Region Growing Structuring Elements and New Operators Based on Their Shape

International audienceThis paper proposes new adaptive structuring elements in the framework of mathematical morphology. These structuring elements (SEs) have a fixed size but they adapt their shape to the image content by choosing, recursively, similar pixels in gray-scale, with regard to the seed pixel. These new SEs are called region growing structuring elements (REGSEs). Then, we introduce an original method to obtain some features by analyzing the shape of each REGSE. We get a powerful set of operators, which is able to enhance efficiently thin structures in an image. We illustrate the performance of the proposed filters with an application: the detection of cracks in the framework of non-destructive testing. We compare these methods with others, including morphological amoebas and general adaptive neighborhood structuring elements and we see that these operators, based on REGSE, yield the best detection for our application

Efficient geodesic attribute thinnings based on the barycentric diameter

International audienceAn attribute opening is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a - more general - attribute thinning. In this paper, we propose efficient grey scale thinnings based on geodesic attributes. Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the barycentric diameter to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up. We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments

One-Dimensional Openings, Granulometries and Component Trees in O(1) Per Pixel

International audienceWe introduce a new, efficient and adaptable algorithm to compute openings, granulometries and the component tree for one-dimensional (1-D) signals. The algorithm requires only one scan of the signal, runs in place in per pixel, and supports any scalar data precision (integer or floating-point data). The algorithm is applied to two-dimensional images along straight lines, in arbitrary orientations. Oriented size distributions can thus be efficiently computed, and textures characterized. Extensive benchmarks are reported. They show that the proposed algorithm allows computing 1-D openings faster than existing algorithms for data precisions higher than 8 bits, and remains competitive with respect to the algorithm proposed by Van Droogenbroeck when dealing with 8-bit images. When computing granulometries, the new algorithm runs faster than any other method of the state of the art. Moreover, it allows efficient computation of 1-D component trees

Image Filtering Using Morphological Amoebas

International audienceThis article presents the use of anisotropic dynamic structuring elements, or amoebas, in order to build content-aware noise reduction filters. The amoeba is the ball defined by a special geodesic distance computed for each pixel, and can be used as a kernel for many kinds of filters and morphological operators. 1. Introduction Noise is possibly the most annoying problem in the field of image processing. There are two ways to work around it: either design particularly robust algorithms that can work in noisy environments, or try to eliminate the noise in a first step while losing as little relevant information as possible and consequently use a normally robust algorithm. There are of course many algorithms that aim at reducing the amount of noise in images. In mathematical morphology filters can be, broadly-speaking, divided into two groups: 1 alternate sequential filters based on morphological openings and clos-ings, that are quite effective but also remove thin elements such as canals or peninsulas. Even worse, they can displace the contours and thus create additional problems in a segmentation application

Restoration of variable density film soundtracks

Full text available at http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569192297.pdfInternational audienceThe restoration of motion picture films has been an active research field for many years. The restoration of the soundtrack however has mainly been performed at the audio domain in spite of the fast that it is recorded as a continuous image on the film stock. In this paper, we propose a new restoration method for variable density soundtracks. The method first detects and corrects accurately the azimuth deviation. A robust thresholding technique based on the minimization of the total variation is then performed to remove the remaining faults. Restoration results are very promising and testify to the efficiency of our method

Parsimonious Path Openings and Closings

International audiencePath openings and closings are morphological tools used to preserve long, thin and tortuous structures in gray level images. They explore all paths from a defined class, and filter them with a length criterion. However, most paths are redundant, making the process generally slow. Parsimonious path openings and closings are introduced in this paper to solve this problem. These operators only consider a subset of the paths considered by classical path openings, thus achieving a substantial speed-up, while obtaining similar results. Moreover, a recently introduced one dimensional (1-D) opening algorithm is applied along each selected path. Its complexity is linear with respect to the number of pixels, independent of the size of the opening. Furthermore, it is fast for any input data accuracy (integer or floating point) and works in stream. Parsimonious path openings are also extended to incomplete paths, i.e. paths containing gaps. Noise-corrupted paths can thus be processed with the same approach and complexity. These parsimonious operators achieve a several orders of magnitude speed-up. Examples are shown for incomplete path openings, where computing times are brought from minutes to tens of milliseconds, while obtaining similar results

Application of the morphological ultimate opening to the detection of microaneurysms on eye fundus images from a clinical database

International audienceDiabetic Retinopathy (DR) is a severe disease which can cause blindness. OPHDIAT is a telemedicine network for DR mass screening, which has gathered thousands of clinical high resolution color eye fundus images. The TELEOPHTA project has been launched in order to develop a computer aided diagnosis system of DR, which aims at performing a preliminary analysis of the OPHDIAT images in order to filter most images corresponding to healthy eyes. Microaneurysms (MAs) are likely to be the lesions present at the earliest stage of the disease. In this paper, a new method of MAs detection, using the recently proposed ultimate opening, is presented. The proposed method does not use any supervised classification, while provides a competitive and efficient way to detect MAs, especially for our clinical database. Further improvements may be brought through the accurate detection of the retinal elements and other retinal diseases, or through the estimation of the image quality

NEW GENERAL FEATURES BASED ON SUPERPIXELS FOR IMAGE SEGMENTATION LEARNING

International audienceSegmenting an image is usually one of the major and most challenging steps in the pipeline of biomedical image analysis. One classical and promising approach is to consider seg-mentation as a classification task, where the aim is to assign to each pixel the label of the objects it belongs to. Pixels are therefore described by a vector of features, where each feature is calculated on the pixel itself or, more frequently, on a sliding window centered on the pixel. In this work, we propose to replace the sliding window by superpixels, i.e. regions which adapt to the image content. We call the resulting features SAF (Superpixel Adaptive Feature). Their contribution is highlighted on a biomedical database of melanocytes images. Qualitative and quantitative analyses show that they are better suited for segmentation purposes than the sliding window approach

Detection And Correction Of Under-/Overexposed Optical Soundtracks By Coupling Image And Audio Signal Processing

International audienceFilm restoration using image processing, has been an active research field during the last years. However, the restoration of the soundtrack has been mainly performed in the sound domain, using signal processing methods, despite the fact that it is recorded as a continuous image between the images of the film and the perforations. While the very few published approaches focus on removing dust particles or concealing larger corrupted areas, no published works are devoted to the restoration of soundtracks degraded by substantial underexposure or overexposure. Digital restoration of optical soundtracks is an unexploited application field and, besides, scientifically rich, because it allows mixing both image and signal processing approaches. After introducing the principles of optical soundtrack recording and playback, this contribution focuses on our first approaches to detect and cancel the effects of under and overexposure. We intentionally choose to get a quantification of the effect of bad exposure in the 1D audio signal domain instead of 2D image domain. Our measurement is sent as feedback value to an image processing stage where the correction takes place, building up a "digital image and audio signal" closed loop processing. The approach is validated on both simulated alterations and real data

Heuristic Hyperparameter Choice for Image Anomaly Detection

Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep features extracted from pretrained models have been proved to be essential for AD based on multivariate Gaussian distribution analysis. However, since models are usually pretrained on a large dataset for classification tasks such as ImageNet, they might produce lots of redundant features for AD, which increases computational cost and degrades the performance. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features. So we proposed some heuristic to choose hyperparameter of NPCA algorithm for getting as fewer components of features as possible while ensuring a good performance