1,721,071 research outputs found
Backlight and Spotlight Image Enhancement Based on Von Kries Model
No full textBacklight and spotlight pictures are images containing both dark and bright regions, caused by a non-uniform illumination. Improving the visual quality of such pictures is a challenging task since one has to increase the visibility of content and details in the dark areas without over-enhancing the bright areas. This work presents REK, a new, computationally efficient method for enhancing this kind of images. REK linearly up-scales the image channel intensities by a von Kries map and sums up the obtained image to the input one by penalizing the contribution of the up-scaled bright regions and awarding that of the up-scaled dark regions. In this way, REK improves the dark areas and preserve the bright ones. The experiments, carried out on data made freely available by the author, show that REK effectively improves the quality of backlight and spotlight images and also outperforms other state-of-the-art methods
STAR: A Segmentation-based Approximation of point-based sampling Milano Retinex for Color Image Enhancement
Full text linkMilano 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 the well known algorithm SIFT on the description and matching of pictures captured under difficult light conditions
Recognition and reconstruction of partially occluded objects
No full textAbstract - A new automatic system for the recognition and reconstruction of rescaled and/or rotated partially occluded objects is presented. The objects to recognize are described by many 2D views and each view is occluded by half-planes with different slopes. The remaining parts (linear cuts) and the whole object views are then stored in a database. To establish if a region R of an input image represents an object possibly occluded, the system generates a set of
linear cuts of R and compare them with the elements in the database. Each linear cut of R is associated to the most similar database linear cut. R is recognized as an instance of the object O if the most of the linear cuts of R are associated to a linear cut of views of O. In the case of recognition, the system selects the region cut and the correspondent view cut C (O) whose log-polar transforms match as
best as possible and uses them to reconstruct the whole shape of R. The scale factor and orientation in image plane of R with respect C (O) are determined
Object Retrieval in Digital Images Using Subgraph Isomorphism
No full textThis work presents a method for object recognition in digital images based on Graph Theory. We aim at establishing if a given object is present in an image. To do this, we describe the object and the image by a labeled topological graph. The search of the object in the image is done testing the existence of a subgraph isomorphism between the topological graph associated to the object and the topological graph associated to the image.
Our method to detect labeled subgraph isomorphisms is based on an analysis of the breadth first search trees and on the construction of an isomorphism by an extension procedure.
The method has been applied to the object retrieval in digital images; each object and image region represented by the vertices of the topological graph is described by a feature vector; the similarity between two regions is measured calculating the L1-distance between the corresponding feature vectors; the L1 - norm is used also to define
a cost for each isomorphism; by a thresholding strategy based on the distance analysis only the isomorphisms of interest can be selecte
Enhancing Backlight and Spotlight Images by the Retinex-Inspired Bilateral Filter SuPeR-B
No full textBacklight and spotlight images are pictures where the light sources generate very bright and very dark
regions. The enhancement of such images has been poorly investigated and is particularly hard because it
has to brighten the dark regions without over-enhance the bright ones. The solutions proposed till now
generally perform multiple enhancements or segment the input image in dark and bright regions and
enhance these latter with different functions. In both the cases, results are merged in a new image, that
often must be smoothed to remove artifacts along the edges. This work describes SuPeR-B, a novel
Retinex inspired image enhancer improving the quality of backligt and spotlight images without needing
for multi-scale analysis, segmentation and smoothing. According to Retinex theory, SuPeR-B re-works the
image channels separately and rescales the intensity of each pixel by a weighted average of intensities
sampled from regular sub-windows. Since the rescaling factor depends both on spatial and intensity
features, SuPeR-B acts like a bilateral filter. The experiments, carried out on public challenging data,
demonstrate that SuPeR-B effectively improves the quality of backlight and spotlight images and also
outperforms other state-of-the-art algorithms
Object Recognition in Color Images by the Self Configuring System MEMORI
No full textSystem MEMORI automatically detects and recognizes
rotated and/or rescaled versions of the objects of a database within
digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a self configuring and highly user-friendly tool
Statistics-based Estimate of Affine Transforms between Images Without Correspondences
No full textTechnical Repor
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