Eulerian video magnification (EVM)

Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian algorithm whether linear or phase based in order to noise cancellation. The approach utilizes wavelet denoising method to localize the frequencies of distributed noise over the different frequency bands. Subsequently, the energy of the coefficients under localized frequencies are attenuated by attenuating the amplitude of these coefficients. The experimental results of the proposed approach sho...

Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian algorithm whether linear or phase based in order to noise cancellation. The approach utilizes wavelet denoising method to localize the frequencies of distributed noise over the different frequency bands. Subsequently, the energy of the coefficients under localized frequencies are attenuated by attenuating the amplitude of these coefficients. The experimental results of the proposed approach sho...

Nowadays, smart devices have become a major part of human life, and this need has led to an increase in the demand for these devices, prompting major telecommunications companies to compete with each other to acquire the bulk of this market. This competition led to a significant increase in the number of mobile towers, to expand the coverage area. Each communication tower has transmitters and receivers to connect subscribers within the mobile network and other networks. The receivers and transmitters of each mobile tower operate on radio frequency waves. These waves can cause harm to humans if the body tissues absorb the radiation resulting from these waves. Headache, discomfort, and some other diseases are among the effects resulting from the spatial proximity to the mobile towers. In this paper, a model based on geographic information systems (GIS) software is proposed for the purpose of discovering the area of exposure to radio frequency radiation. This model can assists mitigate the opportunities of exposure to these radiations, thus reducing its danger. Real data of the levels of electromagnetic pollution resulting from mobile towers were analyzed during this study and compared with international safety standards.

In this study, a linear and phase-based Eulerian video magnification (EVM) methods are developed to minimize magnified noises and processing time. The developed approaches utilize the Lanczos resampling algorithm to reduce the frames' size of the source video so that the size of the processed data is significantly reduced. Then spatial decomposition is applied to the resized frames. Subsequently, temporal filters with specific cutoff frequencies are also used to filter only the desired frequencies to be amplified and then add them to the decomposed frames. The magnified frames are processed by a wavelet denoising algorithm to locate distributed noise over the different frequency bands and then remove it. The resulted denoised-magnified frames are resized up and then reconstructed by the spatial synthesis process. The experiments show the superiority and effectiveness of the developed EVM approaches compared to the conventional ones and other related approaches in terms of the execution time and the quality of the magnified video. The developed EVM approach can be used in several applications such as the detection of human vital signs without contact so that it is very useful to avoid infection in several diseases such as Covid-19. Furthermore, it can be used in detection of human mood and lying detection, detection and localization of material and liquid variations.

, kazuki.okami.ac, dan.mikami.vp, megumi.isogai.ks, hideaki.kimata.yu}@hco.ntt.co.jp Figure 1: Sports use-case: visualizing the impact spread in the iron shaft. The yellow arrow depicts the golf swing along a trajectory. The top row shows 2 frames overlaid to indicate the swing phase and the impact phase of the ball. The bottom row shows the spatiotemporal slices along a single diagonal red line in the top of row of (a), and the green and cyan circles in them respectively indicate the swing phase and impact phase. (a) Original video. (b) Phase-based motion magnification [25]. (c) Video acceleration magnification [28]. (d) Our proposed jerk-aware video acceleration magnification. Our method only magnifies subtle deformation of the iron shaft without artifacts caused by quick swinging motions in other methods. See the supplementary material for the video results.

Many important subtle changes in the environment are invisible to the naked human eyes. These subtle changes occur because of colour variations, such as blood flow in a human face that leads to face colour change, or motion variations, such as vena movement under human skin and vibration of buildings. The human eye requires optical microscopes to detect these variations. Alternatively, new technologies, such as high-speed imagery and computer processing, can be used to detect these variations. These computerised microscopes depend on computation rather than optical amplification to amplify subtle colour and motion changes in videos. The most popular technique to achieve computation-based microscope is the Eulerian video magnification (EVM). However, several challenges in EVM still need to be solved to meet the requirements of real time and video quality. This paper presents a comprehensive study of EVM methods and reviews the related literature. The strengths and drawbacks of existi...

In cases of natural disasters, epidemics or even in dangerous situations like an act of terrorism, battle fields, a shooting or a mountain accident, finding survivors is a challenge. In these kind of situations it is sometimes critical to know if a person has vital signs or not, without the need to be in contact with the victim, thus avoiding jeopardizing the lives of the rescue workers. In this work, we propose the use of video magnification techniques to detect small movements in human bodies due to breathing that are invisible to the naked eye. Two different video magnification techniques, intensity-based and phase-based, were tested. The utility of these techniques to detect people who are alive but injured in risk situations was verified by simulating a scene with three people involved in an accident. Several factors such as camera stability, distance to the object, light conditions, magnification factor or computing time were analyzed. The results obtained were quite positive for both techniques, intensity-based method proving more adequate if the interest is in almost instant results whereas the phase-based method is more appropriate if processing time is not so relevant but the degree of magnification without excessive image noise.

