Vedere AI

Computer Vision
Made Easy

Computer Vision is defined as a field of study that seeks to develop techniques to help computers “see” and understand digital content such as photos and videos. Computer Vision appears simple because it is easily solved by people, even very young children. It is a challenge for computer due to the limited understanding of biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world.


ABOUT

Our mission is to built easy to use computer vision engine and have the machine do it for you, instead of doing it yourself.

Founded by ex-Googlers with headquarter in Silicon Valley, we focus on building an easy-to-use Vedere AI engine for Computer Vision, Autonomous navigation, and Robotics. We believe the world will be better off when each and every one of us can spend less time on mundane chores and more time thinking about how to solve the world's next challenges.

We're a passionate team of computer scientists who think we're just at the beginning of what computer vision can do.  We eager to help make computer vision easy to use and at scale a reality.

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10+

Industry Expertise

A deep focus on Computer Vision allows for the highest level of understanding and precision.

100+

Market Use Cases

Rooted in CV technology & working with leading companies to produce the best solutions.

1,000+

Universal Access

Scalable solutions offering CV use cases to thousands of customers.

Vedere AI Engine

Semantic Segmentation

The semantic segmentation function offers image segmentation via the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze by the machine.

Aesthetic Rating

The aesthetic rating function used a computer vision model that was trained on a set of images based on histogram of ratings, such as those from photo contests. It can give a sense of the overall quality of a picture in different areas and it's not just a mean score or a simple high/low rating.

Image Tagging

The photo tagging function can be used to annotate images with multiple tags, to enhance the quality of visual representation of the trained CNN model. It is based on a large-scale multi-label image database containing over 18 million images and 11 thousands categories.

Style Transfer

The Style Transfer function is an AI algorithm specifically designed to render an output image from an input image based on the structural information such asshape and edges, and used those to style the image for its the color and texture.

Demo

CONTACT

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