Video Image Sequences Research Articles

A scene segmenter; visual tracking of moving vehicles

In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described. ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion. The moving objects are tracked, and their outlines are accurately estimated. The ASSET system provides a useful source of world information, for example, in the area of autonomous vehicle guidance.

人体の動作解析に対するビデオセオドライトの利用

Video image sequences often give important information about the dynamics of human motion in the field of sports, training or rehabilitation. In understanding of the dynamics of human motion from general TV or video image sequences, there are two complicated subjects. One is image processing, for example automated recognition of features on the human body, such as e.g. the head, elbows or knees. The second is how to estimate the photogrammetric camera orientation parameters on a moving camera.This paper describes the development of a video theodolite system, consisting of a CCD camera, a theodolite and a video recorder. The system makes it possible to record image data with a moving camera and simultaneously determine the camera rotation parameters in real time.The authors first analyzed the dynamics of the sprinter Carl Lewis and that of boat rowing by using TV images. In both these cases however, the camera orientation parameters could be determined by utilizing fixed information in the images such as the goal line and course lines in the first case, and the buoys marking the rowing course line in the second case. But often, there exist no such fixed information in the images why the authors concentrated on developing a system where the camera orientation parameters could be determined in real time while recording a moving object.In order to measure the rotation parameters, a CCD camera was mounted on top of a theodolite.ωand κwere defined as the horizontal and vertical angles respectively, φalways being 0 degrees as the theodolite was levelled. The current values of the parameters were then superimposed continuously on the image frames and thus recorded as a part of the image data.

A Scene Segmenter; Visual Tracking of Moving Vehicles

In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described. ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion. The moving objects are tracked, and their outlines are accurately estimated. The ASSET system will provide a useful source of world information, for example, in the area of autonomous vehicle guidance.

An adaptive hybrid DPCM/DCT method for video coding

Discrete cosine transform (DCT) is a very popular coding algorithm for high compression ratio bandwidth reduction of digital video image sequences. Basically, DCT converts statistically dependent picture elements (pixels) into independent coefficients. The results are usually energy concentrated in only a few of the coefficients containing the main part of the picture information which can be quantized and run-length encoded. This concept is, however, not easy to apply when the transform pixel block is small in size and contains an edge boundary of an object. Transformation of such an edge block will not lead to an efficient compaction of signal energy. In this paper, an adaptive hybrid DPCM/DCT coder for video compression is presented. Having avoided using DCT coding for the edge blocks, the adaptive coder which instead uses a DPCM method on these edge blocks has reduced the ringing effects (corona effects) at the edges usual to transform coding method.

A system for sequential step detection with application to video image processing

A method for detecting the occurrence of an abrupt steplike change in a time sequence of video images is presented. A single-pole recursive high-pass filter cascaded with a moving average filter processes the input data to remove the quiescent background level and accumulate a sustained change in amplitude. The absolute value of the output is compared to a threshold to decide whether a steplike change in signal amplitude has occurred. It is shown that, for a given cutoff frequency of the high-pass filter, an optimal value exists for the number of terms in the moving average. Considerations for implementation of the algorithm on practical image processors are discussed. The results of numerical and laboratory experiments are presented that verify the effectiveness of the method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Differentiator-type three-dimensional recursive ladder filters having frequency-planar- or frequency-beam-shaped passbands

By exploring parallelism, novel three-dimensional (3-D) recursive ladder filter structures for implementing a class of 3-D frequency-planar and 3-D frequency-beam filters are developed. These structures use discrete differentiators and are suitable for high-speed real-time processing of video image sequences.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Detecting small, moving objects in image sequences using sequential hypothesis testing

An algorithm is proposed for the solution of the class of multidimensional detection problems concerning the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images. A large number of candidate trajectories, organized into a tree structure, are hypothesized at each pixel in the sequence and tested sequentially for a shift in mean intensity. The practicality of the algorithm is facilitated by the use of multistage hypothesis testing (MHT) for simultaneous inference, as well as the existence of exact, closed-form expressions for MHT test performance in Gaussian white noise (GWN). These expressions predict the algorithm's computation and memory requirements, where it is shown theoretically that several orders of magnitude of processing are saved over a brute-force approach based on fixed sample-size tests. The algorithm is applied to real data by using a robust preprocessing procedure to eliminate background structure and transform the image sequence into a residual representation, modeled as GWN. Results are verified experimentally on a variety of video image sequences.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Analysis of video image sequences using point and line correspondences

