Analysis of Image Sequence Data with Applications to Two-Dimensional Echocardiography

Digital two-dimensional echocardiography is an ultrasonic imaging technique that is used as an increasingly important noninvasive technique in the comprehensive characterization of the left ventricular structure and function. Quantitative analysis often uses heart wall motion and other shape attributes such as the heart wall thickness, heart chamber area, and the variation of these attributes throughout the cardiac cycle. These analyses require the complete determination of the heart wall boundaries. Poor image quality and large amount of noise makes computer detection of the boundaries difficult. An algorithm to detect both the inner and outer heart wall boundaries is presented. The algorithm was applied to images acquired from animal studies and from a tissue equivalent phantom to verify the performance. Different approaches to exploiting the temporal redundancy of the image data without making use of results from image segmentation and scene interpretation are explored. A new approach to perform image flow analysis is developed based on the Total Least Squares method. The result of this processing is an estimate of the velocities in the image plane. In an image understanding system, information acquired from related domains by other sensors are often useful to the analysis of images. Electrocardiogram signals measure the change of electrical potential changes in the heart muscle an d provide important information such as the timing data for image sequence analysis. These signals are frequently plagued by impulsive muscle noise and background drift due to patient movement. A new approach to solving these problems is presented using mathematical morphology. Experiments addressing various aspects of the problem, such as algorithm performance, choice of operator parameters, and response to sinusoidal inputs, are reported

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Embedded Real Time Gesture Tracking

Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm evaluates the video frames and outputs the location of moving targets within the video frame

Temporal Mapping of Surveillance Video for Indexing and Summarization

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created

Life-like Image Processing for Small Target Motion Detection in Cluttered Dynamic Environments

Discriminating targets moving against a cluttered background is a huge challenge for future robotic vision systems, let alone detecting a target as small as one or a few pixels. As a source of inspiration, insects are quite apt at searching for mates and tracking prey – which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Some of the STMDs have also demonstrated direction selectivity which means these STMDs respond strongly only to their preferred motion direction. Build a quantitative STMD model is the first step for not only further understanding of the biological visual system, but also providing robust and economic solutions of small target detection for an artificial visual system. This research aims to explore STMD-based image processing methods for small target motion detection against cluttered dynamic backgrounds. The major contributions are summarized as follows. Three STMD-based neural models are proposed in this research named as directionally selective STMD(DSTMD), STMD Plus and Feedback STMD, respectively. The DSTMD systematically models and studies direction selectivity of the STMD neurons, meanwhile provides with unified and rigorous mathematical description. Specifically, in the DSTMD, a new correlation mechanism is introduced for direction selectivity via correlating signals relayed from two pixels. Then, a lateral inhibition mechanism is implemented on the spatial field for size selectivity of the STMD neurons. Finally, a population vector algorithm is used to encode motion direction of small targets. Extensive experiments showed that the proposed DSTMD not only is in accord with current biological findings, i.e. showing directional preferences, but also works reliably in detecting small targets against cluttered backgrounds. The STMD Plus is developed to discriminate small targets from small-target-like background features (named as fake features) by integrating motion information with directional contrast. More precisely, the STMD Plus is composed of four subsystems – ommatidia, motion pathway, contrast pathway and mushroom body. Compared to existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination. The experimental results demonstrated the significant and consistent improvements of the proposed visual system model over existing STMD-based models against fake features. The Feedback STMD is also designed to filter out fake features by introducing a new feedback mechanism. Specifically, the model output is first temporally delayed then applied to the previous neural layer to construct a feedback loop. By subtracting the feedback signal from the inputs of the STMDs, the background fake features are largely suppressed. Experimental results show that the developed feedback neural model achieves better performance than the existing STMD-based models in discriminating small targets from complex backgrounds