Image Background Subtraction Research Articles

Technical note: Visual, rapid, scintillation point dosimetry for in vivo MV photon beam radiotherapy treatments.

While careful planning and pre-treatment checks are performed to ensure patient safety during external beam radiation therapy (EBRT), inevitable daily variations mean that in vivo dosimetry (IVD) is the only way to attain the true delivered dose. Several countries outside the US require daily IVD for quality assurance. However, elsewhere, the manual labor and time considerations of traditional in vivo dosimeters may be preventing frequent use of IVD in the clinic. This study expands upon previous research using plastic scintillator discs for optical dosimetry for electron therapy treatments. We present the characterization of scintillator discs for in vivo x-ray dosimetry and describe additional considerations due to geometric complexities. Plastic scintillator discs were coated with reflective white paint on all sides but the front surface. An anti-reflective, matte coating was applied to the transparent face to minimize specular reflection. A time-gated iCMOS camera imaged the discs under various irradiation conditions. In post-processing, background-subtracted images of the scintillators were fit with Gaussian-convolved ellipses to extract several parameters, including integral output, and observation angle. Dose linearity and x-ray energy independence were observed, consistent with ideal characteristics for a dosimeter. Dose measurements exhibited less than 5% variation for incident beam angles between 0° and 75° at the anterior surface and 0-60 at the posterior surface for exit beam dosimetry. Varying the angle between the disc surface and the camera lens did not impact the integral output for the same dose up to 55°. Past this point, up to 75°, there is a sharp falloff in response; however, a correction can be used based on the detected width of the disc. The reproducibility of the integral output for a single disc is 2%, and combined with variations from the gantry angle, we report the accuracy of the proposed scintillator disc dosimeters as ±5.4%. Plastic scintillator discs have characteristics that are well-suited for in vivo optical dosimetry for x-ray radiotherapy treatments. Unlike typical point dosimeters, there is no inherent readout time delay, and an optical recording of the measurement is saved after treatment for future reference. While several factors influence the integral output for the same dose, they have been quantified here and may be corrected in post-processing.

Fraction of Clumpy Star-forming Galaxies at 0.5 ≤ z ≤ 3 in UVCANDELS: Dependence on Stellar Mass and Environment

High-resolution imaging of galaxies in rest-frame UV has revealed the existence of giant star-forming clumps prevalent in high-redshift galaxies. Studying these substructures provides important information about their formation and evolution and informs theoretical galaxy evolution models. We present a new method to identify clumps in galaxies’ high-resolution rest-frame UV images. Using imaging data from CANDELS and UVCANDELS, we identify star-forming clumps in an HST/F160W ≤ 25 AB mag sample of 6767 galaxies at 0.5 ≤ z ≤ 3 in four fields, GOODS-N, GOODS-S, EGS, and COSMOS. We use a low-passband filter in Fourier space to reconstruct the background image of a galaxy and detect small-scale features (clumps) on the background-subtracted image. Clumpy galaxies are defined as those having at least one off-center clump that contributes a minimum of 10% of the galaxy’s total rest-frame UV flux. We measure the fraction of clumpy galaxies (f clumpy) as a function of stellar mass, redshift, and galaxy environment. Our results indicate that f clumpy increases with redshift, reaching ∼65% at z ∼ 1.5. We also find that f clumpy in low-mass galaxies () is 10% higher compared to that of their high-mass counterparts (). Moreover, we find no evidence of significant environmental dependence of f clumpy for galaxies at the redshift range of this study. Our results suggest that the fragmentation of gas clouds under violent disk instability remains the primary driving mechanism for clump formation, and incidents common in dense environments, such as mergers, are not the dominant processes.

GESTURE HANDLER

Hand gestures are a natural way of interactions when one person communicates with each other and therefore hand movements can be treated as a nonverbal form of communication. Hand gesture recognition is a process of understanding and classifying meaningful human hands movements. Gesture recognition was supposed to develop a new type of Human Computer Interaction system that would overcome the problems that users face with the current system. The algorithm that is being described in this technology is not based on background image subtraction and is not programmed for a specific hand type the algorithm used can process different hand types, recognizes no. of fingers, and can carry out tasks as per requirement or as described in the algorithm.