In this research, we investigate the problem of analyzing time-varying imagery using a feature-based approach. We assume a scenario where the imaged objects remain stationary while the camera moves. The goal is to compute the structure of the imaged objects and the motion of the camera from a sequence of video images. The proposed method exploits the principle of the invariance of rigid configurations during motion. Using the rigidity constraints, we specify equations based on distance and angular invariance to compute the structural parameters of the imaged 3D objects independently of the camera's motion. Once the structural parameters are recovered, the motion parameters can be computed. The strength of our approach is in the decomposition of the computations of structure and motion, and in the use of point and line correspondences simultaneously. The main advantage of using both points and lines is that our approach is not limited to objects whose images have only a particular type of feature in abundance. In this research, we consider the special case of four points and one line, which are the minimum feature sets required for computing the structure and motion parameters. We then consider additional features in an overdetermined system of equations to improve the reliability and accuracy of the computation. We present computer simulation results, as well as results from real image sequences, to demonstrate the algorithm's validity.

Global tracking of the ocular fundus pattern imaged by scanning laser ophthalmoscopy

This paper presents an algorithm for the automatic global tracking of ocular fundus landmarks in video image sequences generated by scanning laser ophthalmoscopy (SLO). The tracking algorithm is based on the computation of the discrete unnormalized cross-correlation of an interactively preselected small template and respective images in time sequences of fundus patterns. The correlation is executed with binary images derived from an automatical threshold limitation of the grey-value images. Due to the { +1 −1 } representation of the binary images the unnormalized correlation functions directly relate to the Hamming distance of the template and the objects in the images. Experiments show that even templates with features distorted by noise are accurately recognized at any position. The accuracy of position detection is better than 0.4%. Possible hardware implementations of the algorithm which would reduce computation time are briefly mentioned.

Image motion estimation by clustering

AbstractImage motion is estimated by matching feature “interest” points in different frames of video image sequences. The matching is based on local similarity of the displacement vectors. Clustering in the displacement vector space is used to determine the set of plausible match vectors. Subsequently, a similarity‐based algorithm performs the actual matching. The feature points are computed using a multiple‐filter image decomposition operator. The algorithm has been tested on synthetic as well as real video images. The novelty of the approach is that it handles multiple motions and performs motion segmentation.

Semiautomated Border Tracking of Cine Echocardiographic Ventnrcular Images

Two-dimensional ultrasound sector scans of the left ventricle (LV) are commonly used to diagnose cardiac mechanical function. Present quantification procedures of wall motion by this technique entail inaccuracies, mainly due to relatively poor image quality and the absence of a definition of the relative position of the probe and the heart. The poor quality dictates subjective determination of the myocardial edges, while the absence of a position vector increases the errors in the calculations of wall displacement, LV blood volume, and ejection fraction. An improved procedure is proposed here for automatic myocardial border tracking (AMBT) of the endocardial and epicardial edges in a sequence of video images. The procedure includes nonlinear filtering of whole images, debiasing of gray levels, and location-dependent contrast stretching. The AMBT algorithm is based upon tracking movement of a small number of predefined set of points, which are manually defined on the two myocardial borders. Information from one image is used, by utilizing predetermined statistical criteria to iteratively search and detect the border points on the next one. Border contours are reconstructed by Spline interpolation of the border points. The AMBT procedure is tested by comparing processed sequences of cine echocardiographic scan images to manual tracings by an objective observer and to results from previously published data.

Automated measurement of ciliary beat frequency.

Measurements of ciliary beat frequency using video images are dependent on observer interpretation. To obtain objective estimates of ciliary beat frequency from video-image sequences, a computer-based method was developed. Regions of interest of video-image sequences were selected and digitized. Variations in numerical values representing light intensity resulting from cilia beating were extracted and analyzed using autocorrelation techniques. The ciliary beat frequencies obtained for 14 in vitro experiments on ciliated cells or epithelium from the frog palate (Rana catesbeiana) over the range of frequencies 2-25 Hz correlated well with independent observer measurements (r = 0.979). The addition of such computer-based methods to video observer-based systems allows more objective and efficient determinations of ciliary beat frequency.