Object (computer science)
About: Object (computer science) is a(n) research topic. Over the lifetime, 106024 publication(s) have been published within this topic receiving 1360115 citation(s). The topic is also known as: obj & Rq.
Papers published on a yearly basis
TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
Abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks. This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.
25 Dec 2006-ACM Computing Surveys
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Abstract: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
TL;DR: This paper introduces selective search which combines the strength of both an exhaustive search and segmentation, and shows that its selective search enables the use of the powerful Bag-of-Words model for recognition.
Abstract: This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html ).
01 Jan 1995
TL;DR: The object of the book is to produce a general, comprehensive textbook that treats all the essential core areas of cryptography.
Abstract: From the Publisher: The object of the book is to produce a general, comprehensive textbook that treats all the essential core areas of cryptography.
01 Jan 1990
TL;DR: This chapter discusses the development of Object-Oriented Programming Languages and the Structure of Complex Systems, and the role of Classification in this development.
Abstract: I. CONCEPTS. 1. Complexity. The Inherent Complexity of Software. The Structure of Complex Systems. Bringing Order to Chaos. On Designing Complex Systems. Sidebar: Categories of Analysis and Design Methods. 2. The Object Model. The Evolution of the Object Model. Elements of the Object Model. Applying the Object Model. Sidebar: Foundations of the Object Model. 3. Classes and Objects. The Nature of an Object. Relationships Among Objects. The Nature of a Class. Relationships Among Classes. The Interplay of Classes and Objects. On Building Quality Classes and Objects. Sidebar: Invoking a Method. 4. Classification. The Importance of Proper Classification. Identifying Classes and Objects. Key Abstractions and Mechanisms. Sidebar: A Problem of Classification. II. THE METHOD. 5 .The Notation. Elements of the Notation. Class Diagrams. State Transition Diagrams. Object Diagrams. Interaction Diagrams. Module Diagrams. Process Diagrams. Applying the Notation. 6 .The Process. First Principles. The Micro Development Process. The Macro Development Process. 7. Pragmatics. Management and Planning. Staffing. Release Management. Reuse. Quality Assurance and Metrics. Documentation. Tools. Special Topics. The Benefits and Risks of Object-Oriented Development. III. APPLICATIONS. 8. Data Acquisition: Weather Monitoring Station. Analysis. Design. Evolution. Maintenance. Sidebar: Weather Monitorint Station Requirements. 9. Frameworks: Foundation Class Library. Analysis. Design. Evolution. Maintenance. Sidebar: Foundation Class Library Requirements. 10. Client/Server Computing: Inventory Tracking. Analysis. Design. Evolution. Maintenance. Sidebar: Inventory Tracking System Requirements. 11. Artificial Intelligence Cryptanalysis. Analysis. Design. Evolution. Maintenance. Sidebar: Cryptanalysis Requirements. 12. Command and Control Traffic Management. Analysis. Design. Evolution. Maintenance. Sidebar: Traffic Management System Requirements. Afterword. Appendix: Object-Oriented Programming Languages. A.1 Concepts. A.2 Smalltalk. A.3 Object Pascal. A.4 C++. A.5 Common Lisp Object System. A.6 Ada. A.7 Eiffel. A.8 Other Object-Oriented Programming Languages. Notes. Glossary. Classified Bibliography. A. Classification. B. Object-Oriented Analysis. C. Object-Oriented Applications. D. Object-Oriented Architectures. E. Object Oriented Databases. F. Object-Oriented Design. G. Object-Oriented Programming. H. Software Engineering. I. Special References. J. Theory. K. Tools and Environments. Index. 0805353402T04062001
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