Moving Object Tracking Using Multiple Cameras in Network

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit.

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

Infrared Small Target Detection Enhancement Using a Lightweight Convolutional Neural Network

Detection of small, point targets is fundamental in applications such as early warning systems, surveillance, astronomy, and microscopy. The presence of noise and clutter can make it challenging to detect small targets while minimizing false detections. This paper presents a method for infrared small target detection using convolutional neural networks. The proposed method augments a conventional space-based detection processing chain with a lightweight neural network to predict the probability that a detection is a target. The proposed network is trained on 7 × 7 pixel windows using both the image sequence and the respective background-subtracted images. Results show that our method improves probability of detection at low false detection rates.

Seeing-sorted Visible Multi-Object Spectrograph U-band Imaging of the GOODS-south Field*

Abstract We present the optimal resolution and optimal depth U-filter mosaics using the seeing-sorted method of Ashcraft et al. on deep, ground-based U-bandimaging of the Great Observatories Origins Deep Survey South field as part of the near-UV imaging program UVCANDELS. We use the U-bandimages obtained with the VIsible Multi-Object Spectrograph on the European Southern Observatory Very Large Telescope by Nonino et al. Our best resolution mosaic includes images with a seeing full-width half maximum (FWHM) ≤ 0.″8, and encompasses 50% of the data. Our best depth mosaic includes images with FWHM ≤ 1.″5, corresponding to 100% of the data. Prior to being combined, the source fluxes in each individual background-subtracted image are corrected to match a 3D-HST photometric catalog of the same field to correct variations in the U-band zero-points. These mosaics provide deep U-banddata complementary to the UVCANDELS HST WFC3 F275W and ACS F435W images. We assess the depth of both U-bandmosaics.

Estrus Detection Using Background Image Subtraction Technique in Tie-Stalled Cows.

Simple SummaryWith increasing herd sizes and labor costs in recent decades, visual estrus detection by farmers has become more difficult because of the reduced manpower input per cow. To address this problem, various wearable devices have been developed for automatic estrus detection in cows, such as neck- or leg-mounted activity meters for monitoring estrus-associated increments in the amount of activity. However, these animal-contact devices have several limitations; namely, it can be dangerous to attach or remove the device and it can cause discomfort. Recently, a background image subtraction technique has been proposed as a non-contact method for monitoring activity changes in livestock animals. In this study, a new method was developed by combining the background subtraction technique and the thresholding method to detect estrus-associated activity increases in tie-stalled cows. Using this method, a substantial increase in activity in estrus was detectable, and the estrus detection sensitivity reached as high as 90% with a precision of 50%, where the sensitivity and precision were calculated as: (true-positive/[true-positive + false-negative]) × 100% and (true-positive/[true-positive + false-positive]) × 100%, respectively. This result may indicate that activity monitoring using the background subtraction technique has the potential to be a non-contact estrus detection method in tie-stalled cows.In this study, we determined the applicability of the background image subtraction technique to detect estrus in tie-stalled cows. To investigate the impact of the camera shooting direction, webcams were set up to capture the front, top, and rear views of a cow simultaneously. Video recording was performed for a total of ten estrous cycles in six cows. Standing estrus was confirmed by testing at 6 h intervals. From the end of estrus, transrectal ultrasonography was performed every 2 h to confirm ovulation time. Foreground objects (moving objects) were extracted in the videos using the background subtraction technique, and the pixels were counted at each frame of five frames-per-second sequences. After calculating the hourly averaged pixel counts, the change in values was expressed as the pixel ratio (total value during the last 24 h/total value during the last 24 to 48 h). The mean pixel ratio gradually increased at approximately 48 h before ovulation, and the highest value was observed at estrus, regardless of the camera shooting direction. When using front-view videos with an appropriate threshold, estrus was detected with 90% sensitivity and 50% precision. The present method in particular has the potential to be a non-contact estrus detection method for tie-stalled cows.

Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

Schlieren, shadowgraph and other types of refraction-based techniques have been often used to study gas flow structures. They can capture strong density gradients, such as shock waves. Shock wave detection is a very important task in analyzing unsteady gas flows. High-speed imaging systems, including high-speed cameras, are widely used to record large arrays of shadowgraph images. To process large datasets of the high-speed shadowgraph images and automatically detect shock waves, convective plumes and other gas flow structures, two computer software systems based on the edge detection and machine learning with convolutional neural networks (CNN) were developed. The edge-detection software utilizes image filtering, noise removing, background image subtraction in the frequency domain and edge detection based on the Canny algorithm. The machine learning software is based on CNN. We developed two neural networks working together. The first one classifies the image dataset and finds images with shock waves. The other CNN solves the regression task and defines shock wave position (single number) based on image pixels tensor (3-D array of numbers) for each image. The supervised learning code based on example input-output pairs was developed to train models. It was shown, that the machine learning approach gives better results in shock wave detection accuracy, especially for low-quality images with a strong noise level. Software system for automated shadowgraph images processing and x-t curves of the shock wave and convective plume movement plotting was developed.

In-vivo analysis of iron-gold composite nanoparticles as potential exogenous contrast agents for magnetomotive optical coherence tomography (MMOCT)

Magnetomotive optical coherence tomography (MMOCT) is a functional modification of conventional optical coherence tomography (OCT). It utilizes the nano-scale displacement embedded magnetic nanoparticles (MNPs) and subsequent amplitude and phase changes in OCT interferogram scattered from samples. We report a new robust method for ‘phase-lock-in’ detection of MMOCT signals and subsequent imaging. We validated this method utilizing iron-gold (Fe-Au) composite nanoparticles as an exogenous contrast agent for MMOCT. High contrast and specific background-subtracted images was generated for Fe-Au MNPs targeted samples. MMOCT with MNPs imaging systems was corroborated with tissue phantoms and in-vivo swiss albino mice model with modulating external magnetic field (resonant frequency of 30 Hz). A time-dependent dynamics study of diffusion for MNPs inside the tissue of mice as a function of time and depth is also reported.

Infrared (IR) thermography as a potential screening modality for carotid artery stenosis

In the present study, an infrared (IR) thermal camera was used to map the temperature of the target skin surface, and the resulting thermal image was evaluated for the presence of carotid artery stenosis (CAS). In the presence of stenosis in the carotid artery, abnormal temperature maps are expected to occur on the external skin surface, which could be captured and quantified using IR thermography. A Duplex Ultrasound (DUS) examination was used to establish the ground truth. In each patient, the background-subtracted thermal image, referred to as full thermal image, was used to extract novel parametric cold thermal feature images. From these images, statistical features, viz., correlation, energy, homogeneity, contrast, entropy, mean, standard deviation (SD), skewness, and kurtosis, were calculated and the two groups of patients (control and diseased: a total of 80 carotid artery samples) were classified. Both cut-off value- and support vector machine (SVM)-based binary classification models were tested. While the cut-off value classification model resulted in a moderate performance (70% accurate), SVM was found to have classified the patients with high accuracy (92% or higher). This preliminary study suggests the potential of IR thermography as a possible screening tool for CAS patients.

Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems

Schlieren and shadowgraph photography has been widely used to offer insight into the flow field in aerospace engineering due to their ease of application. The high-speed schlieren and shadowgraph techniques are typically applied to investigation of unsteady shock wave structure including shock reflection patterns and shock wave/boundary layer interactions, etc. Generally, qualitative analysis is performed based on the schlieren and shadowgraph image sequences. To process and analyze the large data set of the high-speed schlieren and shadowgraph images quantitatively, especially for the shock wave detection and tracking, a software was developed based on MATLAB GUI and its image processing toolbox. In this study, the image processing techniques exploited in the software, such as background subtraction, filter, threshold, edge detection, and shock tracking are presented. A case study on the phenomena of shock wave reflection from a solid surface was conducted. The results show that the proper filter method and the background image subtraction can effectively eliminate the image noises in frequency domain, which makes it easier to analyze the flow structure. Moreover, the instantaneous locations of shock waves are detected accurately, and the shock wave propagation speed calculated using the developed software are consistent with those of previous studies.

