Robust curvature extrema detection based on new numerical derivation

International audienceExtrema of curvature are useful key points for different image analysis tasks. Indeed, polygonal approximation or arc decomposition methods used often these points to initialize or to improve their algorithms. Several shape-based image retrieval methods focus also their descriptors on key points. This paper is focused on the detection of extrema of curvature points for a raster-to-vector-conversion framework. We propose an original adaptation of an approach used into nonlinear control for fault-diagnosis and fault-tolerant control based on algebraic derivation and which is robust to noise. The experimental results are promising and show the robustness of the approach when the contours are bathed into a high level speckled noise

Spatially-Variant Directional Mathematical Morphology Operators Based on a Diffused Average Squared Gradient Field

International audienceThis paper proposes an approach for mathematical morphology operators whose structuring element can locally adapt its orientation across the pixels of the image. The orientation at each pixel is extracted by means of a diffusion process of the average squared gradient field. The resulting vector field, the average squared gradient vector flow, extends the orientation information from the edges of the objects to the homogeneous areas of the image. The provided orientation field is then used to perform a spatially variant filtering with a linear structuring element. Results of erosion, dilation, opening and closing spatially-variant on binary images prove the validity of this theoretical sound and novel approach

Critique du rapport signal à bruit en communications numériques

International audienceThe signal to noise ratio, which plays such an important rôle in information theory, is shown to become pointless for digital communications where the demodulation is achieved via new fast estimation techniques. Operational calculus, differential algebra, noncommutative algebra and nonstandard analysis are the main mathematical tools.On démontre que le rapport signal à bruit, si important en théorie de l’information, devient sans objet pour des communications numériques où la démodulation s’effectue selon des techniques nouvelles d’estimation rapide. Calcul opérationnel, algèbre différentielle, algèbre non commutative et analyse non standard sont les principaux outils mathématiques

Detection of Optic Disc and Hard Exudates Using TWIN PLANE Gradient Windowing Technique

Detection of Optic disc (OD) in a fundus image is a foremost important step in the process of screening the diabetic retinopathy [1,4,18]. Hard Exudates detection algorithms usually find lot of false positives since the intensity and color distribution of OD will much resemble that of a Hard Exudates region [15,16]. So, most of the Exudates detection algorithms will miss classify the pixels at the OD region as Hard Exudates [2]. In our previous works, we used Genetic Algorithm(GA)[23,24] to find the OD location and size and reduced overall time, even doing the search on the entire problem space and also removed false hard exudates. In this work we are improving Hard Exudates detection accuracy using gradient index mapping technique applied on two similar planes of the RGB. The new algorithm is termed as TWINGRAB use the the two planes of the RGB after proper gradient projection. The database used for this preprocessing is DIARETDB1[10] for evaluation and comparision with the existing methods

BATUD: Blind Atmospheric TUrbulence Deconvolution

A new blind image deconvolution technique is developed for atmospheric turbulence deblurring. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies the impact of the atmospheric turbulence on the optical resolution of images. While the original expression of the Fried kernel can seem cumbersome at first sight, we show that it can be reparameterized in a much simpler form. This simple expression allows us to efficiently embed this kernel in the proposed Blind Atmospheric TUrbulence Deconvolution (BATUD) algorithm. BATUD is an iterative algorithm that alternately performs deconvolution and estimates the Fried kernel by jointly relying on a Gaussian Mixture Model prior of natural image patches and controlling for the square Euclidean norm of the Fried kernel. Numerical experiments show that our proposed blind deconvolution algorithm behaves well in different simulated turbulence scenarios, as well as on real images. Not only BATUD outperforms state-of-the-art approaches used in atmospheric turbulence deconvolution in terms of image quality metrics, but is also faster

MULTIMEDIA KNOWLEDGE-BASED CONTENT ANALYSIS OVER DISTRIBUTED ARCHITECTURE

International audienceIn this paper, we review the recently finished CARETAKER project outcomes from a system point of view. The IST FP6-027231 CARETAKER project aimed at studying, developing and assessing multimedia knowledge-based content analysis, knowledge extraction components, and metadata management sub-systems in the context of automated situation awareness and decision support. More precisely, CARETAKER focused on the extraction of a structured knowledge from large multimedia collections recorded over surveillance networks of camera and microphones deployed in real sites. Indeed, the produced audio-visual streams, in addition to security and safety issues, represent a useful source of information when stored and automatically analysed, for instance in urban planning or resource optimisation. In this paper, we overview the communication architecture developed for the project, and detail the different innovative content analysis components developed within the test-beds. We also highlight the different technical concerns encountered for each individual brick, which are common issues in distributed media applications

ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations

tmospheric turbulence presents a significant challenge in long-range imaging. Current restoration algorithms often struggle with temporal inconsistency, as well as limited generalization ability across varying turbulence levels and scene content different than the training data. To tackle these issues, we introduce a self-supervised method, Consistent Video Restoration through Turbulence (ConVRT) a test-time optimization method featuring a neural video representation designed to enhance temporal consistency in restoration. A key innovation of ConVRT is the integration of a pretrained vision-language model (CLIP) for semantic-oriented supervision, which steers the restoration towards sharp, photorealistic images in the CLIP latent space. We further develop a principled selection strategy of text prompts, based on their statistical correlation with a perceptual metric. ConVRT's test-time optimization allows it to adapt to a wide range of real-world turbulence conditions, effectively leveraging the insights gained from pre-trained models on simulated data. ConVRT offers a comprehensive and effective solution for mitigating real-world turbulence in dynamic videos.Comment: https://convrt-2024.github.io

Optimal number of pressure sensors for real-time monitoring of distribution networks by using the hypervolume indicator

This article proposes a novel methodology to determine the optimal number of pressure sensors for the real-time monitoring of water distribution networks based on a quality hypervolume indicator. The proposed methodology solves the optimization problem for different numbers of pressure sensors, assesses the gain of installing each set of sensors by means of the hypervolume indicator and determines the optimal number of sensors by the variation of the hypervolume indicator. The methodology was applied to a real case study. Several robustness analyses were carried out. The results demonstrate that the methodology is hardly influenced by the method parameters and that a reasonable estimation of the optimal number of sensors can be easily achieved.info:eu-repo/semantics/publishedVersio