motion magnification

Abstract—This paper aims to prove the accuracy of an inexpensive smartphone camera as a non-contact vibration sensor to recover the vibration modes of a vibrating structure such as a cantilever beam. A video of a vibrating beam is filmed using a smartphone camera and then processed by the motion magnification technique. Based on this method, the first two natural frequencies and their associated mode shapes are estimated experimentally and compared to the analytical ones. Results show a relative error of less than 4% between the experimental and analytical approaches for the first two natural frequencies of the beam. Also, for the first two-mode shapes, a Modal Assurance Criterion (MAC) value of above 0.9 between the two approaches is obtained. This slight error between the different techniques ensures the viability of a cheap smartphone camera as a non-contact vibration sensor, particularly for structures vibrating at relatively low natural frequencies.

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...

Recent advancements in digital photography, particularly through mobile phone cameras, microscopes, and satellite imaging, have made high-resolution image capture increasingly accessible. However, these captured images often contain subtle changes in color resolution and density that are challenging to observe with the naked eye. This study explores the application of Eulerian amplification as a method to detect and enhance such small-amplitude variations, making them visible for improved analysis. Unlike traditional approaches, Eulerian methods are particularly effective for processing smooth structures and high-quality printed materials where minor amplifications in color can reveal significant grading details. Using a pixel-by-pixel Eulerian path analysis, this method allows for a detailed comparison of color intensity values on a fine 8-bit scale, which ranges from 0 to 255. Through this analysis, color distribution patterns are visualized across the spatial structure of the image, which is particularly beneficial for identifying specific variations in quality in printed materials, such as highly graded cards. To manage large image structures, spatial decomposition using a multi-level Gaussian pyramid technique is employed. By applying temporal filtering within select frequency bands (0.4-4 Hz) at a coarse pyramid level, the algorithm effectively pools spatial data, enabling the detection of color density shifts that may indicate image defects. The amplified image data is further processed using a classifier neural network, which provides high sensitivity and specificity in detecting edge defects, image centering issues, scuffs, and breakages. The system demonstrates promising results in identifying true positives while maintaining low false-negative rates, thus confirming the reliability of deep machine learning algorithms in high-sensitivity defect detection. This paper presents the significance of Eulerian amplification in optical image processing, offering a robust tool for precise, automated defect detection across a variety of high-resolution image types.

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.

The aim of this work is to experimentally investigate the ability prepared PVDF film sensor bonded on aluminium cantilever beams each having cracks at different locations with increasing crack depths for observing the ability of the film to indicate it with its film voltage response. The produced PVDF film sensor is bonded to a cantilever beam with araldite klear solution. Then with the help of impact hammer the beam is excited and voltage responses are noted down. The modal behaviour of beam and piezo-response is monitored by experimental measurement and is supported by the finite element analysis.

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...

Video devinim büyütme, insan gözünün algılayamadığı küçük mertebedeki devinimlerin uygun bir yöntemle büyütülmesi ve bu devinimlerin videoda gözle görülebilir hale getirilmesi işlemidir. Bu algoritmalar, video çerçevelerini hem uzamsal hem de zamansal alanda işleme tabi tutarak minik devinim ve titreşimlerin büyütülüp videoya geri gömülmesi temeline dayanmaktadır. Örneğin, nabız atımının bilekte oluşturduğu devinim, köprü salınımları ve bina titreşimleri gibi algılanması zor olan devinimlerin yanında ses geriçatımı ve optik gibi çeşitli alanlarda da bu yöntemler kullanılmaya başlanmıştır. Bu çalışmada video devinim büyütmede kullanılan Euler yönteminin zamansal işleme katmanında, radar ve sesötesi (ultrasound) gibi sinyal işleme alanlarında uygulanan ve sinyal-gürültü oranını arttıran Bölünmüş İzge İşleme yöntemi kullanılmıştır. Önerilen yöntem ve Euler devinim büyütme yöntemi, yapısal benzerlik indeksi üzerinden karşılaştırılmış ve iyileştirmeler gösterilmiştir.Video motion magnifi...

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.

In recent years, lightweight structures have become mature and adopted in various applications. The importance of quality assurance cannot be overemphasized hence extensive research has been conducted to assess the quality of lightweight structures. This study investigates a novel process that exploits motion magnification to investigate the damage characteristics of lightweight mission-critical parts. The goal is to assure the structural integrity of 3D printed structures and composite structures by determining the inherent defects present in the part by exploiting their vibration characteristics. The minuscule vibration of the structure was recorded with the aid of a high-speed digital camera, and the motion was estimated by applying a phase-based algorithm. The spectral information was compared with the results obtained by a laser displacement sensor for validation. Then, the video-based results were used to perform damage identification by comparing the extracted information wit...

In recent years, lightweight structures have become mature and adopted in various applications. The importance of quality assurance cannot be overemphasized hence extensive research has been conducted to assess the quality of lightweight structures. This study investigates a novel process that exploits motion magnification to investigate the damage characteristics of lightweight mission-critical parts. The goal is to assure the structural integrity of 3D printed structures and composite structures by determining the inherent defects present in the part by exploiting their vibration characteristics. The minuscule vibration of the structure was recorded with the aid of a high-speed digital camera, and the motion was estimated by applying a phase-based algorithm. The spectral information was compared with the results obtained by a laser displacement sensor for validation. Then, the video-based results were used to perform damage identification by comparing the extracted information wit...

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.

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.

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.

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.

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.

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)

O mundo está cheio de sinais importantes, mas visualmente subtis, que passam despercebidos ao olho humano. A pulsação de uma pessoa, a respiração de uma criança, a oscilação de uma ponte -todos estes exemplos criam padrões visuais. A ampliação de vídeo euleriana é uma ferramenta computacional para visualizar estas variações subtis de cor e movimento em vídeos comuns, tornando as variações maiores. É um microscópio para pequenas mudanças que são difíceis ou impossíveis de vermos por nós próprios.

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.