We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.

/pdf/shape-based-recognition-of-wiry-objects-1hniu7p4lm.pdf

Shape-based recognition of wiry objects

We frame the problem of object recognition from edge cues in terms of determining whether individual edge pixels belong to the target object or to clutter, based on the configuration of edges in their vicinity A classifier solves this problem by computing sparse, localized edge features at image locations determined at training time In order to save computation and solve the aperture problem, we apply a cascade of these classifiers to the image, each of which computes edge features over larger image regions than its predecessors Experiments apply this approach to the recognition of real objects with holes and wiry components in cluttered scenes under arbitrary out-of-image-plane rotation 1

http://www.ri.cmu.edu/pub_files/pub3/carmichael_owen_2002_2/carmichael_owen_2002_2.pdf

Object Recognition by a Cascade Of Edge Probes

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge cues. We first use example images of the desired object in typical backgrounds to train a classifier cascade which determines whether edge pixels in an image belong to an instance of the object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels. The features used for this classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of complex objects in cluttered indoor scenes under arbitrary out-of-image-plane rotation.

/pdf/shape-based-recognition-of-wiry-objects-y5antpmx9u.pdf

Image Analysis and Computer Vision: 1998☆

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

/pdf/two-stage-approach-to-extracting-visual-objects-from-paper-1xahzqxoj4.pdf

Two-stage approach to extracting visual objects from paper documents

Assume that some objects are present in an image but can be seen only partially and are overlapping each other. To recognize the objects, we have to firstly separate the objects from one another, and then match them against the modeled objects using partial observation. This paper presents a probabilistic approach for solving this problem. Firstly, the task is formulated as a two-stage optimal estimation process. The first stage, matching, separates different objects and finds feature correspondences between the scene and each potential model object. The second stage, recognition, resolves inconsistencies among the results of matching to different objects and identifies object categories. Both the matching and recognition are formulated in terms of the maximum a posteriori (MAP) principle. Secondly, contextual constraints, which play an important role in solving the problem, are incorporated in the probabilistic formulation. Specifically, between-object constraints are encoded in the prior distribution modeled as a Markov random field, and within-object constraints are encoded in the likelihood distribution modeled as a Gaussian. They are combined into the posterior distribution which defines the MAP solution. Experimental results are presented for matching and recognizing jigsaw objects under partial occlusion, rotation, translation and scaling.

Joachim Hornegger

Papers

A Two-Stage Probabilistic Approach for Object Recognition