| 1. |
- Heyden, Anders, et al.
(författare)
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Simplifications of Multilinear forms for Sequences of Images
- 1997
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Ingår i: Image and Vision Computing. - 0262-8856. ; 15:10, s. 749-757
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Tidskriftsartikel (refereegranskat)abstract
- This paper contains a simplified framework for the analysis of sequences of images taken by uncalibrated cameras. It is assumed that the correspondences between the points in the different images are known. Corresponding points in a sequence of n images are related to each other by a fixed n-linear form. This form is an object invariant property, closely linked to the motion of the camera relative to the fixed world. We first describe a reduced setting in which these multilinear forms are easier to understand and analyse. This new formulation of the multilinear forms is then extended to the calibrated case and the traditional uncalibrated case, thus highlighting the similarities between the different settings. The framework is of importance as a theoretical tool for understanding the algebra of multiple view geometry, but it is also a basis for constructing linear algorithms for the recovery of structure and motion from image sequences. This is illustrated in experiments. (C) 1997 Elsevier Science B.V.
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| 2. |
- Brunnström, Kjell, et al.
(författare)
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Scale and Resolution in Active Analysis of Local Image Structure
- 1990
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Ingår i: Image and Vision Computing. - : Elsevier. ; 8:4, s. 289-296
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Tidskriftsartikel (refereegranskat)abstract
- Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans. Problems connected to foveation (examination of selected regions of the world at high resolution) are examined. In particular, the problem of finding and classifying junctions from this aspect is considered. It is shown that foveation as simulated by controlled, active zooming in conjunction with scale-space techniques allows for robust detection and classification of junctions.
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| 3. |
- Granlund, Gösta H., et al.
(författare)
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Issues in Robot Vision
- 1994
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Ingår i: Image and Vision Computing. - : Elsevier BV. - 0262-8856 .- 1872-8138. ; 12:3, s. 131-148
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we discuss certain issues regarding robot vision. The main theme will be the importance of the choice of information representation. We will see the implications at different parts of a robot vision structure. We deal with aspects of pre-attentive versus attentive vision, control mechanisms for low level focus of attention, and representation of motion as the orientation of hyperplanes in multdimensional time-space. Issues of scale will be touched upon, and finally, a depth-from stereo algorithm based on guadrature filter phase is presented.
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| 4. |
- Lindeberg, Tony, 1964-
(författare)
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A scale selection principle for estimating image deformations
- 1998
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Ingår i: Image and Vision Computing. - 0262-8856 .- 1872-8138. ; 16, s. 961-977
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Tidskriftsartikel (refereegranskat)abstract
- A basic functionality of a vision system concerns the ability to compute deformation fields between different images of the same physical structure. This article advocates the need for incorporating explicit mechanisms for scale selection in this context, in algorithms for computing descriptors such as optic flow and for performing stereo matching. A basic reason why such a mechanism is essential is the fact that in a coarse-to-fine propagation of disparity or flow information, it is not necessarily the case that the most accurate estimates are obtained at the finest scales. The existence of interfering structures at fine scales may make it impossible to accurately match the image data at fine scales. selecting deformation estimates from the scales that minimize the (suitably normalized) uncertainty over scales. A specific implementation of this idea is presented for a region based differential flow estimation scheme. It is shown that the integrated scale selection and flow estimation algorithm has the qualitative properties of leading to the selection of coarser scales for larger size image structures and increasing noise level, whereas it leads to the selection of finer scales in the neighbourhood of flow field discontinuities
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| 5. |
- Lindeberg, Tony, 1964-, et al.
(författare)
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Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure
- 1997
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Ingår i: Image and Vision Computing. - : Elsevier. - 0262-8856 .- 1872-8138. ; 15:6, s. 415-434
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Tidskriftsartikel (refereegranskat)abstract
- This article describes a method for reducing the shape distortions due to scale-space smoothing that arise in the computation of 3-D shape cues using operators (derivatives) defined from scale-space representation. More precisely, we are concerned with a general class of methods for deriving 3-D shape cues from a 2-D image data based on the estimation of locally linearized deformations of brightness patterns. This class constitutes a common framework for describing several problems in computer vision (such as shape-from-texture, shape-from disparity-gradients, and motion estimation) and for expressing different algorithms in terms of similar types of visual front-end-operations. It is explained how surface orientation estimates will be biased due to the use of rotationally symmetric smoothing in the image domain. These effects can be reduced by extending the linear scale-space concept into an affine Gaussian scalespace representation and by performing affine shape adaptation of the smoothing kernels. This improves the accuracy of the surface orientation estimates, since the image descriptors, on which the methods are based, will be relative invariant under affine transformations, and the error thus confined to the higher-order terms in the locally linearized perspective transformation. A straightforward algorithm is presented for performing shape adaptation in practice. Experiments on real and synthetic images with known orientation demonstrate that in the presence of moderately high noise levels the accuracy is improved by typically one order of magnitude.
