourcing techniques that are inherently error-prone.
Easily create your own training datasets by painting pixels. There are many more cool features. If you want to learn how to finetune your annotations or how to export your image annotations, watch our image annotation video tutorials. Easily create your own training datasets by painting pixels. There are many more cool features.
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most interesting; it is the fact that a single model can learn meaning from images and perform vision tasks, obviating the need for a pipeline of specialized and hand-crafted methods. In this post, you will discover nine interesting computer vision tasks where deep learning methods are achieving some headway.