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.

Fine-grained activity recognition by aggregating abstract object usage

Abstract: In this paper we present results related to achieving finegrained activity recognition for context-aware computing applications. We examine the advantages and challenges of reasoning with globally unique object instances detected by an RFID glove. We present a sequence of increasingly powerful probabilistic graphical models for activity recognition. We show the advantages of adding additional complexity and conclude with a model that can reason tractably about aggregated object instances and gracefully generalizes from object instances to their classes by using abstraction smoothing. We apply these models to data collected from a morning household routine.

OCNet: Object Context Network for Scene Parsing

Abstract: In this paper, we address the semantic segmentation task with a new context aggregation scheme named \emph{object context}, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}. We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff

Linking of computers based on optical sensing of digital data

Abstract: A printed object, such as an item of postal mail, a book, printed advertising, a business card, product packaging, etc., is steganographically encoded with plural-bit data. When such an object is presented to an optical sensor, the plural-bit data is decoded and used to establish a link to an internet address corresponding to that object.

Object identity as a query language primitive

Abstract: We demonstrate the power of object identities (oid's) as a database query language primitive. We develop an object-based data model, whose structural part generalizes most of the known complex-object data models: cyclicity is allowed in both its schemas and instances. Our main contribution is the operational part of the data model, the query language IQL, which uses oid's for three critical purposes: (1) to represent data-structures with sharing and cycles, (2) to manipulate sets and (3) to express any computable database query. IQL can be statically type checked, can be evaluated bottom-up and naturally generalizes most popular rule-based database languages. The model can also be extended to incorporate type inheritance, without changes to IQL. Finally, we investigate an analogous value-based data model, whose structural part is founded on regular infinite trees and whose operational part is IQL.

Neuronal mechanisms of object recognition

Abstract: Recognition of objects from their visual images is a key function of the primate brain. This recognition is not a template matching between the input image and stored images like the vision in lower animals but is a flexible process in which considerable change in images, resulting from different illumination, viewing angle, and articulation of the object, can be tolerated. Recent experimental findings about the representation of object images in the inferotemporal cortex, a brain structure that is thought to be essential for object vision, are summarized and discussed in relation to the computational frames proposed for object recognition.