| 1. |
- Lindeberg, Tony, 1964-, et al.
(author)
-
Analysis of brain activation patterns using a 3-D scale-space primal sketch
- 1999
-
In: Human Brain Mapping. - 1065-9471 .- 1097-0193. ; 7:3, s. 166-94
-
Journal article (peer-reviewed)abstract
- A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale-space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale-space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data.
|
|
| 2. |
- Roland, P, et al.
(author)
-
A database generator for human brain imaging
- 2001
-
In: TINS - Trends in Neurosciences. - 0166-2236 .- 1878-108X. ; 24:10, s. 562-564
-
Journal article (peer-reviewed)abstract
- Sharing scientific data containing complex information requires new concepts and new technology. NEUROGENERATOR is a database generator for the neuroimaging community. A database generator is a database that generates new databases. The scientists submit raw PET and fMRI data to NEUROGENERATOR, which then processes the data in a uniform way to create databases of homogenous data suitable for data sharing, met-analysis and modelling the human brain at the systems level. These databases are then distributed to the scientists.
|
|
| 3. |
- Rosbacke, M., et al.
(author)
-
Evaluation of using absolute versus relative base level when analyzing brain activation images using the scale-space primal sketch
- 2001
-
In: Medical Image Analysis. - 1361-8415 .- 1361-8423. ; 5:2, s. 89-110
-
Journal article (peer-reviewed)abstract
- A dominant approach to brain mapping is to define functional regions in the brain by analyzing images of brain activation obtained from positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). This paper presents an evaluation of using one such tool, called the scale-space primal sketch, for brain activation analysis. A comparison is made concerning two possible definitions of a significance measure of blob structures in scale-space, where local contrast is measured either relative to a local or global reference level. Experiments on real brain data show that (i) the global approach with absolute base level has a higher degree of correspondence to a traditional statistical method than a local approach with relative base level, and that (ii) the global approach with absolute base level gives a higher significance to small blobs that are superimposed on larger scale structures, whereas the significance of isolated blobs largely remains unaffected. Relative to previously reported works, the following two technical improvements are also presented. (i) A post-processing tool is introduced for merging blobs that are multiple responses to image structures. This simplifies automated analysis from the scale-space primal sketch. (ii) A new approach is introduced for scale-space normalization of the significance measure, by collecting reference statistics of residual noise images obtained from the general Linear model.
|
|
| 4. |
- Zagal, Juan Cristobal, et al.
(author)
-
Signficance determination for the scale-space primal sketch by comparison of statistics of scale-space blob volumes computed from PET signals vs. residual noise
- 2000
-
Conference paper (peer-reviewed)abstract
- A dominant approach to brain mapping is to define functional regions in the brain by analyzing brain activation images obtained by PET or fMRI. In [1], it has been shown that the scale-space primal sketch provides a useful tool for such analysis. Some attractive properties of this method are that it only makes few assumptions about the data and the process for extracting activations is fully automatic.In the present version of the scale-space primal sketch, however, there is no method for determining p-values. The purpose here is to present a new methodology for addressing this question, by introducing a descriptor referred to as the -curve, which serves as a first step towards determining the probability of false positives, i.e. alpha.
|
|
| 5. |
- Åkerman, S., et al.
(author)
-
Surface Model Generation and Segmentation of the Human Celebral Cortex for the Construction of Unfolded Cortical Maps
- 1996
-
In: Proc. 2nd International Conference on Functional Mapping of the Human Brain. ; , s. S126-S126
-
Conference paper (peer-reviewed)abstract
- Representing the shape of the human cerebral cortex arises as a basic subproblem in several areas of brain science, such as when describing the anatomy of the cortex and when relating functional measurements to cortical regions. Most current methods for building such representions of the cortical surface are either based on contours from two-dimensional cross sections or landmarks that have been obtained manually.In this article, we outline a methodology for semi-automatic contruction of a solely surface based representation of the human cerebral cortex in vivo for subsequent generation of (unfolded) two-dimensional brain maps.The method is based on input data in the form of three-dimensional NMR images, and comprises the following main steps:suppression of disturbing fine-scale structures by linear and non-linear scale-space techniques,generation of a triangulated surface representation based on either iso-surfaces or three-dimensional edge detection,division of the surface model into smaller segments based on differential invariants computed from the image data.When constructing an unfolded (flattened) surface representation, the instrinsic curvature of the cortex means that such a unfolding cannot be done without introducing distortions. To reduce this problem, we propose to cut the surface into smaller parts, where a ridge detector acts as guideline, and then unfold each patch individually, so as to obtain low distortions.Having a solely surface based representation of the cortex and expressing the image operations using multi-scale differential invariants in terms of scale-space derivatives as done in this work is a natural choice both in terms of conceptual and algorithmic simplicity. Moreover, explicitly handling the multi-scale nature of the data is necessary to obtain robust results.
