Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> Abstract Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest category cue validity, and are, thus, the most differentiated from one another. The four experiments of Part I define basic objects by demonstrating that in taxonomies of common concrete nouns in English based on class inclusion, basic objects are the most inclusive categories whose members: (a) possess significant numbers of attributes in common, (b) have motor programs which are similar to one another, (c) have similar shapes, and (d) can be identified from averaged shapes of members of the class. The eight experiments of Part II explore implications of the structure of categories. Basic objects are shown to be the most inclusive categories for which a concrete image of the category as a whole can be formed, to be the first categorizations made during perception of the environment, to be the earliest categories sorted and earliest named by children, and to be the categories most codable, most coded, and most necessary in language. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naive Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information. <s> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes. <s> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> We present a method for object categorization in real-world scenes. Following a common consensus in the field, we do not assume that a figure-ground segmentation is available prior to recognition. However, in contrast to most standard approaches for object class recognition, our approach automatically segments the object as a result of the categorization. <s> BIB004 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> Current approaches to visual object class detection mainly focus on the recognition of basic level categories, such as cars, motorbikes, mugs and bottles. Although these approaches have demonstrated impressive performance in terms of recognition, their restriction to these categories seems inadequate in the context of embodied, cognitive agents. Here, distinguishing objects according to functional aspects based on object affordances is important in order to enable manipulation of and interaction between physical objects and cognitive agent. ::: ::: In this paper, we propose a system for the detection of functional object classes, based on a representation of visually distinct hints on object affordances (affordance cues). It spans the complete range from tutordriven acquisition of affordance cues, learning of corresponding object models, and detecting novel instances of functional object classes in real images. <s> BIB005 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> We present a family of scale-invariant local shape features formed by chains of k connected roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary without including nearby clutter. Moreover, they offer an attractive compromise between information content and repeatability and encompass a wide variety of local shape structures. We also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kAS, making kAS easy to reuse in other frameworks, for example, as a replacement or addition to interest points (IPs). Software for detecting and describing kAS is released at http://lear.inrialpes.fr/software. We demonstrate the high performance of kAS within a simple but powerful sliding-window object detection scheme. Through extensive evaluations, involving eight diverse object classes and more than 1,400 images, we (1) study the evolution of performance as the degree of feature complexity k varies and determine the best degree, (2) show that kAS substantially outperform IPs for detecting shape-based classes, and (3) compare our object detector to the recent state-of-the-art system by Dalal and Triggs (2005). <s> BIB006 </s> Data-Driven Grasp Synthesis - A Survey <s> III. GRASPING FAMILIAR OBJECTS <s> We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set. <s> BIB007
The idea of addressing the problem of grasping familiar objects originates from the observation that many of the objects in the environment can be grouped together into categories with common characteristics. In the computer vision community, objects within one category usually share similar visual properties. These can be, e.g., a common texture  or shape BIB006 BIB007 , the occurrence of specific local features BIB002 BIB003 or their specific spatial constellation BIB004  . These categories are usually referred to as basic level categories and emerged from the area of cognitive psychology BIB001 . For grasping and manipulation of objects, a more natural characteristic may be the functionality that they afford BIB005 : similar objects are grasped in a similar way or may be used to fulfill the same task (pouring, rolling, etc). The difficulty is to find a representation that encodes these common affordances. Given the representation, a similarity metric has to be found under which objects of the same functionality can be considered as alike. The approaches discussed in this survey are summarized in Table II . All of them employ learning mechanisms and showed that they can generalize the grasp experience on training data to new but familiar objects.