Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.

Towards automatic discovery of object categories

Abstract: We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Our models represent objects as probabilistic constellations of rigid parts (features). The variability within a class is represented by a join probability density function on the shape of the constellation and the appearance of the parts. Our method automatically identifies distinctive features in the training set. The set of model parameters is then learned using expectation maximization. When trained on different, unlabeled and unsegmented views of a class of objects, each component of the mixture model can adapt to represent a subset of the views. Similarly, different component models can also "specialize" on sub-classes of an object class. Experiments on images of human heads, leaves from different species of trees, and motor-cars demonstrate that the method works well over a wide variety of objects.

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

Abstract: We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on this https URL

Semiautomatic segmentation and tracking of semantic video objects

Abstract: This paper introduces a novel semantic video object extraction system using mathematical morphology and a perspective motion model. Inspired by the results from the study of the human visual system, we intend to solve the semantic video object extraction problem in two separate steps: supervised I-frame segmentation, and unsupervised P-frame tracking. First, the precise semantic video object boundary can be found using a combination of human assistance and a morphological segmentation tool. Second, the semantic video objects in the remaining frames are obtained using global perspective motion estimation and compensation of the previous semantic video object plus boundary refinement as used for I frames.

Method of controlling the execution of object-oriented programs

Abstract: A method used by a digital computer in controlling execution of an object-oriented program to effect a defined action, e.g., stopping the program, when a specified virtual function is invoked on a specified object during execution of the program. A breakpoint address is determined at run time, advantageously after the specified object is created in accordance with execution of the program. The breakpoint address determination is not based solely on symbol table, pre-execution, information, but in addition on information generated in conjunction with the creation of the specified object. The breakpoint is inserted while program execution is stopped at an intermediate program point after the specified object is created. After program execution is resumed and the specified virtual function is invoked in accordance with the program, the breakpoint fires. However, the defined action is performed only in response to determining that the firing occurred on the specified object.

Framework for negotiation and tracking of sale of goods

Abstract: A computer implemented system and process are provided for negotiation and tracking of sale of goods. In this system and process, a negotiation engine (16) operates to store data representing a current state (18) of a negotiation between a seller and buyer. The negotiation engine (16) stores the data within a framework for representing aspects of the negotiation between the seller and buyer. The framework includes a request object, a promise object and an acceptance object that can store a current description of a contract. The framework also includes a set of one or more delivery deals determined by the contract. Each delivery deal can have a delivery request object, a delivery promise object, and a delivery acceptance object that can store associated item deals and time periods for delivery of item deals. Each item deal can have an item request object, an item promise object and an item acceptance object that can store individual sales-order line-items. The negotiation engine (16) thereby allows a user to monitor the current state of the negotiation over a range of prices, a range of dates, ranges of quantities of a set of goods, and a range of configurations of the goods in the set.