|
|
| 6. |
- Linde, Oskar, 1979-, et al.
(author)
-
Composed Complex-Cue Histograms : An Investigation of the Information Content in Receptive Field Based Image Descriptors for Object Recognition
- 2012
-
In: Computer Vision and Image Understanding. - : Elsevier. - 1077-3142 .- 1090-235X. ; 116:4, s. 538-560
-
Journal article (peer-reviewed)abstract
- Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations. This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories. It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complex-cue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive experimental evaluations on two mutually independent data sets. For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatio-chromatic) differential invariants up to order two lead to very high recognition rates. For the task of category classification, primary information is contained in both first- and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue. Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.
|
|
| 7. |
- Lindeberg, Tony, 1964-, et al.
(author)
-
Scale-space with causal time direction
- 1996
-
Conference paper (peer-reviewed)abstract
- This article presents a theory for multi-scale representation of temporal data. Assuming that a real-time vision system should represent the incoming data at different time scales, an additional causality constraint arises compared to traditional scale-space theory—we can only use what has occurred in the past for computing representations at coarser time scales. Based on a previously developed scale-space theory in terms of noncreation of local maxima with increasing scale, a complete classification is given of the scale-space kernels that satisfy this property of non-creation of structure and respect the time direction as causal. It is shown that the cases of continuous and discrete time are inherently different.
|
|
| 8. |
- Lindeberg, Tony, 1964-
(author)
-
On the behaviour in scale-space of local extrema and blobs
- 1991
-
In: Theory and Applications of Image Analysis. - : World Scientific. ; , s. 38-47, s. 8-17
-
Book chapter (peer-reviewed)abstract
- We apply elementary techniques from real analysis and singularity theory to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treatment comprise: a description of the general nature of trajectories of critical points in scale-space. an estimation of the drift velocity of critical points and edges. an analysis of the qualitative behaviour of critical points in bifurcation situations. a classification of what types of blob bifurcations are possible.
|
|
| 9. |
- Lindeberg, Tony, 1964-, et al.
(author)
-
Foveal scale-space and the linear increase of receptive field size as a function of eccentricity
- 1994
-
Reports (other academic/artistic)abstract
- This paper addresses the formulation of a foveal scale-space and its relation to the scaling property of receptive field sizes with eccentricity. It is shown how the notion of a fovea can be incorporated into conventional scale-space theory leading to a foveal log-polar scale-space. Natural assumptions about uniform treatment of structures over scales and finite processing capacity imply a linear increase of minimum receptive field size as a function of eccentricity. These assumptions are similar to the ones used for deriving linear scale-space theory and the Gaussian receptive field model for an idealized visual front-end.
|
|
| 10. |
- Lindeberg, Tony, 1964-, et al.
