HDR vision sensor with neuro-memristive skin detection for edge computing.

Human skin classification is an essential task for several machine vision applications such as human-machine interfaces, people/object tracking, and classification. In this paper, we describe a hybrid CMOS/memristor vision sensor architecture embedding skin detection over a wide dynamic range. In-sensor RGB to -chromaticity color-space conversion is executed on-the-fly through a pixel-level automatic exposure time control.

A dataset for illuminant- and device- invariant colour barcode decoding with cameras.

Barcodes are visual representations of data widely used in commerce and administration to compactly codify information about objects, services, and people. Specifically, a barcode is an image composed of parallel lines, with different widths, spacing and sizes. Generally, the lines are dark (usually black) on a bright background (usually white) or vice-versa.

Graph embedding and geometric deep learning relevance to network biology and structural chemistry.

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g.

Mathematical insights into the original Retinex algorithm for image enhancement.

The Retinex theory, originally developed by Land and McCann as a computation model of the human color sensation, has become, with time, a pillar of digital image enhancement. In this area, the Retinex algorithm is widely used to improve the quality of any input image by increasing the visibility of its content and details, enhancing its colorfulness, and weakening, or even removing, some undesired effects of the illumination. The algorithm was originally described by its creators in terms of a sequence of image processing operations and was not fully formalized mathematically.

Performance comparison of image enhancers with and without deep learning.

Image enhancement is a computational procedure to improve visibility of details and content of an input image. Several image enhancement algorithms have been developed thus far, from traditional methods that process a single image based on physical models of image acquisition and formation to recent deep learning techniques, where enhancement models are learned from data. Here, we empirically compare a set of traditional and deep learning enhancers, which we selected as representing different methodologies for the improvement of poorly illuminated images.

An Image Contrast Measure Based on Retinex Principles.

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory.

Generalized equation for real-world image enhancement by Milano Retinex family.

Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel by the so-called local reference white, i.e.

SuPeR: Milano Retinex implementation exploiting a regular image grid.

A spatial color algorithm grounded on the Retinex theory is known as a Milano Retinex. This type of algorithm performs image enhancement by processing spatial and color cues in the neighborhood of each image pixel. Because this local, pixel-wise analysis is time consuming, optimization techniques are needed to expand the use of Milano Retinexes to applications that require fast or even real-time image processing.

STAR: A Segmentation-Based Approximation of Point-Based Sampling Milano Retinex for Color Image Enhancement.

Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation.

GREAT: a gradient-based color-sampling scheme for Retinex.

Modeling the local color spatial distribution is a crucial step for the algorithms of the Milano Retinex family. Here we present GREAT, a novel, noise-free Milano Retinex implementation based on an image-aware spatial color sampling. For each channel of a color input image, GREAT computes a 2D set of edges whose magnitude exceeds a pre-defined threshold.

GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex.

Retinex is an early and famous theory attempting to estimate the human color sensation derived from an observed scene. When applied to a digital image, the original implementation of retinex estimates the color sensation by modifying the pixels channel intensities with respect to a local reference white, selected from a set of random paths. The spatial search of the local reference white influences the final estimation.

A population-based approach to point-sampling spatial color algorithms.

Inspired by the behavior of the human visual system, spatial color algorithms perform image enhancement by correcting the pixel channel lightness based on the spatial distribution of the intensities in the surrounding area. The two visual contrast enhancement algorithms RSR and STRESS belong to this family of models: they rescale the input based on local reference values, which are determined by exploring the image by means of random point samples, called sprays. Due to the use of sampling, they may yield a noisy output.

Always-on low-power optical system for skin-based touchless machine control.

Embedded vision systems are smart energy-efficient devices that capture and process a visual signal in order to extract high-level information about the surrounding observed world. Thanks to these capabilities, embedded vision systems attract more and more interest from research and industry. In this work, we present a novel low-power optical embedded system tailored to detect the human skin under various illuminant conditions.

Energy-driven path search for Termite Retinex.

The human color sensation depends on the local and global spatial arrangements of the colors in the scene. Emulating this dependence requires the exploration of the image in search of a white reference. The algorithm Termite Retinex explores the image by a set of paths resembling traces of a swarm of termites.

Tuning the locality of filtering with a spatially weighted implementation of random spray Retinex.

The human color sensation depends on the spatial distribution of the colors in the viewed scene. This principle is at the basis of the random spray Retinex (RSR) algorithm. In this work, we modify RSR by integrating its approach with a method to weight and tune the locality of spatial image information.