Indexing for local appearance-based recognition of planar objects
TL;DR: An optimal feature extraction technique that selects only the salient features of an object that exploits the fact that features tend to form clusters in the feature space based on their similarity of appearances is proposed.
About: This article is published in Pattern Recognition Letters.The article was published on 2002-01-01. It has received 3 citations till now. The article focuses on the topics: Feature (computer vision) & Feature extraction.
Citations
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Patent•
26 Dec 2005TL;DR: In this paper, the authors presented an image processing system, a learning device and a method, and a program capable of easily extracting a characteristic amount used for recognition processing. But this method was not applied to a robot.
Abstract: There are provided an image processing system, a learning device and a method, and a program capable of easily extracting a characteristic amount used for recognition processing. A characteristic point is extracted from a learning model image. According to the characteristic point, a characteristic amount is extracted. The characteristic amount is registered in a leaning model dictionary registration unit (23). Similarly, a characteristic point is extracted from a leaning input image containing a model object contained in the learning model image. According to the characteristic point, a characteristic amount is extracted. The characteristic amount is compared to the characteristic amount registered in the learning model registration unit (23). As the comparison result, the characteristic amount which has become a pair most frequently is registered as a characteristic amount used for recognition processing in a model dictionary registration unit (12). The present invention may be applied to a robot.
51 citations
Patent•
26 Dec 2005
TL;DR: In this article, the authors propose a method to improve the quality of the data collected by the data collection system by using the information gathered from the sensor nodes of the sensor board.
Abstract: 本発明は、簡便に、認識処理に用いる特徴量を抽出できるようにする画像処理システム、学習装置および方法、並びにプログラムに関する。学習用モデル画像から特徴点が抽出され、その特徴点を基に、特徴量が抽出され、その特徴量が学習用モデル辞書登録部２３に登録される。同様に、学習用モデル画像に含まれるモデル物体を含む学習用入力画像から特徴点が抽出され、その特徴点を基に、特徴量が抽出され、その特徴量と、学習用モデル登録部２３に登録されている特徴量が比較される。その比較の結果、最も対になった回数が多い特徴量が、認識処理に用いられる特徴量として、モデル辞書登録部１２に登録される。本発明は、ロボットに適用することができる。
References
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TL;DR: An object recognition system is implemented that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation, and is therefore relatively insensitive to occlusion.
Abstract: Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. Using physical models for image formation, the authors derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces including many textured surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. The authors have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. The authors present several examples demonstrating the system's ability to recognize model objects in cluttered scenes independent of object configuration and scene illumination. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses.
133 citations
01 Jan 1997
TL;DR: The use of appearance based vision for defining visual processes for navigation by associating the appearance of a scene from a given viewpoint with the simple trajectories is described.
Abstract: This paper describes the use of appearance based vision for defining visual processes for navigation. A visual processes which transform images to commands and events. A family of visual processes are defined by associating the appearance of a scene from a given viewpoint with the simple trajectories. Appearance is captured as a set of low-resolution images. Energy normalised cross correlation is used to maintain heading, to estimated confidence and to servo control a robot vehicle while following a path. Experimental results are presented which compare results with a single camera, a pair of parallel cameras and a pair of divergent cameras. The most accurate (and robust) navigation is found with a pair of cameras which are slightly divergent.
95 citations
18 Jun 1996
TL;DR: This work presents an algorithm that is robust in these terms, which uses several small features on the object rather than a monolithic template, giving the system robustness in the face of background clutter and partial occlusions.
Abstract: Planar pose measurement from images is an important problem for automated assembly and inspection. In addition to accuracy and robustness, ease of use is very important for real world applications. Recently, Murase and Nayar have presented the "parametric eigenspace " for object recognition and pose measurement based on training images. Although their system is easy to use, it has potential problems with background clutter and partial occlusions. We present an algorithm that is robust in these terms. It uses several small features on the object rather than a monolithic template. These "eigenfeatures" are matched using a median statistic, giving the system robustness in the face of background clutter and partial occlusions. We demonstrate our algorithm's pose measurement accuracy with a controlled test, and we demonstrate its detection robustness on cluttered images with the objects of interest partially occluded.
45 citations
17 Jun 1997
TL;DR: The method, which implements a robust hierarchical form of the Kalman filter derived from the Minimum Description Length (MDL) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recognition.
Abstract: We describe a hierarchical appearance-based method for learning, recognizing, and predicting arbitrary spatiotemporal sequences of images The method, which implements a robust hierarchical form of the Kalman filter derived from the Minimum Description Length (MDL) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recognition Successive levels of the hierarchical filter implement dynamic models operating over successively larger spatial and temporal scales Each hierarchical level predicts the recognition state at a lower level and modifies its own recognition state using the residual error between the prediction and the actual lower-level state Simultaneously, on a longer time scale, the filter learns an internal model of input dynamics by adapting its generative and state transition matrices at each level to minimize prediction errors The resulting prediction/learning scheme thereby implements an on-line form of the well-known Expectation-Maximization (EM) algorithm from statistics We present experimental results demonstrating the method's efficacy in mediating robust spatiotemporal recognition in a variety of scenarios containing varying degrees of occlusions and clutter
44 citations
13 Apr 1996
TL;DR: Experiments show the method capable of learning to recognize complex objects in cluttered images, acquiring models that represent those objects using relatively few views.
Abstract: We describe how to model the appearance of an object using multiple views, learn such a model from training images, and recognize objects with it The model uses probability distributions to characterize the significance, position, and intrinsic measurements of various discrete features of appearance; it also describes topological relations among features The features and their distributions are learned from training images depicting the modeled object A matching procedure, combining qualities of both alignment and graph subisomorphism methods, uses feature uncertainty information recorded by the model to guide the search for a match between model and image Experiments show the method capable of learning to recognize complex objects in cluttered images, acquiring models that represent those objects using relatively few views
36 citations