Alessandro Ledda

Extending the Depth of Field in Microscopy Through Curvelet-Based Frequency-Adaptive Image Fusion

Limited depth of field is an important problem in microscopy imaging. 3D objects are often thicker than the depth of field of the microscope, which means that it is optically impossible to make one single sharp image of them. Instead, different images in which each time a different area of the object is in focus have to be fused together. In this work, we propose a curvelet-based image fusion method that is frequency-adaptive. Because of the high directional sensitivity of the curvelet transform (and consequentially, its extreme sparseness), the average performance gain of the new method over state-of-the-art methods is high.

Real-time multi-colourspace hand segmentation

This paper proposes an accurate real-time hand tracking and segmentation algorithm. A particle filter tracks the hands in time, based on colour and motion cues. This filter is able to automatically recover from failures and does not need an initialization phase. The algorithm is proven to be robust against lighting changes, and can be used in unconstrained environments. Hand segmentation is based on a Gaussian Mixture Model and refined using a combination of spatial information. Cues from both HSV and RGB colour space are used to increase robustness.

Majority ordering and the morphological pattern spectrum

Binary and grayscale mathematical morphology have many applications in different area. On the other hand, colour morphology is not widespread. The reason is the lack of a unique ordering of colour that makes the extension of grayscale morphology to colour images not straightforward. We will introduce a new majority sorting scheme that can be applied on binary, grayscale and colour images. It is based on the area of each colour or grayscale present in the image, and has the advantage of being independent of the values of the colours or grayvalues. We will take a closer look at the morphological pattern spectrum and will show the possible differences of the morphological pattern spectrum on colour images with the grayscale image pattern spectrum.

Phase transformations and precipitation in amorphous Ti50Ni25Cu25 ribbons

Abstract Phase transformations and precipitation mechanisms in Ti 50 Ni 25 Cu 25 material, starting from an amorphous planar-cast ribbon, were studied by in-situ transmission electron microscopy (TEM). On heating, the cubic B2 austenite crystallisation occurs around 470°C after which the orthorhombic B19 martensite is formed on cooling. Annealings up to 700°C yield the identification of Cu 3 Ti 2 and Ti 2 Ni precipitates. These precipitations drastically alter the matrix composition, resulting in the formation upon cooling of the monoclinic B19′ martensite with different types of long-period stacking sequences. Also the B19′ martensite is still recognised in the latter samples.

Non-Local Image Interpolation

In this paper we present a novel method for interpolating images and we introduce the concept of non-local interpolation. Unlike other conventional interpolation methods, the estimation of the unknown pixel values is not only based on its local surrounding neighbourhood, but on the whole image (non-locally). In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to better estimates of the unknown pixel values. Results show the effectiveness of non-local interpolation and its superiority at very large magnifications to other interpolation methods.

Correspondence Preserving Elastic Surface Registration with Shape Model Prior

In this paper, we describe a framework for surface registration. The framework consists of a combination of rigid registration, elasticity modulated registration and the use of a shape model prior. The main goal in this paper is to minimize the geometric surface registration error while maintaining correspondences. Experiments show improved geometric fit, correspondence, and timing compared to the current state of the art. Possible applications of the framework are construction of correspondences for shape models, reconstruction of missing parts, and artifact reduction.

An image interpolation scheme for repetitive structures

In this paper we present a novel method for interpolating images with repetitive structures. Unlike other conventional interpolation methods, the unknown pixel value is not estimated based on its local surrounding neighbourhood, but on the whole image. In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to a better reconstruction of the interpolated image. Results show the effectiveness of our proposed method and its superiority at very large magnifications to other traditional interpolation methods.

Real-time hand tracking by invariant hough forest detection

This paper proposes a robust real-time hand tracking approach by combining a discriminative random forest classifier with generative color based cues using a particle filter. The proposed detector is scale and rotation invariant and is able to overcome ambiguities and local maxima in the color based likelihood function in real-time. A new hand tracking dataset with manually annotated groundtruths is created and made freely available for research purposes. Thorough evaluation shows the robustness and advantages of our proposal compared to other state of the art object tracking methods.

Real-time, long-term hand tracking with unsupervised initialization

This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods.

Morphological image interpolation to magnify images with sharp edges

In this paper we present an image interpolation method, based on mathematical morphology, to magnify images with sharp edges. Whereas a simple blow up of the image will introduce jagged edges, called ‘jaggies’, our method avoids these jaggies, by first detecting jagged edges in the trivial nearest neighbour interpolated image, making use of the hit-or-miss transformation, so that the edges become smoother. Experiments have shown that our method performs very well for the interpolation of ‘sharp’ images, like logos, cartoons and maps, for binary images and colour images with a restricted number of colours.