Image analysis

Visual insights and automated inspection

Visual insights on your customers, industry, and brand

Billions of photos are shared publicly online by consumers, capturing every thinkable consumer experience from a variety of angles. New, AI-powered methods enable us to organize and map these images to find useful patterns and view the world from the eyes of consumers. A landscape of visual themes emerge bottom-up through the use of cutting edge deep learning techniques.

 

Use cases include:

  • Consumer insights: View a phenomenon or activity from the eyes of consumers and spot opportunities for strategy, innovation, and marketing
  • Place insights: Understand how people view and describe places to find gaps and opportunities for destination development and place making.
  • Brand scans: Map how products and brands are used and presented by their users and feed into product development and branding.
  • Trend spotting: Explore what trendsetters think is cool from global hotspots without having to go there.

 

Dcipher’s image analysis capabilities are also used in production. Organizations use them to increase the quality, consistency, and efficiency of workflows by automating manual inspection and classification. Use cases range from decreasing the variability of intra-batch log dimensions in sawmills to classification of recyclable goods in recycling lines.

Image analysis is offered by Dcipher Lab. Whether you are interested in training machine learning models for image classification or image landscaping for bottom-up exploration of visual themes in large quantities of images, our team of machine learning engineers will develop a solution tailored for your needs. Contact us for more information.

How organizations leverage Dcipher for image analysis

Atrium Ljungberg, a real estate developer, wanted to enhance their placemaking capabilities by mapping what makes a good place. The Dcipher Lab team used hundreds of thousands of images posted by residents of six places and applied image landscaping to map the visual themes that were conveyed in relation to each place. The result was a map of needs and preferences relating the lived environment, as viewed by the residents of each place. The insights were used as input into Atrium Ljungberg’s innovation and destination development processes.

Example: Mapping of interests from images

Identifying different types of interests