Recognizing an object from its background is always a very challenging task for pattern recognition, especially when the size, pose, or illumination of the object or the background are changing. The most essential method of handling this classical problem is to learn and define the structure of an object using its topological or geometrical features and components. To create a data structure that can describe the spatial relationships of object components formally and join knowledge learning and applying in a seamless loop, a representation platform must be developed. This platform can serve as a shared workspace not only for learning but also for recognition. In this paper, the platform is established by simulating the primary functional modules in the biological primary visual cortex (V1). V1 is located at the middle level of the visual information processing system. As the conjunction of low-level data and high-level knowledge, it performs visual processing for general purposes. Orientation columns in V1 are simulated in our platform, and an array of such columns is designed to represent the orientation features of edges in an image. With this platform, formalized prototypes are designed to represent each typical view of an object and thus the object concept. Data- and concept-driven processing can shift iteratively on this platform. The processes of acquiring knowledge of an object and applying that knowledge coincide with each other perfectly. The experimental results show that our algorithm can learn from a small training set and can recognize the same types of objects in natural background without any preliminary information. This bioinspired representation platform offers a promising prospect for the handling of semantic-concerned problems that need prior knowledge.

Learning and Representing Object Shape Through an Array of Orientation Columns

The shape, or contour, of an object is usually stable and persistent, so it is a good basis for invariant recognition. For this purpose, two problems must be addressed. The first is to obtain clean edges, and the second is to organize those edges into a structured data form upon which the necessary manipulations and analysis may be performed. Simple cells in the primary visual cortex are specialized in orientation detection, so the neural mechanism can be simulated by a computational model, which can produce a fairly clean set of lines, and all of them in vectors rather than in pixels. Then a line-context descriptor was designed to describe geometrical distribution of lines in a local area. All lines were also recorded by a weighted graph, and its minimum spanning tree can be used to describe the topological features of an object. An iterative matching algorithm was developed by combining line-context descriptors and minimum spanning tree, and was shown to match objects of the same type but with different shapes very well. Our results suggest that key to representation efficiency of searchable trees is to apply a mid-level line-context. This once more confirms the crucial role played by simple cells in visual processing path, for its preprocessing can greatly ease the subsequent processing.

A Shape Recognition Method Based on Graph- and Line-Contexts

In 3D objects recovering, geometry features of 3D contour curves were analyzed, method of 3D contour curves matching based geometry features was presented. Contour curves of 3D objects were extracted and represented by B-spline. According to geometry features of 3D contour, curvatures and torsions of point on curves were calculated after filtering and smoothing contour curves. Eigenvectors of 3D contours were constructed by curvatures and torsions. Features of corresponding points on two contour curves were compared to judge similarity of contour. Matching relation of contour was determined between tow contour curves based on similarity between two contours. Fragments mosaicing were realized for recovering broken objects based on contour curves matching. Experimental results show that that this method has high speed, accuracy and validity.

On 3D Contour Matching Based on Geometry Features

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On 3D Contour Matching Based on Geometry Features