Video Magnification in the Wild Using Fractional Anisotropy in Temporal Distribution

Takeda, Shoichiro; Akagi, Yasunori; Okami, Kazuki; Isogai, Megumi; Kimata, Hideaki

doi:10.1109/cvpr.2019.00171

Cited by 22 publications

(28 citation statements)

References 22 publications

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“…Unlike the above approach, the current video magnification methods are mainly based on a phase-based approach [1]- [6]. Phase-based video magnification methods analyze local phase changes, which correspond to local motion changes independent from color changes [7], at a fixed pixel position over time without object tracking.…”

Section: Phase-based Approachmentioning

confidence: 99%

“…These methods first decompose each image frame in a video into a multi-scale complex-steerable pyramid with various scales and orientations to obtain local phase changes, and these changes are then temporally filtered to detect subtle phase changes to be magnified. Although the phase changes are often contaminated by the effect of large motions and noise, with recently developed spatio-temporal filtering techniques [2]- [6], current phase-based video magnification methods can detect only subtle phase changes related to attractive physical phenomena even if the large motions of objects and noise exist in a video. After this processing, the subtle phase changes are amplified at each pyramid level, and the amplified complex-steerable pyramid is then reconstructed to output each magnified image frame in a video.…”

Section: Phase-based Approachmentioning

confidence: 99%

“…To visualize such subtle motion changes in a video, phase-based video magnification methods have recently attracted much attention as a way of visualizing these changes using a form of magnification [1]- [6]. These methods are based on the analysis of local phase changes that correspond to local motion changes independent from color changes [7].…”

Section: Introductionmentioning

confidence: 99%

“…The phase-based video magnification achieves good results for magnifying the subtle sway of a bridge, the breathing of a baby, and skin deformations due to blood circulation. Moreover, with recently developed spatio-temporal filtering techniques [2]- [6], current phasebased video magnification methods can magnify and visualize only subtle motion changes related to attractive physical phenomena even if large motions of objects and noise exist in a video. However, the methods require a long computational time to analyze local phase changes, in proportion to the video resolution and the number of time frames.…”

Section: Introductionmentioning

confidence: 99%

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Local Riesz Pyramid for Faster Phase-Based Video Magnification

Takeda¹,

Isogai²,

Shimizu³

et al. 2020

IEICE Trans. Inf. & Syst.

Self Cite

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Phase-based video magnification methods can magnify and reveal subtle motion changes invisible to the naked eye. In these methods, each image frame in a video is decomposed into an image pyramid, and subtle motion changes are then detected as local phase changes with arbitrary orientations at each pixel and each pyramid level. One problem with this process is a long computational time to calculate the local phase changes, which makes high-speed processing of video magnification difficult. Recently, a decomposition technique called the Riesz pyramid has been proposed that detects only local phase changes in the dominant orientation. This technique can remove the arbitrariness of orientations and lower the over-completeness, thus achieving high-speed processing. However, as the resolution of input video increases, a large amount of data must be processed, requiring a long computational time. In this paper, we focus on the correlation of local phase changes between adjacent pyramid levels and present a novel decomposition technique called the local Riesz pyramid that enables faster phase-based video magnification by automatically processing the minimum number of sufficient local image areas at several pyramid levels. Through this minimum pyramid processing, our proposed phase-based video magnification method using the local Riesz pyramid achieves good magnification results within a short computational time.

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Section: Phase-based Approachmentioning

confidence: 99%

Section: Phase-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local Riesz Pyramid for Faster Phase-Based Video Magnification

Takeda¹,

Isogai²,

Shimizu³

et al. 2020

IEICE Trans. Inf. & Syst.

Self Cite

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“…• Video Magnification, is the act of making something look larger than it is, the act of magnifying something or the larger appearance of an object when it is seen through a microscope, telescope, etc. Video Magnification Disease revealing invisible changes in the world allowed you to see subtle changes that cannot be seen with the naked eye like respiratory motion, human pulses to extract heart rate, see invisible (tiny) motion and hear silent sounds [13]. • Location tracking using Image Processing Based on video footage from multiple cameras located in and around a pen, which houses the animals, to extract their location and determine their activity [14].…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks-Based Solutions for Cattle Health Monitoring: A Survey

Gameil

Gaber

2019

Advances in Intelligent Systems and Computing

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Wireless Sensor Networks (WSN) are nowadays becoming an active research in different fields. Precise irrigation, agriculture, earthquake, fire monitoring in forests and animal health monitoring are few applications of WSN. Animal health monitoring systems (AHMS) are usually used to monitor physiological parameters such as rumination, heart rate, and body temperature. Traditional methods to monitor animal health such as (traditional surveillance, single observation, and simple tabular and graphic techniques) are not efficient to achieve high performance in the large herds' management systems. These methods can only provide partial information and introduce a large cost in staffing and physical hardware. Thus, it is of important need to overcome a foresaid drawback by using alternative low cost, low power consumption sensor nodes, and providing real-time communications at a sensible hardware cost. The objectives of this paper are: reviewing existing WSN solutions for cattle health monitoring models and determining the requirements needed for building an effective WSN model suitable for cattle health monitoring and detect animal diseases. From this review, requirements of the effective WSN-based solution for cattle health monitoring were suggested.

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