A Novel Algorithm for Moving Target Detection

Image subtraction by phase encoding pseudocolor technique

The purpose of this study was to assess the advantages of the image subtraction technique in chemical shift MRI for the differentiation of adrenal adenomas from nonadenomas. Thirty-five patients with 42 adrenal masses (eight metastases and 34 nonfunctioning adenomas) underwent chemical shift MRI using a double-echo fast low-angle shot sequence. Subsequently, opposed-phase chemical shift MR images were subtracted from in-phase images. The subtraction images were assessed quantitatively and qualitatively. For quantitative assessment, the signal intensity values of the adrenal masses were measured by one investigator with manually defined regions of interest. Qualitative assessment of the subtraction images was performed independently by two investigators, who reported their confidence in diagnosing adenomas versus nonadenomas based on signal intensity of the adrenal masses on subtraction images. The mean signal intensities were significantly different between adenomas and metastases on subtraction images (213 vs 18; p < 0.0001). There was no overlap in signal intensities between adenomas and metastatic tumors. The accuracy in distinguishing adenomas from metastatic tumors was 100% if the cutoff value of the signal intensity selected was 36-106. Quantitative results corresponding to 100% specificity were also observed, with similar sensitivity. No difference in interpretation between the two investigators occurred. Chemical shift subtraction MRI provides a high confidence level in distinguishing adrenal adenomas from adrenal metastases. The image subtraction technique also facilitates quantitative and qualitative evaluation of adrenal masses in chemical shift MRI.

Image subtraction techniques are powerful tools for detecting small changes in medical images, aerial pictures, and other scenes. By employing suitable optoelectronic devices, the incoherent subtraction technique can provide a fast method for image detection with simultaneous difference processing. Here we propose an incoherent technique of image subtraction for rendering visible in a linear fashion the first derivative of the phase structure. Our procedure consists of subtracting incoherently two images of the same phase object. Each image is spatial filtered by different methods, and it is recorded independently. The spatial filters are novel.

Background/Objectives: In this paper, a tracking method that is based on the colors and contours is proposed to obtain results that are superior to those of the traditional methods for the tracking of the objects in video. Methods/Statistical Analysis: The proposed method requires an initial conversion of the existing RGB-color-based video in the HSV color space, and a back-projection histogram in the HSV color space is used to obtain the color information of the object region that is to be tracked. The contour information is also obtained to track the target based on the Canny algorithm, whereby the moving object in the video is tracked in real time on the basis of the color and contour information. Findings: An experiment was performed wherein the proposed method was compared with the Mean-shift tracking method, the Camshaft tracking method, the Kalman-filter tracking method, and the Particle-filter tracking method in terms of the objective of this paper. The comparison showed that the proposed method is more accurate regarding the tracking of a single object in video. Improvements/Applications: This improved tracking method will be utilized in object tracking, CCTV for car tracking and HCI.

Experimental measurements of shock wave propagation from explosions of C4 are presented. Each test is recorded with a high-speed digital video camera and the shock wave is visualized using background-oriented schlieren (BOS). Two different processing techniques for BOS analysis are presented: image subtraction and image correlation. The image subtraction technique is found to provide higher resolution for identifying the location of a shock wave propagating into still air. The image correlation technique is more appropriate for identifying shock reflections and multiple shock impacts in a region with complex flow patterns. The optical shock propagation measurements are used to predict the peak overpressure and overpressure duration at different locations and are compared to experimental pressure gage measurements. The overpressure predictions agree well with the pressure gage measurements and the overpressure duration prediction is within an order of magnitude of the experimental measurements. The BOS technique is shown to be an important tool for explosive research which can be simply incorporated into typical large-scale outdoor tests.

Contrast-medium-enhanced digital mammography (CEDM) is an image subtraction technique which might help unmasking lesions embedded in very dense breasts. Previous works have stated the feasibility of CEDM and the imperative need of radiological optimization. This work presents an extension of a former analytical formalism to predict contrast-to-noise ratio (CNR) in subtracted mammograms. The goal is to optimize radiological parameters available in a clinical mammographic unit (x-ray tube anode/filter combination, voltage, and loading) by maximizing CNR and minimizing total mean glandular dose (D(gT)), simulating the experimental application of an iodine-based contrast medium and the image subtraction under dual-energy nontemporal, and single- or dual-energy temporal modalities. Total breast-entrance air kerma is limited to a fixed 8.76 mGy (1 R, similar to screening studies). Mathematical expressions obtained from the formalism are evaluated using computed mammographic x-ray spectra attenuated by an adipose/glandular breast containing an elongated structure filled with an iodinated solution in various concentrations. A systematic study of contrast, its associated variance, and CNR for different spectral combinations is performed, concluding in the proposal of optimum x-ray spectra. The linearity between contrast in subtracted images and iodine mass thickness is proven, including the determination of iodine visualization limits based on Rose's detection criterion. Finally, total breast-entrance air kerma is distributed between both images in various proportions in order to maximize the figure of merit CNR2/D(gT). Predicted results indicate the advantage of temporal subtraction (either single- or dual-energy modalities) with optimum parameters corresponding to high-voltage, strongly hardened Rh/Rh spectra. For temporal techniques, CNR was found to depend mostly on the energy of the iodinated image, and thus reduction in D(gT) could be achieved if the spectral energy of the noniodinated image is decreased and the breast-entrance air kerma is evenly distributed between both acquisitions. Predicted limits, in terms of iodine concentration, are found to guarantee the visualization of common clinical angiogenic concentrations in the breast.

