# K-Origins: Better Colour Quantification for Neural Networks Source: [K-Origins: Better Colour Quantification for Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:15:34.723Z Authors: Lewis Mason, Mark Martinez Published: 2024-09-03T20:28:30.000Z ## Abstract K-Origins is a neural network layer designed to improve image-based network performances when learning colour, or intensities, is beneficial. Over 250 encoder-decoder convolutional networks are trained and tested on 16-bit synthetic data, demonstrating that K-Origins improves semantic segmentation accuracy in two scenarios: object detection with low signal-to-noise ratios, and segmenting multiple objects that are identical in shape but vary in colour. K-Origins generates output features from the input features, $\\textbf{X}$, by the equation $\\textbf{Y}\_k = \\textbf{X}-\\textbf{J}\\cdot w\_k$ for each trainable parameter $w\_k$, where $\\textbf{J}$ is a matrix of ones. Additionally, networks with varying receptive fields were trained to determine optimal network depths based on the dimensions of target classes, suggesting that receptive field lengths should exceed object sizes. By ensuring a sufficient receptive field length and incorporating K-Origins, we can achieve better semantic network performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()