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.

A Scalable Approach to Activity Recognition based on Object Use

Abstract: We propose an approach to activity recognition based on detecting and analyzing the sequence of objects that are being manipulated by the user. In domains such as cooking, where many activities involve similar actions, object-use information can be a valuable cue. In order for this approach to scale to many activities and objects, however, it is necessary to minimize the amount of human-labeled data that is required for modeling. We describe a method for automatically acquiring object models from video without any explicit human supervision. Our approach leverages sparse and noisy readings from RFID tagged objects, along with common-sense knowledge about which objects are likely to be used during a given activity, to bootstrap the learning process. We present a dynamic Bayesian network model which combines RFID and video data to jointly infer the most likely activity and object labels. We demonstrate that our approach can achieve activity recognition rates of more than 80% on a real-world dataset consisting of 16 household activities involving 33 objects with significant background clutter. We show that the combination of visual object recognition with RFID data is significantly more effective than the RFID sensor alone. Our work demonstrates that it is possible to automatically learn object models from video of household activities and employ these models for activity recognition, without requiring any explicit human labeling.

HoloWall: designing a finger, hand, body, and object sensitive wall

Abstract: This TechNote reports on our initial results of realizing a computer augmented wall called the HoloWall. Using an infrared camera located behind the wall, this system allows a user to interact with this computerized wall using ngers, hands, their body, or even a physical object such as a document folder.

Learning hierarchical models of scenes, objects, and parts

Abstract: We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the expected appearance and position, in an object centered coordinate frame, of features detected by a low-level interest operator. Each object category then has its own distribution over these parts, which are shared between objects. We learn the parameters of this model via a Gibbs sampler which uses the graphical model's structure to analytically average over many parameters. Applied to a database of images of isolated objects, the sharing of parts among objects improves detection accuracy when few training examples are available. We also extend this hierarchical framework to scenes containing multiple objects

Digital media management apparatus and methods

Abstract: Methods and apparatus for managing, finding and displaying objects such as digital images. Objects are tagged (“associated”) with descriptive textual and numeric data (“metadata”), and stored in a relational database from which they can be selected, sorted, and found. Tags can be defined by name, tag type, and associated attributes. Objects can be tagged by dropping a tag onto the object, or relating a database record for the tag to a database record for the object. Tagged objects can be searched for and displayed according to the degree to which their metadata matches the search criteria. Visual cues can indicate whether displayed objects match all, some but not all, or none of the search criteria. Database object distributions can be displayed as histograms or scatter plots, including timelines, calendars or maps. Object distributions can be used to search for objects or to limit search results for a previous search.

Segmentation, registration, and measurement of shape variation via image object shape

Abstract: A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representation's prior probability of local geometry by reflecting variabilities in the net's node and link parameter values, and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm for image analysis of deforming such a model to optimize a posteriori probability is described, and this paradigm is shown to be usable as a uniform approach for object definition, object-based registration between images of the same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object, compared with a normal anatomical object. Examples of applications of these methods in radiotherapy, surgery, and psychiatry are given.