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. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures.

We present CORVO (Computing Organoids VOlume in medical images), a tool for calculating the volume of complex time-changing 3D structures from medical videos. In order to identify anisotropies in volume variation over time, CORVO is equipped with a module implementing an advanced regression-based statistical analysis. We tested CORVO for the analysis of the variation of rectal organoids volume, whose anisotropic or missing expansion indicates a pathological state and a non-response to a pharmacological treatment, respectively.

The performance of an image enhancer is usually evaluated either perceptually or functionally. The perceptual evaluation is carried out from a human perspective, i.e. by considering features related to human perception of image content and details. The functional evaluation is made instead from a machine perspective, i.e. by judging the enhancer effects with a specific machine application. his work proposes a comprehensive, empirical evaluation accounting for both perceptual and functional aspects. Precisely, 13 enhancers lowering undesired illumination effects are considered within the keypoint based image description and matching task, which is relevant to many computer vision fields. Each enhancer is first evaluated perceptually, then it is employed as a pre‐processing step of the popular algorithms SIFT and ORB and judged by measuring how its use influences the performance of these algorithms. his study, conducted on a freely available data set, shows that the enhancement generally improves the perceptual features of the input image as well as the SIFT and ORB performance. More importantly, it reveals the existence of a correlation among some of their perceptual and functional measures. In this way, this work contributes to promote a more aware use of enhancement techniques within the mage description and matching task.

This paper presents a 64 × 64 pixels vision sensor embedding pixel-wise computation of the Local Binary Pattern (LBP) code, which is an oriented, binary, vector contrast that is widely used for texture description and retrieval. For each pixel, the sensor estimates the LBP code over four neighbors in a 3×3 pixel kernel. The image processing is performed inside each pixel during the integration time over a dynamic range up to 98dB, thanks to a pixel-level auto-exposure control. The contrast detection relies on the estimation of the time difference between two pixels thresholded against two reference voltages. The four binary signed contrast vectors are delivered to the output, coded into 4-bit/pixel. The 0.35μm CMOS sensor features a power consumption of 35μW at 3.3V and 15fps.

Milano‐Retinex is a family of Retinex‐inspired spatial colour algorithms mainly developed for colour image enhancement. According to the Retinex theory, a Milano‐Retinex algorithm takes as input an RGB image and processes the colour intensities of each pixel (i.e. the target) based on the spatial distribution of the colour intensities sampled in a surrounding region. The output is an RGB image, with locally adjusted colours and contrast. In Milano‐Retinex family, different ways of spatial sampling are implemented. This study reviews and compares these sampling characteristics within a group of Milano‐Retinex algorithms developed in the last decade, from Random Spray Retinex (2007) to the gradient‐based colour sampling schemes GREAT and GRASS (2017). Instead of exploring the target neighbourhood by random paths as the original Retinex algorithm does, these methods consider sets of pixels, randomly or deterministically defined, including all the image pixels or a part of them, such as random sprays or image edges. They replace the ratio‐reset‐threshold‐product‐average mechanism of the original Retinex with equations re‐working maximal intensities over the sampled sets. The performance of these approaches is compared with more than 200 images of indoor and outdoor scenes, captured by commercial cameras under several different conditions.

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. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions.

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. The recent algorithm energy-driven termite retinex (ETR), as well as its predecessor termite retinex, has introduced a new path-based image aware sampling scheme, where the paths depend on local visual properties of the input image. Precisely, the ETR paths transit over pixels with high gradient magnitude that have been proved to be important for the formation of color sensation. Such a sampling method enables the visit of image portions effectively relevant to the estimation of the color sensation, while it reduces the analysis of pixels with less essential and/or redundant data, i.e., the flat image regions. While the ETR sampling scheme is very efficacious in detecting image pixels salient for the color sensation, its computational complexity can be a limit. In this paper, we present a novel Gradient-based RAndom Sampling Scheme that inherits from ETR the image aware sampling principles, but has a lower computational complexity, while similar performance. Moreover, the new sampling scheme can be interpreted both as a path-based scanning and a 2D sampling.

This paper presents a new approach for rotation, rescaling and occlusion invariant retrieval of the objects of a given database D. The objects are represented by means of many 2D views and each of them is occluded by several half-planes. The remaining visible parts (linear cuts) as well as the whole views are stored in a new database D ’ and described by low-level features. Given a portion R of an image, the retrieval of the most similar object is done by generating some linear cuts of R, and by comparing their descriptors with those of the elements of D’. Some heuristic rules regarding visual similarity and geometric properties of the objects in the database drive this process. In the case R is recognized as an object partially occluded, a strategy for the reconstruction of the whole shape of R is also presented. The tests carried out on synthetic and real-world datasets showed good performances both in recognition and in reconstruction accuracy. 1

The MEMORI system automatically detects and recognizes rescaled and/or rotated versions of objects from a database within digital color images with cluttered background. This paper describes the system's architecture and the methods implemented within, and its use as a tool for the automatic annotation and browsing of digital images of web furniture catalogs.