Model-based stroke extraction and matching for handwritten Chinese character recognition

Image Analysis and Computer Vision

An efficient algorithm for the optimal polygonal approximation of digitized curves

We consider the problem of detecting object contours in natural images. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. We introduce a new morphological operator, called adaptive pseudo-dilation (APD), which uses context dependent structuring elements in order to identify long curvilinear structure in the edge map. We show that grouping edge pixels as the connected components of the output of APD results in a good agreement with the Gestalt law of good continuation. The novelty of this operator is that dilation is limited to the Voronoi cell of each edge pixel. An efficient implementation of APD is presented. The grouping algorithm is then embedded in a multithreshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. The use of different thresholds makes the algorithm much less sensitive to the values of the input parameters. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contours.

/pdf/adaptive-pseudo-dilation-for-gestalt-edge-grouping-and-3zf7ncypzo.pdf

Adaptive Pseudo Dilation for Gestalt Edge Grouping and Contour Detection

It is generally accepted that to develop versatile bin-picking systems capable of grasping and manipulation operations, accurate 3-D information is required. To accomplish this goal, we have developed a fast and precise range sensor based on active depth from defocus (DFD). This sensor is used in conjunction with a three-component vision system, which is able to recognize and evaluate the attitude of 3-D objects. The first component performs scene segmentation using an edge-based approach. Since edges are used to detect the object boundaries, a key issue consists of improving the quality of edge detection. The second component attempts to recognize the object placed on the top of the object pile using a model-driven approach in which the segmented surfaces are compared with those stored in the model database. Finally, the attitude of the recognized object is evaluated using an eigenimage approach augmented with range data analysis. The full bin-picking system will be outlined, and a number of experimental results will be examined.

http://www.cipa.dcu.ie/papers/mva_final.pdf

A bin picking system based on depth from defocus

A new approach for aggregating edge points into line segments

Recognition of occluded objects with heuristic search

A quadratic classifier for high-dimensional, periodic-measurement pattern-recognition problems

The use of data windows in feature extraction for high dimensional PR problems

Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.

Guturu Parthasarathy

Papers

A new approach for aggregating edge points into line segments

Recognition of occluded objects with heuristic search

A quadratic classifier for high-dimensional, periodic-measurement pattern-recognition problems

The use of data windows in feature extraction for high dimensional PR problems

Recognition of partial planar shapes in limited memory environments