The performance of a visual speech recognition technique is greatly influenced by the choice of visual speech features. Speech information in the visual domain can be generally categorized into static (mouth appearance) and motion (mouth movement) features. This chapter reviews a number of computer-based lip-reading approaches using motion features. The motion-based visual speech recognition techniques can be broadly categorized into two types of algorithms: optical-flow and image subtraction. Image subtraction techniques have been demonstrated to outperform optical-flow based methods in lip-reading. The problem with image subtraction-based method using difference of frames (DOF) is that these features capture the changes in the images over time, but do not indicate the direction of the mouth movement. New motion features to overcome the limitation of the conventional image subtraction-based techniques in visual speech recognition are presented in this chapter. The proposed approach extracts features by applying motion segmentation on image sequences. Video data are represented in a 2-D space using grayscale images named as motion history images (MHI). MHIs are spatio-temporal templates that implicitly encode the temporal component of mouth movement. Zernike moments are computed from MHIs as image descriptors and classified using support vector machines (SVMs). Experimental results demonstrate that the proposed technique yield a high accuracy in a phoneme classification task. The results suggest that dynamic information is important for visual speech recognition.Request access from your librarian to read this chapter's full text.

The aims of this analysis were to: (1) determine the value of ictal SPECT in the localization of neocortical epileptogenic foci, (2) evaluate the relationships between the results of ictal SPECT and other potential affecting factors, and (3) compare traditional visual analysis and the subtraction method. We retrospectively analyzed 81 consecutive patients with neocortical epilepsy who underwent epilepsy surgery and achieved a favourable surgical outcome, including 36 patients with normal MRI. Side-by-side visual analysis and subtraction images were classified as correctly localizing,correctly lateralizing, or non-localizing/non-lateralizing images according to the resected lobe. Side-by-side visual analysis and subtraction SPECT correctly localized the epileptogenic lobe in 58.9% and 63.0% of patients, respectively. The two methods were complementary and the diagnostic sensitivity of ictal SPECT using the two methods was 79.0%. Ictal SPECT using the visual method correctly localized the epileptogenic lobe more frequently in patients with a localizing pattern of ictal scalp EEG at the time of radioligand injection. When using subtraction images, an injection delay of less than 20 s after seizure onset was significantly correlated with correct localization. The subtraction method was superior to the visual method for localizing frontal lobe epilepsy (FLE) and parietal lobe epilepsy (PLE), and in patients with non-localizing/non-lateralizing EEG at onset. Ictal SPECT analyses using visual and subtraction methods are useful and complementary for the localization of the epileptogenic foci of neocortical epilepsy. Early radioligand injection and ictal EEG patterns are related to ictal SPECT localization. The subtraction method may be more useful in some epileptic syndromes.

The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.

Fire and smoke are common sight at mines and could be catastrophic for humans working there. Our paper focuses on an alert mechanism that is built based on detection and validation of smoke from mines. We propose a computer vision based model that is robust enough to detect presence of smoke and the direction of motion of smoke. This research paper proposes a smoke detection model integrated with Unmanned Aerial Vehicle (UAV). The model focuses on smoke detection that leads to early detection of fire. The model works on the basis of image processing. The distinguishing feature of our model is the dynamic and real time processing. Here the system tracks the dynamic environment in the mining areas using UAVs featured with cameras. The real time captured video frames are calibrated to overcome distortion considering the radial and tangential factors. The calibrated frame thus obtained is further processed to get the Region of Interest (ROI) based on the spatial Hue Saturation Value (HSV) colouring model. Each pixel of the frame is then compared with colour range of smoke using opencv functions. The matched ROI is de-noised using Non-local means De-nosing algorithm to avoid unnecessary colours. These filtered frames are then processed concurrently to identify the pattern of change in motion. Based on this pattern the ROI is classified as either smoke or no smoke. With the analysis of turbulence in each pixel the intensity of smoke is detected. The experiment is conducted with-1) Prerecorded smoke video clippings. 2) Real time video captured in the camera. The results turned out to be positive in both the test cases. So, the model works quite efficiently with both recorded and real time video. Key Terms: UAV, ROI, HSV, De-noising algorithm, pattern matching

