Indexing for local appearance-based recognition of planar objects

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.

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。

Abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks. This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.

Abstract: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >

Abstract: The problem of automatically learning object models for recognition and pose estimation is addressed. In contrast to the traditional approach, the recognition problem is formulated as one of matching appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties and constant for a rigid object, pose and illumination vary from scene to scene. A compact representation of object appearance is proposed that is parametrized by pose and illumination. For each object of interest, a large set of images is obtained by automatically varying pose and illumination. This image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the object is represented as a manifold. Given an unknown input image, the recognition system projects the image to eigenspace. The object is recognized based on the manifold it lies on. The exact position of the projection on the manifold determines the object's pose in the image. A variety of experiments are conducted using objects with complex appearance characteristics. The performance of the recognition and pose estimation algorithms is studied using over a thousand input images of sample objects. Sensitivity of recognition to the number of eigenspace dimensions and the number of learning samples is analyzed. For the objects used, appearance representation in eigenspaces with less than 20 dimensions produces accurate recognition results with an average pose estimation error of about 1.0 degree. A near real-time recognition system with 20 complex objects in the database has been developed. The paper is concluded with a discussion on various issues related to the proposed learning and recognition methodology.

Abstract: Objects can be recognized on the basis of their color alone by color indexing, a technique developed by Swain-Ballard (1991) which involves matching color-space histograms. Color indexing fails, however, when the incident illumination varies either spatially or spectrally. Although this limitation might be overcome by preprocessing with a color constancy algorithm, we instead propose histogramming color ratios. Since the ratios of color RGB triples from neighboring locations are relatively insensitive to changes in the incident illumination, this circumvents the need for color constancy preprocessing. Results of tests with the new color-constant-color-indexing algorithm on synthetic and real images show that it works very well even when the illumination varies spatially in its intensity and color. >

Abstract: This paper describes a method for recognizing partially occluded objects for bin-picking tasks using eigenspace analysis, referred to as the "eigen window" method, that stores multiple partial appearances of an object in an eigenspace. Such partial appearances require a large amount of memory space. Three measurements, detectability, uniqueness, and reliability, on windows are developed to eliminate redundant windows and thereby reduce memory requirements. Using a pose clustering technique, the method determines the pose of an object and the object type itself. We have implemented the method and verified its validity.