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Heinrich H. Bülthoff

Bio: Heinrich H. Bülthoff is an academic researcher from Max Planck Society. The author has contributed to research in topics: Haptic technology & Cognitive neuroscience of visual object recognition. The author has an hindex of 86, co-authored 974 publications receiving 30926 citations. Previous affiliations of Heinrich H. Bülthoff include Brown University & Korea University.


Papers
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Journal ArticleDOI
TL;DR: It is shown that, depending on the type of information, different combination and integration strategies are used and that prior knowledge is often required for interpreting the sensory signals.

1,628 citations

Journal ArticleDOI
TL;DR: The results suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.
Abstract: Does the human brain represent objects for recognition by storing a series of two-dimensional snapshots, or are the object models, in some sense, three-dimensional analogs of the objects they represent? One way to address this question is to explore the ability of the human visual system to generalize recognition from familiar to unfamiliar views of three-dimensional objects. Three recently proposed theories of object recognition--viewpoint normalization or alignment of three-dimensional models [Ullman, S. (1989) Cognition 32, 193-254], linear combination of two-dimensional views [Ullman, S. & Basri, R. (1990) Recognition by Linear Combinations of Models (Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge), A. I. Memo No. 1152], and view approximation [Poggio, T. & Edelman, S. (1990) Nature (London) 343, 263-266]--predict different patterns of generalization to unfamiliar views. We have exploited the conflicting predictions to test the three theories directly in a psychophysical experiment involving computer-generated three-dimensional objects. Our results suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.

696 citations

Journal ArticleDOI
TL;DR: The Ebbinghaus illusion does not provide evidence for the existence of two distinct pathways for perception and action in the visual system, and the differences found previously can be accounted for by a hitherto unknown, nonadditive effect in the illusion.
Abstract: Neuropsychological studies prompted the theory that the primate visual system might be organized into two parallel pathways, one for conscious perception and one for guiding action. Supporting evidence in healthy subjects seemed to come from a dissociation in visual illusions: In previous studies, the Ebbinghaus (or Titchener) illusion deceived perceptual judgments of size, but only marginally influenced the size estimates used in grasping. Contrary to those results, the findings from the present study show that there is no difference in the sizes of the perceptual and grasp illusions if the perceptual and grasping tasks are appropriately matched. We show that the differences found previously can be accounted for by a hitherto unknown, nonadditive effect in the illusion. We conclude that the illusion does not provide evidence for the existence of two distinct pathways for perception and action in the visual system.

442 citations

Journal ArticleDOI
TL;DR: Evidence is presented suggesting that the same dissociation between perception and action is evident in the visual processing of object shape as it is in the control of goal-directed grasping.

438 citations

01 Jan 2004
TL;DR: In this paper, it was shown that visual perception of object size and orientation depends on visual pathways in the cerebral cortex that are separate from those mediating the use of these same object properties in the control of goal-directed grasping.
Abstract: Summary SummaryBackground:DiscussionMaterials and methodsAcknowledgementsReferences Background: Earlier work with neurological patients has shown that the visual perception of object size and orientation depends on visual pathways in the cerebral cortex that are separate from those mediating the use of these same object properties in the control of goal-directed grasping. We present evidence suggesting that the same dissociation between perception and action is evident in the visual processing of object shape. In other words, discrimination between objects on the basis of their shape appears to be mediated by visual mechanisms that are functionally and neurally distinct from those controlling the pre-shaping of the hand during grasping movements directed at those same objects. Results We studied two patients with lesions in different parts of the cerebral visual pathways. One patient (RV), who had sustained bilateral lesions of the occipitoparietal cortex, was unable to use visual information to place her fingers correctly on the circumference of irregularly shaped objects when asked to pick them up, even though she had no difficulty in visually discriminating one such object from another. Conversely, a second patient (DF), who had bilateral damage in the ventrolateral occipital region, had no difficulty in placing her fingers on appropriate opposition points during grasping, even though she was unable to discriminate visually amongst such objects.

429 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

15,696 citations

Journal ArticleDOI
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

10,696 citations

Proceedings ArticleDOI
17 Jun 2006
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Abstract: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

8,736 citations

Journal ArticleDOI
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Abstract: A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

8,175 citations