Dynamic Scene Analysis Research Articles

Understanding blood cell motion

The main function of the surface of a blood cell is to receive information from the environment. Experiments have indicated that the cell membrane plays a vital role in the life, development, and regulation of cells. There is, however, no existing method to quantify the observable changes in membrane shape that occur in locomotion. The main goal of this research is to develop an image interpretation system capable of analyzing the structural changes in the morphology of cells from a sequence of pictures, using automatic techniques of image processing. A model for a general dynamic scene analysis system is described. It consists of three basic entities: dynamic data, static data, and a collection of analysis processes. Based on this model, a rule-based image interpretation system for moving cells has been implemented. The system consists of different cooperating computational processes, which interact with two common memories, a short term memory (STM) and a long term memory (LTM), The STM contains a dynamic record of the instantaneous cell motion, shape, and structural changes, as well as the current global description of the cell behavior. The LTM data arc static, and are implemented as rules. These describe the general model of the morphology of the cells under analysis, as well as control information pertinent to the computational processes. The latter are activated by the control rules throughout the three hierarchical analysis stages: static, incremental, and global. They interact through the STM using the information stored in the LTM, until a complete description of the dynamic cell motion and morphology is obtained. The system has been successfully employed to analyze and study the pseudopod kinetics of white blood cells.

Volumetric descriptions from dynamic scenes

A dynamic scene analysis system for the derivation of volumetric descriptions is presented. The description is referred to as the ‘volume segment’ representation. The structures that implement the representation are described along with several techniques to modify and use the representation.

A Pipelined Pseudoparallel System Architecture for Real-Time Dynamic Scene Analysis

In this paper we introduce the concept of pseudoparallelism, in which the serial algorithm is partitioned into several noninteractive independent subtasks so that parallelism can be used within each subtask level. This approach is illustrated by applying it to a real-time dynamic scene analysis. Complete details of such a pseudoparallel architecture with an emphasis to avoid interprocessor communications have been worked out. Problems encountered in the course of designing such a system with a distributed operating system (no master control) have been outlined and necessary justifications have been provided. A scheme indicating various memory modules, processing elements, and their data-path requirements is included and ways to provide continuous flow of partitioned information in the form of a synchronized pipeline are described.

An approach to optimal threshold selection on a sequence of two-dimensional echocardiographic images.

In our laboratory, thresholding has been found to have a remarkable effect on the detection of moving boundaries in two-dimensional echocardiographic images (2D echo). In this note, a technique for selection of an optimum threshold for a sequence of 2D echo frames by means of counting the maximum number of moving points in two consecutive frames is explored. This threshold is applied to the sequence of images to convert them to binary form prior to dynamic scene analysis. Experimental results of threshold images are shown with 2D echos obtained from two patients.

NATO Advanced study institute on image sequence processing and dynamic scene analysis

NATO Advanced study institute on image sequence processing and dynamic scene analysis

NATO Advanced study institute on image sequence processing and dynamic scene analysis

NATO Advanced study institute on image sequence processing and dynamic scene analysis

Special issue of computer graphics and image processing

A special issue of Computer Graphics and Image Processing on Motion and Time-Varying Imagery is planned for 1982. Professor J.K. Aggarwal will be the guest editor for the special issue. The issue will address both theoretical and experimental issues related to time-varying imagery including dynamic scene analysis, real-time processing of imagery, applications of optic flow to motion problems, and the determination of structure from multiple sensor images. Authors are invited to submit three copies of their papers by February 28, 1982 to the guest editor.

Dynamic Scene Analysis Using Pixel-Based Processes

Because they ignore stationary scene components, difference pictures provide a simple, robust method for detecting motion in the early stages of dynamic scene analysis.

Correspondence processes in dynamic scene analysis

One of the fundamental problems in dynamic scene analysis is the tracking of objects from frame to frame. A general approach to tracking is to establish correspondences between points, or sets of points, between frames and then group the sets into objects based upon similarity of motion. This paper will focus on processes for establishing the correspondence between sets of points in successive frames. A succession of correspondence processes are discussed, based on the factors which contribute to the complexity of the correspondence problem.

Computer analysis of dynamic scenes containing curvilinear figures

A brief introduction to dynamic scene analysis is given, followed by a description of a system which analyzes sequences of images of moving figures. The methods used to describe the boundaries of the figures, to match common shapes between paired images, and then to analyze the motion of matched figures, are discussed in detail. Finally, a complete example is presented along with some directions for further research.

Dynamic scene analysis

The three major components of dynamic scene analysis, namely segmentation, occlusion and the computation of three-dimensional information from images are discussed in depth. Segmentation refers to the process of determining features of interest, occlusion analysis includes the deriving of changes due to projection perspective, and computation of three-dimensional information entails the constructing of structural models and describing motions from image information. The purpose of the review is to give the reader a coherent view of the issues and the manner in which researchers are currently addressing these issues. Detailed descriptions of the systems developed at The University of Texas are presented.