A system for person-independent hand posture recognition against complex backgrounds

A computer vision system for non-independent recognition of hand postures against complex background is presented. The system is based on Elastic Graph Matching (EGM), which was extended to allow for combinations of different feature types at the graph nodes

Grouping variables in an underdetermined system for invariant object recognition

Poster presentation: Introduction We study the problem of object recognition invariant to transformations, such as translation, rotation and scale. A system is underdetermined if its degrees of freedom (number of possible transformations and potential objects) exceed the available information (image size). The regularization theory solves this problem by adding constraints [1]. It is unclear what constraints biological systems use. We suggest that rather than seeking constraints, an underdetermined system can make decisions based on available information by grouping its variables. We propose a dynamical system as a minimum system for invariant recognition to demonstrate this strategy. ..

Activity-dependent bidirectional plasticity and homeostasis regulation governing structure formation in a model of layered visual memory

Poster presentation: Our work deals with the self-organization [1] of a memory structure that includes multiple hierarchical levels with massive recurrent communication within and between them. Such structure has to provide a representational basis for the relevant objects to be stored and recalled in a rapid and efficient way. Assuming that the object patterns consist of many spatially distributed local features, a problem of parts-based learning is posed. We speculate on the neural mechanisms governing the process of the structure formation and demonstrate their functionality on the task of human face recognition. The model we propose is based on two consecutive layers of distributed cortical modules, which in turn contain subunits receiving common afferents and bounded by common lateral inhibition (Figure 1). In the initial state, the connectivity between and within the layers is homogeneous, all types of synapses – bottom-up, lateral and top-down – being plastic. During the iterative learning, the lower layer of the system is exposed to the Gabor filter banks extracted from local points on the face images. Facing an unsupervised learning problem, the system is able to develop synaptic structure capturing local features and their relations on the lower level, as well as the global identity of the person at the higher level of processing, improving gradually its recognition performance with learning time. ..

Experience-driven formation of parts-based representations in a model of layered visual memory

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

A global decision-making model via synchronization in macrocolumn units

Poster presentation: Introduction We here address the problem of integrating information about multiple objects and their positions on the visual scene. A primate visual system has little difficulty in rapidly achieving integration, given only a few objects. Unfortunately, computer vision still has great difficultly achieving comparable performance. It has been hypothesized that temporal binding or temporal separation could serve as a crucial mechanism to deal with information about objects and their positions in parallel to each other. Elaborating on this idea, we propose a neurally plausible mechanism for reaching local decision-making for "what" and "where" information to the global multi-object recognition. ..

A correspondence-based neural mechanism for position invariant feature processing

Poster presentation: Introduction We here focus on constructing a hierarchical neural system for position-invariant recognition, which is one of the most fundamental invariant recognition achieved in visual processing [1,2]. The invariant recognition have been hypothesized to be done by matching a sensory image of a particular object stimulated on the retina to the most suitable representation stored in memory of the higher visual cortical area. Here arises a general problem: In such a visual processing, the position of the object image on the retina must be initially uncertain. Furthermore, the retinal activities possessing sensory information are being far from the ones in the higher area with a loss of the sensory object information. Nevertheless, with such recognition ambiguity, the particular object can effortlessly and easily be recognized. Our aim in this work is an attempt to resolve such a general recognition problem. ..

Feature-driven Emergence of Model Graphs for Object Recognition and Categorization

An important requirement for the expression of cognitive structures is the ability to form mental objects by rapidly binding together constituent parts. In this sense, one may conceive the brain\u27s data structure to have the form of graphs whose nodes are labeled with elementary features. These provide a versatile data format with the additional ability to render the structure of any mental object. Because of the multitude of possible object variations the graphs are required to be dynamic. Upon presentation of an image a so-called model graph should rapidly emerge by binding together memorized subgraphs derived from earlier learning examples driven by the image features. In this model, the richness and flexibility of the mind is made possible by a combinatorical game of immense complexity. Consequently, the emergence of model graphs is a laborious task which, in computer vision, has most often been disregarded in favor of employing model graphs tailored to specific object categories like, for instance, faces in frontal pose. Recognition or categorization of arbitrary objects, however, demands dynamic graphs. In this work we propose a form of graph dynamics, which proceeds in two steps. In the first step component classifiers, which decide whether a feature is present in an image, are learned from training images. For processing arbitrary objects, features are small localized grid graphs, so-called parquet graphs, whose nodes are attributed with Gabor amplitudes. Through combination of these classifiers into a linear discriminant that conforms to Linsker\u27s infomax principle a weighted majority voting scheme is implemented. It allows for preselection of salient learning examples, so-called model candidates, and likewise for preselection of categories the object in the presented image supposably belongs to. Each model candidate is verified in a second step using a variant of elastic graph matching, a standard correspondence-based technique for face and object recognition. To further differentiate between model candidates with similar features it is asserted that the features be in similar spatial arrangement for the model to be selected. Model graphs are constructed dynamically by assembling model features into larger graphs according to their spatial arrangement. From the viewpoint of pattern recognition, the presented technique is a combination of a discriminative (feature-based) and a generative (correspondence-based) classifier while the majority voting scheme implemented in the feature-based part is an extension of existing multiple feature subset methods. We report the results of experiments on standard databases for object recognition and categorization. The method achieved high recognition rates on identity, object category, pose, and illumination type. Unlike many other models the presented technique can also cope with varying background, multiple objects, and partial occlusion

Mühsame Detektivarbeit : die Memorik als Herausforderung für die Geschichtswissenschaft

Rezension zu: Johannes Fried : Der Schleier der Erinnerung. Grundzüge einer historischen Memorik, C.H. Beck Verlag, München 2004, ISBN 3406522114, 512 Seiten, 39,90 Euro

The Correlation Theory of Brain Function

A summary of brain theory is given so far as it is contained within the framework of Localization Theory. Diffculties of this "conventional theory" are traced back to a specific deficiency: there is no way to express relations between active cells (as for instance their representing parts of the same object). A new theory is proposed to cure this deficiency. It introduces a new kind of dynamical control, termed synaptic modulation, according to which synapses switch between a conducting and a non- conducting state. The dynamics of this variable is controlled on a fast time scale by correlations in the temporal fine structure of cellular signals. Furthermore, conventional synaptic plasticity is replaced by a refined version. Synaptic modulation and plasticity form the basis for short-term and long-term memory, respectively. Signal correlations, shaped by the variable network, express structure and relationships within objects. In particular, the figure-ground problem may be solved in this way. Synaptic modulation introduces flexibility into cerebral networks which is necessary to solve the invariance problem. Since momentarily useless connections are deactivated, interference between different memory traces can be reduced, and memory capacity increased, in comparison with conventional associative memory