AI-driven second-opinion systems play a crucial role in decision-making, especially in medicine, where accurate predictions guide clinicians. However, quantifying uncertainty in deep learning is challenging, as current methods often rely on hard class labels, which do not reflect true prediction confidence. This often results in overconfident predictions and slow convergence to true probabilities. To address this, we suggest a new method that separates uncertainty into two types: epistemic and aleatoric. We estimate these uncertainties using hard and soft confidence labels, with experts providing confidence levels that indicate the likelihood of misclassification. We release an updated blood typing dataset consisting of 3139 images with soft labels of uncertainty annotations from six experts and hard labels collected from medical records. Proposed approach improves SotA uncertainty estimation quality by two times for blood typing (classification) and by 62% for histology (segmentation)11The code is available at: https://github.com/createcolor/confidence-aware-uncertainty..

A camera’s auto-white-balance (AWB) module operates under the assumption that there is a single dominant illumination in a captured scene. AWB methods estimate an image’s dominant illumination and use it as the target "white point" for correction. However, in natural scenes, there are often many light sources present. We performed a user study that revealed that non-dominant illuminations often produce visually pleasing white-balanced images and, in some cases, are even preferred over the dominant illumination. Motivated by this observation, we revisit AWB to predict a distribution of plausible illuminations for use in white balance. As part of this effort, we extend the Cube+ + illumination estimation dataset [12] to provide ground truth illumination distributions per image. Using this new ground truth data, we describe how to train a lightweight neural network method to predict the scene’s illumination distribution. We describe how our idea can be used with existing image formats by embedding the estimated distribution in the RAW image to enable users to generate visually plausible white-balance images.

Spectral reconstruction (recovering spectra from RGB measurements) is a vital problem of computational photography. As a matter of curiosity, modern mobile devices open a new opportunity to improve the quality of spectral reconstruction by utilizing images from several cameras at once. This leads to the idea of creating a mobile hyperspectral camera for the general public. In this paper we investigate the achievable accuracy when using several identical cameras simultaneously in combination with different spectral filters. To find optimal filters, two algorithms are proposed: one learns spectral transmittance functions simultaneously with spectral reconstruction, the other learns only spectral transmittances by information loss minimization. As a result of numerical experiments, 4 cameras and 4 filters allow us to perform spectral reconstruction two times accurately than from a single RGB image.

This paper proposes a RANSAC-based algorithm for determining the axial rotation angle of an object from a pair of its tomographic projections. An equation is derived for calculating the rotation angle using one correct keypoints correspondence of two tomographic projections. The proposed algorithm consists of the following steps: keypoints detection and matching, rotation angle estimation for each point correspondence, outliers filtering with the RANSAC algorithm, finally, calculation of the desired angle by minimizing the re-projection error from the remaining correspondences. To validate the proposed method an experimental comparison against methods based on analysis of the distribution of the angles computed from all correspondences is conducted.

We present a collection of 24 multiple object scenes recorded under 18 multiple light source illumination scenarios each. The illuminants are varying in dominant spectral colours, intensity and distance from the scene. We mainly address the realistic scenarios for evaluation of computational colour constancy algorithms, but also have aimed to make the data as general as possible for computational colour science and computer vision. Along with the images, we provide also spectral characteristics of the camera, light sources, and the objects and include pixel-by-pixel ground truth annotation of uniformly coloured object surfaces. The dataset is freely available at https://github.com/visillect/mls-dataset.

This work focuses on the Fast Hough Transform (FHT) algorithm proposed by M.L. Brady. We propose how to modify the standard FHT to calculate sums along lines within any given range of their inclination angles. We also describe a new way to visualise Hough-image based on regrouping of accumulator space around its center. Finally, we prove that using Brady parameterization transforms any line into a figure of type “angle”.

The paper presents a new fast computation scheme for linear separation in two-dimensional feature space. This scheme is based on a combination of several image processing techniques: fast Hough transform, cumulative sum computation and expression of optimized criterion as a function of additive statistics. It is shown that complexity of the scheme is O(n log n) for chosen set of criteria. Two appropriate criteria are discussed, both being a 2D extension of well-known Otsu’s criterion: standard one considering covariance trace and one considering covariance second eigenvalues. Applicability of the latter criterion for the color segmentation problem is discussed.

In this paper, we analyze properties of dyadic patterns. These pattern were proposed to approximate line segments in the fast Hough transform (FHT). Initially, these patterns only had recursive computational scheme. We provide simple closed form expression for calculating point coordinates and their deviation from corresponding ideal lines.

We describe an original low cost hardware setting for efficient testing of stereo vision algorithms. The method uses a combination of a special hardware setup and mathematical model and is easy to construct, precise in applications of our interest. For a known scene we derive its analytical representation, called virtual scene. Using a four point correspondence between the scene and virtual one we compute extrinsic camera parameters, and project virtual scene on the image plane, which is the ground truth for depth map. Another result, presented in this paper, is a new depth map quality metric. Its main purpose is to tune stereo algorithms for particular problem, e.g. obstacle avoidance.