Topic
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.
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TL;DR: For certain classes that are particularly prevalent in the dataset, such as people, this work is able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
Abstract: With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
1,871 citations
TL;DR: The concept of anobject file as a temporary episodic representation, within which successive states of an object are linked and integrated, is developed, which develops the concept of a reviewing process, which is triggered by the appearance of the target and retrieves just one of the previewed items.
1,855 citations
TL;DR: An improved digital correlation method is presented for obtaining the full-field in-plane deformations of an object by numerically correlating a selected subset from the digitized intensity pattern of the undeformed object.
1,788 citations
Book•
01 Jan 1990
TL;DR: A complete implementation guide to a new requirements analysis technique, based on an object-oriented paradigm, offering numerous case studies and step-by-step examples.
Abstract: Introduction. 1. Improving Analysis with Object-Oriented Techniques. 2. Experiencing an Object Perspective. 3. Identifying Objects. 4. Identifying Structures. 5. Identifying Subjects. 6. Defining Attributes. 7. Defining Services. 8. Moving to Object-Oriented Design.
1,708 citations
08 Sep 2018
TL;DR: CornerNet as mentioned in this paper detects an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network.
Abstract: We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.1% AP on MS COCO, outperforming all existing one-stage detectors.
1,642 citations