Image recognition algorithms are nothing like our eyes, and here is blobby static proof. These images fooled an algorithm into seeing a gorilla, bikini, stopwatch and more—yet they obviously ...

You can apply considerable efforts to speed up image recognition algorithms, but algorithmic ... It also supports scripts in R and Python, and RMarkdown reports. Kaggle can provide you with ...

But neural network-based image recognition algorithms are still far from perfect, and according to a pair of recent papers these algorithms can be tricked pretty easily. The first group of ...

A new image recognition algorithm uses the way humans see things for inspiration. The context: When humans look at a new image of something, we identify what it is based on a collection of ...

found that they could fool even sophisticated image recognition algorithms into confusing objects simply by slightly altering their surface texture. These weren’t minor mix-ups, either.

Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a ...

Image recognition ... learning algorithms to recognize objects, people, text, places, and actions within those images. This field is a subset of computer vision, and image recognition is often ...

Here is an interesting conundrum for Google: it has created an algorithm ... from these images. The standard approach is to separate out the localization, segmentation and recognition steps ...

Stanford researchers have developed new software that can automatically evaluate microscopic images of breast cancer ... Beck developed a machine-learning algorithm and trained it using existing ...