30,860 research outputs found
A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing
The goal of edge-histogram specification is to find an image whose edge image
has a histogram that matches a given edge-histogram as much as possible.
Mignotte has proposed a non-convex model for the problem [M. Mignotte. An
energy-based model for the image edge-histogram specification problem. IEEE
Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge
magnitudes of an input image are first modified by histogram specification to
match the given edge-histogram. Then, a non-convex model is minimized to find
an output image whose edge-histogram matches the modified edge-histogram. The
non-convexity of the model hinders the computations and the inclusion of useful
constraints such as the dynamic range constraint. In this paper, instead of
considering edge magnitudes, we directly consider the image gradients and
propose a convex model based on them. Furthermore, we include additional
constraints in our model based on different applications. The convexity of our
model allows us to compute the output image efficiently using either
Alternating Direction Method of Multipliers or Fast Iterative
Shrinkage-Thresholding Algorithm. We consider several applications in
edge-preserving smoothing including image abstraction, edge extraction, details
exaggeration, and documents scan-through removal. Numerical results are given
to illustrate that our method successfully produces decent results efficiently
Adaptive Filters for 2-D and 3-D Digital Images Processing
Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideálními obrazy obsahujícími aditivní a impulzní šum. Závěr práce se věnuje prostorové rekonstrukci objektů na základě jejich optických řezů. Veškeré postupy a algoritmy jsou i prakticky zpracovány v softwaru, který byl vyvinut v rámci této práce.The thesis is concerned with filters for visualization of high dynamic range images. In the theoretical part, the principle of confocal microscopy is described and the term digital image is defined in a mathematically correct way. Both frequency approach (using 2-D and 3-D discrete Fourier transform and frequency filters) and digital geometry approach (using adaptive histogram equalization with adaptive neighbourhood) are chosen for the processing of images. Necessary adjustments when working with non-ideal images containing additive and impulse noise are described as well. The last part of the thesis is interested in 3-D reconstruction from optical cuts of an object. All the procedures and algorithms are also implemented in the software developed as a part of this thesis.
Image Enhancement with Statistical Estimation
Contrast enhancement is an important area of research for the image analysis.
Over the decade, the researcher worked on this domain to develop an efficient
and adequate algorithm. The proposed method will enhance the contrast of image
using Binarization method with the help of Maximum Likelihood Estimation (MLE).
The paper aims to enhance the image contrast of bimodal and multi-modal images.
The proposed methodology use to collect mathematical information retrieves from
the image. In this paper, we are using binarization method that generates the
desired histogram by separating image nodes. It generates the enhanced image
using histogram specification with binarization method. The proposed method has
showed an improvement in the image contrast enhancement compare with the other
image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print
Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images
Images of the human retina vary considerably in their appearance depending on the skin pigmentation (amount of melanin) of the subject. Some form of normalisation of colour in retinal images is required for automated analysis of images if good sensitivity and specificity at detecting lesions is to be achieved in populations involving diverse races. Here we describe an approach to colour normalisation by shade-correction intra-image and histogram normalisation inter-image. The colour normalisation is assessed by its effect on the automated detection of microaneurysms in retinal images. It is shown that the Na¨ıve Bayes classifier used in microaneurysm detection benefits from the use of features measured over colour normalised images
Obstacle Avoidance and Proscriptive Bayesian Programming
Unexpected events and not modeled properties of the robot environment are some of
the challenges presented by situated robotics research field. Collision avoidance is a basic security
requirement and this paper proposes a probabilistic approach called Bayesian Programming, which
aims to deal with the uncertainty, imprecision and incompleteness of the information handled to
solve the obstacle avoidance problem. Some examples illustrate the process of embodying the
programmer preliminary knowledge into a Bayesian program and experimental results of these
examples implementation in an electrical vehicle are described and commented. A video illustration
of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac
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