Object Recognition Using Image Warping in an Intelligent Space
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We propose a method to improve object recognition performance of a robot with an intelligent space system (iSpace). Existing SIFT-based object recognition is powerful, but it has still some limitations. Among limitations, we focus on degraded performance according to the viewpoint change. In order to improve the performance, we use appearance estimation of the object in the point of robot's view using image warping based on the robot pose and object pose. The proposed method is evaluated by experiments. Keywords:
Image warping
Image warping
Resizing
Distortion (music)
Seam carving
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The recognition task is transformed into simpler subtasks. Two assumptions are vital in this approach: (a) the object representation is pictorial, and (b) the parts of the object do not bear any information about the shape of the object. The aim is to find a framework which will make the problem of recognition easier. The recognition consists of two subtasks: classification of the object into its proper class and identification of the particular member of the class. The classification is performed on the basis of the object's iconic representation; the identification is based on the pattern representation. This fact is used to propose a multiresolution architecture which features classification of the whole object at only one resolution. It provides a framework in which the contemporary neural networks being applied to simple problems may be applied to real-world problems of visual object recognition.< >
Representation
Identification
Basis (linear algebra)
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Recognition of objects is accomplished through the use of cues that depend on internal representations of familiar shapes. We used a paradigm of perceptual learning during visual search to explore what features human observers use to identify objects. Human subjects were trained to search for a target object embedded in an array of distractors, until their performance improved from near-chance levels to over 80% of trials in an object-specific manner. We determined the role of specific object components in the recognition of the object as a whole by measuring the transfer of learning from the trained object to other objects sharing components with it. Depending on the geometric relationship of the trained object with untrained objects, transfer to untrained objects was observed. Novel objects that shared a component with the trained object were identified at much higher levels than those that did not, and this could be used as an indicator of which features of the object were important for recognition. Training on an object also transferred to the components of the object when these components were embedded in an array of distractors of similar complexity. These results suggest that objects are not represented in a holistic manner during learning but that their individual components are encoded. Transfer between objects was not complete and occurred for more than one component, regardless of how well they distinguish the object from distractors. This suggests that a joint involvement of multiple components was necessary for full performance.
Component (thermodynamics)
Transfer of learning
3D single-object recognition
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This paper presents the recognition of object categories and object arrangements using You Only Look Once framework. Daily life objects are usually surrounded by other objects. The nearby objects around a target are obstacles to object picking. Therefore, different grasp strategies are needed depending on the object arrangements as well as the object categories. The recognition method of two arrangement categories of books (horizontal stacking and vertical stacking) is discussed in this paper. The recognition results show that the method is useful for recognizing object categories and object arrangements.
3D single-object recognition
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We present an approach to function-based object recognition that reasons about the functionality of an object's initiative parts. We extend the popular "recognition by parts" shape recognition framework to support "recognition, by functional parts", by combining a set of functional primitives and their relations with a set of abstract volumetric shape primitives and their relations. Previous approaches have relied on more global object features, often ignoring the problem of object segmentation, and thereby restricting themselves to range images of unoccluded scenes. We show how these shape primitives and relations can be easily recovered from superquadric ellipsoids which, in turn, can be recovered from either range or intensity images of occluded scenes. Furthermore, the proposed framework supports both unexpected (bottom-up) object recognition and expected (top-down) object recognition. We demonstrate the approach on, a simple domain by recognizing a restricted class of hand-tools from 2-D images.< >
3D single-object recognition
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Automatic learning of 3D object descriptions for the recognition of future instances of sensed objects is an important feature of vision systems. In this paper, we describe a clustering-based method for learning object description and a scheme for subsequent classification using this description. The description derived is an evidence rulebase that discriminates between the object classes. During classification, a sensed object is either recognized as one of the objects in the database or rejected as an unknown object. The rules fired by the object are used to eliminate hypotheses for which there is no evidence in the sensed object and to arrive at its identity and pose.
3D single-object recognition
Feature (linguistics)
Object model
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Identification
3D single-object recognition
Canny edge detector
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Proposes a method of object recognition using appearance models accumulated into a RFID (radio frequency identification) tag attached to the environment. Robots recognize the object using appearance models accumulated in the tag on the object. If the robot fails in recognition, it acquires a model of the object and accumulates it to the tag. Since robots in the environment observe the object from different points of view at different time, various appearance models are accumulated as time passes. In order to accumulate many models, eigenspace analysis is applied. The eigenspace is reconstructed every time robots acquire the model. Experimental result of object recognition shows effectiveness of the proposed method.
3D single-object recognition
Identification
Active appearance model
Object model
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Image warping
Basis function
Basis (linear algebra)
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This project report is aimed to study the warping behaviour of steel section. The main objective of this study is to obtain relationship of warping displacement and warping constant. In BS5950, there is already a basic derivation of warping constant with its relationship with the geometry of cross section. Somehow, this formula is unsuitable to be applied for some section member such as corrugated I-beam. Modifying the formula needs a lot of study and research; it is not economic both in cost and time. To overcome this, there comes a suggestion. There might be a relationship between warping displacements of member with different cross section as they are using the same material. By developing a relationship between warping constant and warping displacement, it is easy to find the warping constant of abnormal section with condition that we know its warping displacement. But, there is no reference available for the relationship governing the warping constant and warping displacement. In order to achieve the objectives, finite element analysis is camed out. Each parameter that affects the warping displacement and warping constant is under the consideration and been studied.
Image warping
Constant (computer programming)
Section (typography)
Dynamic Time Warping
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