(author)
-
Linear Scale-Space II : Early visual operations
- 1994
-
In: Geometry-Driven Diffusion in Vision. - : Kluwer Academic Publishers. ; , s. 43-77
-
Book chapter (other academic/artistic)abstract
- Vision deals with the problem of deriving information about the world from the light reflected from it. Although the active and task-oriented nature of vision is only implicit in this formulation, this view captures several of the essential aspects of vision. As Marr (1982) phrased it in his book Vision, vision is an information processing task, in which an internal representation of information is of utmost importance. Only by representation information can be captured and made available to decision processes. The purpose of a representation is to make certain aspects of the information content explicit, that is, immediately accessible without any need for additional processing.This introductory chapter deals with a fundamental aspect of early image representation---the notion of scale. As Koenderink (1984) emphasizes, the problem of scale must be faced in any imaging situation. An inherent property of objects in the world and details in images is that they only exist as meaningful entities over certain ranges of scale. A simple example of this is the concept of a branch of a tree, which makes sense only at a scale from, say, a few centimeters to at most a few meters. It is meaningless to discuss the tree concept at the nanometer or the kilometer level. At those scales it is more relevant to talk about the molecules that form the leaves of the tree, or the forest in which the tree grows. Consequently, a multi-scale representation is of crucial importance if one aims at describing the structure of the world, or more specifically the structure of projections of the three-dimensional world onto two-dimensional images.The need for multi-scale representation is well understood, for example, in cartography; maps are produced at different degrees of abstraction. A map of the world contains the largest countries and islands, and possibly, some of the major cities, whereas towns and smaller islands appear at first in a map of a country. In a city guide, the level of abstraction is changed considerably to include streets and buildings etc. In other words, maps constitute symbolic multi-scale representations of the world around us, although constructed manually and with very specific purposes in mind.To compute any type of representation from image data, it is necessary to extract information, and hence interact with the data using certain operators. Some of the most fundamental problems in low-level vision and image analysis concern: what operators to use, where to apply them, and how large they should be. If these problems are not appropriately addressed, the task of interpreting the output results can be very hard. Ultimately, the task of extracting information from real image data is severely influenced by the inherent measurement problem that real-world structures, in contrast to certain ideal mathematical entities, such as ``points'' or ``lines'', appear in different ways depending upon the scale of observation.Phrasing the problem in this way shows the intimate relation to physics. Any physical observation by necessity has to be done through some finite aperture, and the result will, in general, depend on the aperture of observation. This holds for any device that registers physical entities from the real world including a vision system based on brightness data. Whereas constant size aperture functions may be sufficient in many (controlled) physical applications, e.g., fixed measurement devices, and also the aperture functions of the basic sensors in a camera (or retina) may have to determined a priori because of practical design constraints, it is far from clear that registering data at a fixed level of resolution is sufficient. A vision system for handling objects of different sizes and at difference distances needs a way to control the scale(s) at which the world is observed.The goal of this chapter is to review some fundamental results concerning a framework known as scale-space that has been developed by the computer vision community for controlling the scale of observation and representing the multi-scale nature of image data. Starting from a set of basic constraints (axioms) on the first stages of visual processing it will be shown that under reasonable conditions it is possible to substantially restrict the class of possible operations and to derive a (unique) set of weighting profiles for the aperture functions. In fact, the operators that are obtained bear qualitative similarities to receptive fields at the very earliest stages of (human) visual processing (Koenderink 1992). We shall mainly be concerned with the operations that are performed directly on raw image data by the processing modules are collectively termed the visual front-end. The purpose of this processing is to register the information on the retina, and to make important aspects of it explicit that are to be used in later stage processes. If the operations are to be local, they have to preserve the topology at the retina; for this reason the processing can be termed retinotopic processing.Early visual operationsAn obvious problem concerns what information should be extracted and what computations should be performed at these levels. Is any type of operation feasible? An axiomatic approach that has been adopted in order to restrict the space of possibilities is to assume that the very first stages of visual processing should be able to function without any direct knowledge about what can be expected to be in the scene. As a consequence, the first stages of visual processing should be as uncommitted and make as few irreversible decisions or choices as possible.The Euclidean nature of the world around us and the perspective mapping onto images impose natural constraints on a visual system. Objects move rigidly, the illumination varies, the size of objects at the retina changes with the depth from the eye, view directions may change etc. Hence, it is natural to require early visual operations to be unaffected by certain primitive transformations (e.g. translations, rotations, and grey-scale transformations). In other words, the visual system should extract properties that are invariant with respect to these transformations.As we shall see below, these constraints leads to operations that correspond to spatio-temporal derivatives which are then used for computing (differential) geometric descriptions of the incoming data flow. Based on the output of these operations, in turn, a large number of feature detectors can be expressed as well as modules for computing surface shape.The subject of this chapter is to present a tutorial overview on the historical and current insights of linear scale-space theories as a paradigm for describing the structure of scalar images and as a basis for early vision. For other introductory texts on scale-space; see the monographs by Lindeberg (1991, 1994) and Florack (1993) as well as the overview articles by ter Haar Romeny and Florack (1993) and Lindeberg (1994).
|
|
