Showing papers by "Azriel Rosenfeld published in 1999"

Advances in Digital and Computational Geometry

Abstract: From the Publisher: Computational geometry deals with the construction of algorithms and their complexity related to problems in geometry arising in computer graphics, pattern recognition, robotics, image processing, CAD-CAM, VLSI design and geographic information systems. Digital geometry deals with geometric properties of subsets of digital images or, equivalently, with geometric properties of finite sets of lattice points. Digital geometry can anticipate progress in imaging technology allowing higher and higher spatial resolution. It seems that the input data in both fields will "converge" to data embedded in digital arrays of very high spatial resolution. This book covers the important developments in digital and computational geometry including methods of approximating geometric objects.

The Applicability of Green‘s Theorem to Computation of Rate of Approach

Abstract: The rate of approach (ROA) of a moving observer toward a scene point, as estimated at a given instant, is proportional to the component of the observer‘s instantaneous velocity in the direction of the point. In this paper we analyze the applicability of Green‘s theorem to ROA estimation. We derive a formula which relates three quantities: the average value of the ROA for a surface patch in the scenes a surface integral that depends on the surface slant of the patchs and the contour integral of the normal motion field around the image of the boundary of the patch. We analyze how much larger the ROA on the surface patch can be than the value of the contour integral, for given assumptions about the variability of the distance to points on the surface patch. We illustrate our analysis quantitatively using synthetic data, and we also validate it qualitatively on real image sequences.

Content-Based Access to Multimedia Information: From Technology Trends to State of the Art

Abstract: From the Publisher: The Microelectronics and Computer Technology Corporation (MCC) and its member companies carried out a study in 1997 to investigate the state of the art in technologies to annotate and manipulate large-scale networks of multimedia information objects with content-based concepts. Content-Based Access to Multimedia Information: From Technology Trends to State of the Art documents the study's technology assessment and identifies shortcomings where further research and integration of technologies are needed, to meet anticipated application requirements. The major points highlighted in this book can be used as cornerstones for defining advanced research and development directions and opportunities to exploit the content available in networks of large-scale multi-media sources.

Eye detection and tracking

Abstract: We can judge the importance of automatic detection and tracking of facial features, such as eyes, irises, eyelids, etc., by the list of potential applications. Some of these applications are in human-computer interaction; for instance, the computer may want to know what the user is looking at on the screen. Other applications can be found in compression techniques like MPEG4, where eye and iris information is part of the communication stream. Still another application can be found in driver behavior analysis. In this application, attentiveness on the driver's part is directly related to safety. For example, the car may want to know when the driver is tired and keeps closing his/her eyes; if the car finds that the driver is not attentive it may send visual or auditory signals to get the driver's attention. Of course, identifying and locating the face and the eyes are integral parts of most face recognition and classification algorithms, where eye information is primarily used to normalize the face. The contributions of this thesis are in two areas. The first is detection and verification of the face by locating the eyes in a color image of the face. The second is location of features of the eyes (the irises and eyelids) and identification of their behavior by tracking them over a sequence of images. The face is detected as a large flesh-colored region, and anthropometric data are then used to estimate the size and separation of the eyes. Our first method of eye detection uses a linear filtering approach applied to the gray-level image of the face; our second method uses nonlinear filters, applied to the color face image, to detect the corners of the eyes. Both methods were tested on two datasets. The first method had a good detection rate, but also gave many false alarms; the second method had a 90% detection rate with no false alarms. After locating the eye corners, we detect the eyelids and irises in every frame of an image sequence. We perform a frame-to-frame analysis of the movements of the irises and eyelids to determine changes in gaze direction and blinking, respectively. This analysis is improved upon by using motion information in the form of normal flow. We model the head and eyes and determine their respective motions. Using our models we determine the head-independent motions of the irises and eyelids by stabilizing for the head motion. The head-independent motions of the irises are used to determine behaviors like saccades and smooth pursuit. Tracking the upper eyelid and using the distance between its apex and the center of the iris, we detect instances of eye closure during blinking. In our experiments, we successfully located the irises in every frame in which the eye was fully or partially open. The eyelids were successfully located 80% of the time.

State of The Art: Informedia Digital Video Library

Abstract: This chapter provides a description of the most comprehensive searchable digital video library in the state of the art, the Informedia project at CMU Informedia,; Wactlar et al., 1996b. This project is comprehensive in terms of the amount of digital video that it has archived in various content searchable representations — Informedia currently has about 1,000 hours (1 terabyte) of video that has been automatically processed by integrated speech, image, and language understanding technologies. In its efforts to create an automatically processed and large-scale video collection, Informedia is not as ambitious, in terms of the content analysis, as the technologies “on the horizon” described in the previous chapter. In terms of the CBAM technical components (Figure 2.6), Informedia represents the state of the art for content representations offering semantic analysis of the content in large video repositories. It is a testament to the immaturity of video content management that Informedia can only offer fairly shallow exploitation of the content in video repositories, as will be described in this chapter.