Detection Object on Sea Surface to Avoid Collision with Post-Processed in Background Subtraction Image

Data on shipping accident investigations from the National Transportation Safety Committee (NTSC) throughout 2010-2016 of fifty-four accident cases at sea, seventeen of which were accidents caused by collisions on ships in Indonesian waters, act to avoid a collision by detecting an object on the sea surface. Detection object is challenging because so many varieties object on the sea surface. Illumination variations with different seasons, periods, illumination intensity and direction affect the detection of objects directly. A rough sea is seen as a dynamic background of moving objects with size order and shape. All these factors make it difficult to object detection. Therefore, it is possible to conclude that background subtraction on sea surface problem remains open and a definitive robust solution is still missing. In this paper, we have applied a selection of background subtraction algorithms with post-processed to the problem. Experimental results with our dataset verify the high efficiency of our proposed method

A novel framework for background subtraction and foreground detection

In this paper, we propose a novel image background subtraction framework based on KDE. Firstly a new data structure called Mino Vector (MV) is designed for each pixel; we define dynamic nature (DN) for pixels of a scene and rank them in terms of DN for getting quantized results named dynamic rank (DR). Then, the varying KDE is adopted and implemented which significantly improves the estimation accuracy. Unlike using a global threshold in literature, we adaptively set a threshold for each pixel according to its DR. Inspired by the popular computer game Tetris, we present a Tetris update scheme (TUS) to update the background model in which the bottom row will be cleared, so do noises when the update condition is met. In experiments, we evaluate our framework on a well-known video dataset, CDnet 2012. Our results indicate that our framework achieves competitive results when compared with the state-of-the-art methods.

Discovery of a jet-like structure with overionized plasma in the SNR IC 443

Context. IC 443 is a supernova remnant (SNR) located in a quite complex environment since it interacts with nearby clouds. Indications for the presence of overionized plasma have been found though the possible physical causes of overionization are still debated. Moreover, because of its peculiar position and proper motion, it is not clear if the pulsar wind nebula (PWN) within the remnant is the relic of the IC 443 progenitor star or just a rambling one seen in projection on the remnant. Aims. Here we address the study of IC 443 plasma in order to clarify the relationship PWN-remnant, the presence of overionization and the origin of the latter. Methods. We analyzed two XMM-Newton observations producing background-subtracted, vignetting-corrected and mosaicked images in two different energy bands and we performed a spatially resolved spectral analysis of the X-ray emission. Results. We identified an elongated (jet-like) structure with Mg-rich plasma in overionization. The head of the jet is interacting with a molecular cloud and the jet is aligned with the position of the PWN at the instant of the supernova explosion. Interestingly, the direction of the jet of ejecta is somehow consistent with the direction of the PWN jet. Conclusions. Our discovery of a jet of ejecta in IC 443 enlarge the sample of core-collapse SNRs with collimated ejecta structures. IC 443’s jet is the first one which shows overionized plasma, possibly associated with the adiabatic expansion of ejecta. The match between the jet’s direction and the original position of the PWN strongly supports the association between the neutron star and IC 443.

Efficient background subtraction for thermal images using reflectional symmetry pattern (RSP)

Nowadays, thermal image processing has gained more attention. Thermal camera’s cost is decreasing, and so many real-time applications use thermal cameras since they have an ability to detect objects in darkness and track objects in the video. A background subtraction approach using Reflectional Symmetry Pattern (RSP) for thermal image background subtraction is proposed based on the assumption that the geometric reflectional symmetrical pattern of each of the objects (person) is much lower than the surrounding background. Reflectional symmetrical texture pattern can be used to create a subspace from the result of frame differencing approach. Statistical parameters such as VP (Vector Product), VD (Vector Direction) can be used to create an accurate background model. As a result, the proposed scheme can provide a high precision and less error rate to meet the requirements of object detection from real-time thermal videos.

Image Processing for Bioluminescence Resonance Energy Transfer Measurement-BRET-Analyzer.