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| 6. |
- Lindeberg, Tony, 1964-, et al.
(författare)
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The Scale-Space Primal Sketch: Construction and Experiments
- 1992
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Ingår i: Image and Vision Computing. - 0262-8856 .- 1872-8138. ; 10:1, s. 3-18
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Tidskriftsartikel (refereegranskat)abstract
- We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure — stable scales and regions of interest-in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives intuitively reasonable results.
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| 7. |
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| 8. |
- Smeraldi, Fabrizio, et al.
(författare)
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Saccadic search with Gabor features applied to eye detection and real-time head tracking
- 2000
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Ingår i: Image and Vision Computing. - Guildford : Butterworths. - 0262-8856 .- 1872-8138. ; 18:4, s. 323-329
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Tidskriftsartikel (refereegranskat)abstract
- The Gabor decomposition is a ubiquitous tool in computer vision. Nevertheless, it is generally considered computationally demanding for active vision applications. We suggest an attention-driven approach to feature detection inspired by the human saccadic system. A dramatic speedup is achieved by computing the Gabor decomposition only on the points of a sparse retinotopic grid. An off-line eye detection application and a real-time head localisation and tracking system are presented. The real-time system features a novel eyeball-mounted camera designed to simulate the dynamic performance of the human eye and is, to the best of our knowledge, the first example of active vision system based on the Gabor decomposition.
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| 9. |
- Anjomshoae, Sule, et al.
(författare)
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Context-based image explanations for deep neural networks
- 2021
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Ingår i: Image and Vision Computing. - : Elsevier. - 0262-8856 .- 1872-8138. ; 116
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Tidskriftsartikel (refereegranskat)abstract
- With the increased use of machine learning in decision-making scenarios, there has been a growing interest in explaining and understanding the outcomes of machine learning models. Despite this growing interest, existing works on interpretability and explanations have been mostly intended for expert users. Explanations for general users have been neglected in many usable and practical applications (e.g., image tagging, caption generation). It is important for non-technical users to understand features and how they affect an instance-specific prediction to satisfy the need for justification. In this paper, we propose a model-agnostic method for generating context-based explanations aiming for general users. We implement partial masking on segmented components to identify the contextual importance of each segment in scene classification tasks. We then generate explanations based on feature importance. We present visual and text-based explanations: (i) saliency map presents the pertinent components with a descriptive textual justification, (ii) visual map with a color bar graph showing the relative importance of each feature for a prediction. Evaluating the explanations using a user study (N = 50), we observed that our proposed explanation method visually outperformed existing gradient and occlusion based methods. Hence, our proposed explanation method could be deployed to explain models’ decisions to non-expert users in real-world applications.
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| 10. |
- Bengtsson, Tomas, 1983, et al.
(författare)
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On robust optical flow estimation on image sequences with differently exposed frames using primal-dual optimization
- 2017
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Ingår i: Image and Vision Computing. - : Elsevier BV. - 0262-8856. ; 57, s. 78-88
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Tidskriftsartikel (refereegranskat)abstract
- Optical flow methods are used to estimate pixelwise motion information based on consecutive frames in image sequences. The image sequences traditionally contain frames that are similarly exposed. However, many real-world scenes contain high dynamic range content that cannot be captured well with a single exposure setting. Such scenes result in certain image regions being over- or underexposed, which can negatively impact the quality of motion estimates in those regions. Motivated by this, we propose to capture high dynamic range scenes using different exposure settings every other frame. A framework for OF estimation on such image sequences is presented, that can straightforwardly integrate techniques from the state-of-the-art in conventional OF methods. Different aspects of robustness of OF methods are discussed, including estimation of large displacements and robustness to natural illumination changes that occur between the frames, and we demonstrate experimentally how to handle such challenging flow estimation scenarios. The flow estimation is formulated as an optimization problem whose solution is obtained using an efficient primal–dual method.
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