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This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643., This paper shows how a good internal model of the subject viewed aids in the visual recognition and following of key parts. The role of knowledge driven top-down tools and methods is shown by recognizing a series of human figures drawn from Eadward Muybridge's collection of 1887. Knowledge of the subject's structure and actions are used to find the head, shoulder, elbow, hip, knees, and ankles of the subject., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research. contract N88814-75C-8643-8885., This paper describes a simple method of converting visual coordinates to arm coordinates which does not require knowledge of the position of the camera(s). Comparisons are made to other methods and two camera, three dimensional extensions are discusssed. The single camera method for converting points on a tabletop is used by Marc Raibert and Glen Speckert in a working hand-eye system which recognizes objects and picks them up under visual guidance. This was implemented on the MIT Micro-Automation PDP 11/45 using a low speed vidicon and a Scheinman arm., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75C-0643., This paper describes the progress made towards tracking an object visually using a PIN diode attached to a dual mirror deflection system which enables the PIN diode to "optically point" to any position in two-space. A helium neon laser equipted with a similar mirror deflection system was used to point at the object being tracked. Actual objects tracked include a hand, a bouncing ping pong ball, and a white center on a black target attached to a moving metronome., MIT Artificial Intelligence Laboratory

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643., This paper shows how a good internal model of the subject viewed aids in the visual recognition and following of key parts. The role of knowledge driven top-down tools and methods is shown by recognizing a series of human figures drawn from Eadward Muybridge's collection of 1887. Knowledge of the subject's structure and actions are used to find the head, shoulder, elbow, hip, knees, and ankles of the subject., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75C-0643., This paper describes the progress made towards tracking an object visually using a PIN diode attached to a dual mirror deflection system which enables the PIN diode to "optically point" to any position in two-space. A helium neon laser equipted with a similar mirror deflection system was used to point at the object being tracked. Actual objects tracked include a hand, a bouncing ping pong ball, and a white center on a black target attached to a moving metronome., MIT Artificial Intelligence Laboratory

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75C-0643., This paper describes the progress made towards tracking an object visually using a PIN diode attached to a dual mirror deflection system which enables the PIN diode to "optically point" to any position in two-space. A helium neon laser equipted with a similar mirror deflection system was used to point at the object being tracked. Actual objects tracked include a hand, a bouncing ping pong ball, and a white center on a black target attached to a moving metronome., MIT Artificial Intelligence Laboratory

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643., This paper shows how a good internal model of the subject viewed aids in the visual recognition and following of key parts. The role of knowledge driven top-down tools and methods is shown by recognizing a series of human figures drawn from Eadward Muybridge's collection of 1887. Knowledge of the subject's structure and actions are used to find the head, shoulder, elbow, hip, knees, and ankles of the subject., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research. contract N88814-75C-8643-8885., This paper describes a simple method of converting visual coordinates to arm coordinates which does not require knowledge of the position of the camera(s). Comparisons are made to other methods and two camera, three dimensional extensions are discusssed. The single camera method for converting points on a tabletop is used by Marc Raibert and Glen Speckert in a working hand-eye system which recognizes objects and picks them up under visual guidance. This was implemented on the MIT Micro-Automation PDP 11/45 using a low speed vidicon and a Scheinman arm., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

This paper describes a three phase project concerning the watchin, analyzing, and describing of motions of a cat in various gaits. All data is based on two 16mm films of an actual cat moving on a treadmill. In phase I, the low level issues of tracking key points on the cat from frame to frame are discussed. Phase II deals with building and using a graphics tool to analyze the data of phase I. Pahse III is a high level discussion of cat locomotion based on the trajectories and movements explored by phase II.

This paper describes a three phase project concerning the watchin, analyzing, and describing of motions of a cat in various gaits. All data is based on two 16mm films of an actual cat moving on a treadmill. In phase I, the low level issues of tracking key points on the cat from frame to frame are discussed. Phase II deals with building and using a graphics tool to analyze the data of phase I. Pahse III is a high level discussion of cat locomotion based on the trajectories and movements explored by phase II.

This paper describes a three phase project concerning the watchin, analyzing, and describing of motions of a cat in various gaits. All data is based on two 16mm films of an actual cat moving on a treadmill. In phase I, the low level issues of tracking key points on the cat from frame to frame are discussed. Phase II deals with building and using a graphics tool to analyze the data of phase I. Pahse III is a high level discussion of cat locomotion based on the trajectories and movements explored by phase II.

This paper describes a three-phase project concerning the watching, analyzing, and describing of motions of a cat in various gaits. All data is based on two 16 mm films of an actual cat moving on a treadmill. In phase 1 the low level issues of tracking key points on the cat from frame to frame are discussed. Phase 2 deals with building and using a graphics tool to analyze the data of phase 1. Phase 3 is a high level discussion of cat locomotion based on the trajectories and movements explored by phase 2. (Author)