A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji—BRET-Analyzer—allowing a systematic analysis, from image processing to ratio quantification. We share this open source solution and a step-by-step tutorial at https://github.com/ychastagnier/BRET-Analyzer. This toolset proposes (1) image background subtraction, (2) image alignment over time, (3) a composite thresholding method of the image used as the denominator of the ratio to refine the precise limits of the sample, (4) pixel by pixel division of the images and efficient distribution of the ratio intensity on a pseudocolor scale, and (5) quantification of the ratio mean intensity and standard variation among pixels in chosen areas. In addition to systematize the analysis process, we show that the BRET-Analyzer allows proper reconstitution and quantification of the ratiometric image in time and space, even from heterogeneous subcellular volumes. Indeed, analyzing twice the same images, we demonstrate that compared to standard analysis BRET-Analyzer precisely define the luminescent specimen limits, enlightening proficient strengths from small and big ensembles over time. For example, we followed and quantified, in live, scaffold proteins interaction dynamics in neuronal sub-cellular compartments including dendritic spines, for half an hour. In conclusion, BRET-Analyzer provides a complete, versatile and efficient toolset for automated reproducible and meaningful image ratio analysis.

Background derivation and image flattening: getimages

Modern high-resolution images obtained with space observatories display extremely strong intensity variations across images on all spatial scales. Source extraction in such images with methods based on global thresholding may bring unacceptably large numbers of spurious sources in bright areas while failing to detect sources in low-background or low-noise areas. It would be highly beneficial to subtract background and equalize the levels of small-scale fluctuations in the images before extracting sources or filaments. This paper describes getimages, a new method of background derivation and image flattening. It is based on median filtering with sliding windows that correspond to a range of spatial scales from the observational beam size up to a maximum structure width $X_{\lambda}$. The latter is a single free parameter of getimages that can be evaluated manually from the observed image $\mathcal{I}_{\lambda}$. The median filtering algorithm provides a background image $\tilde{\mathcal{B}}_{\lambda}$ for structures of all widths below $X_{\lambda}$. The same median filtering procedure applied to an image of standard deviations $\mathcal{D}_{\lambda}$ derived from a background-subtracted image $\tilde{\mathcal{S}}_{\lambda}$ results in a flattening image $\tilde{\mathcal{F}}_{\lambda}$. Finally, a flattened detection image $\mathcal{I}_{{\lambda}\mathrm{D}}{\,=\,}\tilde{\mathcal{S}}_{\lambda}{/}\tilde{\mathcal{F}}_{\lambda}$ is computed, whose standard deviations are uniform outside sources and filaments. Detecting sources in such greatly simplified images results in much cleaner extractions that are more complete and reliable. As a bonus, getimages reduces various observational and map-making artifacts and equalizes noise levels between independent tiles of mosaicked images.

An Adaptive Homomorphic Aperture Photometry Algorithm for Merging Galaxies

We present a novel automatic adaptive aperture photometry algorithm for measuring the total magnitudes of merging galaxies with irregular shapes. First, we use a morphological pattern recognition routine for identifying the shape of an irregular source in a background-subtracted image. Then, we extend the shape of the source by using the Dilation image operation to obtain an aperture that is quasi-homomorphic to the shape of the irregular source. The magnitude measured from the homomorphic aperture would thus have minimal contamination from the nearby background. As a test of our algorithm, we applied our technique to the merging galaxies observed by the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope. Our results suggest that the adaptive homomorphic aperture algorithm can be very useful for investigating extended sources with irregular shapes and sources in crowded regions.

Electron diffraction study of the plastic relaxation of MgO epitaxially grown on BCC FeV(001) alloys by varying the lattice mismatch

The epitaxial growth of MgO on Fe1−xVx buffer layers with adjustable lattice parameter is studied by electron diffraction (RHEED) in real time. At the onset of plastic relaxation in the MgO layer, a clear splitting of the diffraction rods is observed in 〈110〉 directions, as well as an increase in their length in the 〈100〉 directions. Splitting along 〈100〉 is also made visible through image background subtraction. These features originate from the surface strain above misfit dislocations, as previously proposed to account for satellite spots in LEED measurements. This explanation is supported by simulations of the diffraction patterns using kinematic diffraction theory. Observation of the diffraction rods splitting is shown to be a powerful way to check the presence of dislocations in MgO tunnel barriers and to accurately determine the critical thickness of plastic relaxation.