conflu3nce develops patented image-based technologies to advance early detection and predictive insight across industries. From spotting critical anomalies in manufacturing, to detecting emerging safety risks in autonomous systems, to identifying subtle signals in healthcare and scientific research, our solutions are designed to extract meaningful patterns from visual data, supporting human expertise and artificial intelligence in complex environments.
Image-Based Technologies
From early disease detection to searching for the proverbial "needle in the haystack" to developing personalized stimuli to support cognitive health, conflu3nce's tools integrate a top-down and bottom-up approach to build image intelligence and improve ROI visualization, anomaly detection, and overall image and scene analysis for both humans and machines.
The cornerstone of the company's product development initiative is the patented Stitch n' Peel (SnP) method. SnP logically juxtaposes non-contiguous image sections to identify long-term dependencies within an image. The approach enhances visual attention, reduces pixel volume, and deconstructs image complexity - - a technology Gestalt that balances ambiguity and clarity to help bridge the gap between human cognition, perception and computer vision.
SnP leverages an image's contiguity attributes; its embedded visual cues. These attributes can be algorithmically analyzed, and symmetries and differences across the image identified. The integration of these Gestalt-based visual cues can then be used to anchor local and global spatial and contextual relationships between parts of an image and help improve scene understanding.
SnP technology can be used to: 1) identify salient features;
2) optimize image search;
3) refine lesion and anomaly analysis by combining asymmetry and edge-to-edge analyses; and,
4) enhance self-attention of long-range dependencies across complex image scenes for both humans and machines.
As a Vision Transformer, SnP methods combine positional encoding with contiguity-based spatial awareness in computer vision - a process similar to how embedded Gestalt visual cues informs our perception and cognition of visual scenes and allowing us to assess, anticipate, infer, and predict local and global (near and far) interactions with the world around us.
