Multi-modal Primitives as Functional Models of Hyper-columns and Their Use for Contextual Integration

Krüger, Norbert; Wörgötter, Florentin

doi:10.1007/11565123_16

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…Local descriptors such as the intrinsic dimensionality confidences, however, give good indications of the kind of local image structure and the post-processing that should be applied to it. In this way, it can be used as an initializer of processes taking also context information into account (see, e.g., [30,38]). …”

Section: Summary and Discussionmentioning

confidence: 99%

Continuous dimensionality characterization of image structures

Felsberg

Kalkan

Krüger

2009

Image and Vision Computing

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Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patches, edge-like structures and junctions. The main novelty of our approach is the representation of confidences as prior probabilities which can be used within a probabilistic framework. To show the potential of our continuous representation, we highlight applications in various contexts such as image structure classification, feature detection and localisation, visual scene statistics and optic flow evaluation.

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Section: Summary and Discussionmentioning

confidence: 99%

Continuous dimensionality characterization of image structures

Felsberg

Kalkan

Krüger

2009

Image and Vision Computing

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“…For a state-of-the-art example of a computer vision system that uses contextual disambiguation for natural scene interpretation see Rasiwasia and Vasconcelos (2012). For a biologically based neurocomputational study showing the effectiveness of context-sensitive disambiguation at the level of local feature processing see Krüger and Wörgötter (2005).…”

Section: Contextual Disambiguation In Perceptionmentioning

confidence: 99%

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Phillips

Clark

Silverstein

2015

Neuroscience & Biobehavioral Reviews

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A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.

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“…Observations are provided by an early-cognitive-vision (ECV) system [8], which extracts 3D primitives from stereo views of a scene. The quality of such ECV representations varies as a function of local visual signal quality.…”

Section: Methodsmentioning

confidence: 99%

Probabilistic Pose Recovery Using Learned Hierarchical Object Models

2008

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Abstract. This paper presents a probabilistic representation for 3D objects, and details the mechanism of inferring the pose of real-world objects from vision. Our object model has the form of a hierarchy of increasingly expressive 3D features, and probabilistically represents 3D relations between these. Features at the bottom of the hierarchy are bound to local perceptions; while we currently only use visual features, our method can in principle incorporate features from diverse modalities within a coherent framework. Model instances are detected using a Nonparametric Belief Propagation algorithm which propagates evidence through the hierarchy to infer globally consistent poses for every feature of the model. Belief updates are managed by an importance-sampling mechanism that is critical for efficient and precise propagation. We conclude with a series of pose estimation experiments on real objects, along with quantitative performance evaluation.

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Multi-modal Primitives as Functional Models of Hyper-columns and Their Use for Contextual Integration

Cited by 11 publications

References 17 publications

Continuous dimensionality characterization of image structures

Continuous dimensionality characterization of image structures

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Probabilistic Pose Recovery Using Learned Hierarchical Object Models

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