In the new MPEG-4 video coding standard, automatic video object segmentation plays a key role in supporting object-oriented coding and enabling content-based functionalities. Background subtraction is one of the basic automatic video object segmentation methods. But various environmental illumination conditions often make it hard to work. A robust background subtraction method is presented in this paper. A statistical background model is first setup in this algorithm. Then the hypothesis testing is applied to the following frames to segment the video objects. The HSV color model is used and its color components are efficiently analyzed and treated separately so that the proposed algorithm can adapt to different environmental illumination conditions. Shadows are detected and a new background update algorithm is also presented based on the observation that the illumination changes are temporal and will not influence all the following frames. All of them contribute to the robustness of the method. The experimental results show that the proposed background subtraction method can automatically segment video objects robustly and accurately in various illuminating environments.

The work presented in this paper describes a novel algorithm for aut omatic video object tracking based on a process of subtraction of successive frames, where the prediction of the direction of movement of the object being tracked is carried out by analyzing the changing areas generated as result of the object’s motion, specifically in regions of interest defined inside the object being tracked in both the current and the next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of the object being tracked in the next frame usi ng a function which measures the grade of dissimilarity between the region of interest defined inside the object being tracked in the current frame and a moving region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on the process of subtraction of successive frames. Finally, the location of the moving region of interest in the next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of the object being trackedin the next frame. On the other hand, it is also designed a testing platform which is used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These virtual scenarios are exposed to heavily cluttered conditions w here areas which surround the object being tracked present a high variability. The results obtained with the proposed algorithm show that the tracking process was successfully carried out in a set of virtual scenarios under different challenging conditions .

Remote laboratories are an inevitable necessity for Internet enabled education in Science, Technology, Engineering, and Math (STEM) fields due to their effectiveness, flexibility, cost savings, and the fact that they provide many benefits to students, instructors and researchers. Accordingly, real-time experiment live video streaming is an essential part of remote experimentation operation. Nevertheless, in the development of real-time experiment video transmission, it is a key and difficult issue that the video is transferred across the network firewall in most of the current remote laboratory solutions. To address the network firewall issue, we developed a complete novel solution via HTTP Live Streaming (HLS) protocol and FFMPEG that is a powerful cross-platform command line video trans-code/encoding software package on the server side. In this paper, a novel, real-time video streaming transmission approach based on HLS for the remote laboratory development is presented. With this new solution, the terminal users can view the real-time experiment live video streaming on any portable device without any firewall issues or the need for a third party plug-in., This new solution also significantly benefits remote laboratory development in the future.

Gravitational lensings provide unique opportunities to study distant galaxies. Despite their usefulness, only a dozens of gravitational arcs have been reported up-to-date, hampered by the low resolution imaging from ground-based surveys. We propose a novel method to identify Einstein rings/arcs in the IFU surveys via image subtraction and computer vision techniques. We use the recent discovered strong lens system in the MaNGA survey as a proof-of-concept; in addition to MaNGA, our approach is applicable to other IFU surveys, such as SAMI, CALIFA, Hector, DESI, and 4MOST.

To improve the early detectability of gastrointestinal (GI) bleeding, we have developed a new subtraction scintigraphic technique using in vivo labeled 99mTc red blood cells (RBC). Use of this new sequential subtraction method every 5 min makes it possible to detect early the bleeding site and calculate the bleeding rate. In phantom experiments, focal activity was detected on subtraction images at pump rates of more than 0.20 ml/min at any urethane thickness, but was negative on conventional scintigrams. The calculated pump rate by the subtraction method correlated well with the actual syringe pump rate (y = 1.07x - 0.02, r = 0.99, p < 0.01) in the case of an 8-cm urethane board as an abdominal wall. Twenty-four of 50 patients with suspected GI bleeding were confirmed to be positive within 24 hr. The subtraction method showed 15 true positive and 2 false positive cases within 60 min. On the other hand, the conventional scintigraphic method showed only 7 true positive and 1 false positive cases. Sensitivity of the subtraction method was 62.5%, which was significantly higher (p < 0.05) than that of the conventional scintigraphic method (29.2%). These results suggest that sequential subtraction scintigraphy with 99mTc-RBC is an effective method for the early detection of GI bleeding and shortening the examination time as compared with conventional scintigraphy.