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. INDEX TERMS Singular value decomposition, Fourier transform, short-term motion, motion denoising, subtle color changes.

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. INDEX TERMS Singular value decomposition, Fourier transform, short-term motion, motion denoising, subtle color changes.

In this paper, we present a semi-automated method to magnify small motions in videos. This method amplifies invisible or hidden motions in videos. To achieve motion magnification, we process the spatial and temporal information obtained from the video itself. Advantage of this work is that it is application independent. Proposed technique estimates required parameters to get desirable results. We demonstrate performance on 9 different videos. Motion magnification performance is equivalent to existing manual methods.

One of the key methods for diagnosing the structural degradation of technical objects relies on observations of mechanical vibrations that accompany equipment operation and damage. Hardware and software advancements and the development mathematical methods for modelling and inference have increased the popularity of vibroacoustic diagnostics in mechanical systems. Displacement in the time domain of physical points in a vibrating object is the primary diagnostic symptom that undergoes further processing in the measurement system. At present, vibrations are usually registered with the use of accelerometers or optical sensors. Advanced tools for image recording, processing and analysis are deployed in quasi-realistic observations of motion that cannot be perceived by the human senses. This article discusses a method for visualizing vibrations based on deliberate deformation of the registered image through motion magnification. The presented approach is illustrated with selected examples.

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. INDEX TERMS Singular value decomposition, Fourier transform, short-term motion, motion denoising, subtle color changes.

In this paper an adaptation of the Eulerian Video Magnification technique is described for use with .TIFF files produced by a photo-conversion time lapse protocol for live cell microscopy, specifically for research into Acquired Immune Deficiency Syndrome. The tracking and characterisation of a protein found in Human Immunodeficiency Virus, to determine its dynamics and pathways is a key determinant in understanding the protein’s function. The aim of this algorithm is to process an image sequence in the temporal direction with the result being that changes in fluorescence for particular pixel locations, or regions of interest, are tracked and filtered thereby removing noise which is inherent with these types of images. This reduction in noise produced overall clearer results that will aid in further analysis of the live cells. In addition to this, this implementation attempts to adapt the existing EVM algorithm to aid in the analysis of photo-conversion experiments. The algorithm wi...

This paper proposes a modification approach for phased-based EVM in order to reduce the processing time without effect the quality of the magnified video. The proposed approach applies a resizing process on the input video using Lanczos-3 algorithm. Then, it decomposes video frames using steerable pyramid to obtain multi-scale frame with its orientation. Subsequently, the resulted frames are filtered by temporal filters for specific bands and the filtered frames are multiplied by a magnification factor. Now, both the magnified regions and the unmagnified regions for each frame are added together. Finally, reconstructing the produced magnified multi-scale frames using the inverse steerable pyramid. The experimental results show that superiority of the proposed approach compares to the conventional phase-based EVM in processing time, where the processing time reduction about 60-65%. Furthermore, this approach does not affect on the video quality, which maintain it in the boundary of the conventional Phase-based EVM.

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color i...

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color i...

Thispaper reviews computational techniques to efficiently represent, analyse and visualise both shortterm and long-term temporal variation in video and image sequences. This paper reveals temporal variation in videos that are imperceptible to the naked eye. The method takes a standard video sequence as an input, and applies spatial decomposition, followed by temporal filtering to the frames which is called “Eulerian Video Magnification”. The resulting signal is then amplified to reveal the hidden information. Paper contains four techniques which are: 1.Linear approximation method, 2.Phase based video processing, 3. Riesz pyramid for fast phase based video processing and 4.Enhanced Eulerian video magnification. Using above methods, one is able to amplify and visualize small motions and temporal colour changes.

In this paper, we present a semi-automated method to magnify small motions in videos. This method amplifies invisible or hidden motions in videos. To achieve motion magnification, we process the spatial and temporal information obtained from the video itself. Advantage of this work is that it is application independent. Proposed technique estimates required parameters to get desirable results. We demonstrate performance on 9 different videos. Motion magnification performance is equivalent to existing manual methods.

Videos are full of dynamic changes along both the spatial and temporal dimensions. Large, jerky short-term motions make it difficult to extract significant changes from videos such as subtle color changes and long-term motions occurring in time-lapse sequences. In this paper, we introduce two singular value decomposition (SVD)-based video decomposition schemes to clearly reveal such changes. The first scheme involves enhancing the visual characteristics of small subtle color changes in the presence of a wide variety of motion patterns by magnifying their pixel intensities. The second scheme removes short-term motions that visually distract attention from the underlying content of video sequences such as time-lapse videos, snowing scene, and maritime surveillance. Both schemes involve the decomposition of videos into spatiotemporal slices in which each slice is further decomposed into several singular components. The low-rank components that primarily represent background and color intensity information are then temporally processed to magnify the magnitude of the signal at the subtle color change target frequency. At the same time, an approach similar to that used in denoising time-lapse sequences is applied to temporally filter the singular components representing sparse information, thereby removing jittery short-term motions while preserving long-term motions, which are represented by both low-rank and unfiltered sparse components. We demonstrate promising color magnification and motion denoising results that can be obtained much faster than results estimated using state-of-the-art techniques. INDEX TERMS Singular value decomposition, Fourier transform, short-term motion, motion denoising, subtle color changes.

Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Using our method, we are able to visualize the flow of blood as it fills the face and also to amplify and reveal small motions. Our technique can run in real time to show phenomena occurring at temporal frequencies selected by the user.

Road accident has been serious case in Indonesia, the big number of the cases is not decreasing for six years. Many ways have been done, one of example is exploiting smart camera or CCTV to observe mocement estimation explicitly or implicitly. One problem is when explicit-based technique is applied, the computation process would take more resource. Implicit-based technique like exploitting processing-based frequency domain must be tried to make a better study and produce more knowledge in this study field. Color magnification can helpful information to support better movement estimation. This eulerian-based technique may be the one useful method to help this study. This paper implements eluerian video magnification to get color magnification on road as observed environment. This technique produces unexpected result that unknown black color appears, it still ambiguous because some scene can be described as black color object magnification result and another is shocking camera effect ...

Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian algorithm whether linear or phase based in order to noise cancellation. The approach utilizes wavelet denoising method to localize the frequencies of distributed noise over the different frequency bands. Subsequently, the energy of the coefficients under localized frequencies are attenuated by attenuating the amplitude of these coefficients. The experimental results of the proposed approach sho...

This paper proposes a modification approach for phased-based EVM in order to reduce the processing time without effect the quality of the magnified video. The proposed approach applies a resizing process on the input video using Lanczos-3 algorithm. Then, it decomposes video frames using steerable pyramid to obtain multi-scale frame with its orientation. Subsequently, the resulted frames are filtered by temporal filters for specific bands and the filtered frames are multiplied by a magnification factor. Now, both the magnified regions and the unmagnified regions for each frame are added together. Finally, reconstructing the produced magnified multi-scale frames using the inverse steerable pyramid. The experimental results show that superiority of the proposed approach compares to the conventional phase-based EVM in processing time, where the processing time reduction about 60-65%. Furthermore, this approach does not affect on the video quality, which maintain it in the boundary of the conventional Phase-based EVM.

In this paper, we present a system that is a combination of spatial and temporal processing to emphasize the subtle changes in a video. Images and videos contain valuable information about the changes that evolve over time. There is a lot of information in videos, which is not visible by naked eye, and we need to magnify these small changes to study its behavior. The human eye has limited spatial-temporal sensitivity and visualization capability within a certain frequency band. However many signals of interest lie below these bands and can be very much informative. For example, a slight variation in the human skin due to the blood flow cannot be seen by the naked eye. This slight variation can be used to extract the human pulse. In the same way, low motion amplitude is tough for the human eye to observe. This is magnified able to expose interesting information. The growth of new tackle to expose the unseen signals in a video is an open and extensive area of research. In this paper, we have proposed a system that combines the spatial and temporal processing of videos to amplify the small changes in the human skin to detect the heart rate. We have performed an experiment on thirty videos of different subjects. Subjects come from different age group, gender, and ethnicity. Simulation result shows that the proposed algorithm achieves around 93% accuracy in heart rate estimation. This area has an extensive research potential and in the last few decades, it has shown huge achievements not only in the field of medical but also in other fields of engineering and practical applications as like security.

Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian algorithm whether linear or phase based in order to noise cancellation. The approach utilizes wavelet denoising method to localize the frequencies of distributed noise over the different frequency bands. Subsequently, the energy of the coefficients under localized frequencies are attenuated by attenuating the amplitude of these coefficients. The experimental results of the proposed approach show its superiority over conventional linear and phase based Eulerian video magnification approaches in terms of quality of the resulted magnified videos. This allows to amplify the videos by larger amplification factor, so that several new applications can be added to the list of Eulerian video magnification users. Furthermore, the processing time does not significantly increase, the increment is only less than 3% of the overall processing compare to conventional Eulerian video magnification. Keywords: De-noising based wavelet transform Eulerian video magnification (EVM) Linear-based (LB-EVM) Motion magnification Phase-based (PB-EVM)

There are subtle motions present in a video sequence which are invisible by human eyes and require certain algorithms to analyse these motions. The fundamental goal of motion analysis is to determine a vector field describing changes in the image over time. In this process, the input video sequence in which motion is to be revealed initially undergoes spatial decomposition followed by the temporal processing of the decomposed frames where the frames are filtered to select desired band of frequencies to be enhanced. These bands of frequencies are experimentally derived and allowed the user to decide the amplification factor by which the motion is to amplify. After the processing and enhancement of the low frequency motions, the spatial pyramid is reconstructed back to get the improved video sequence, where the subtle motions are significantly visible. There are some techniques available for the said objective. The review of such techniques has been done and presented in this article.