Time-Variant Moving Objects Extraction using Velocity-Tuned Filter Banks and Time-Recursion Method in Infrared Image Sequences

1997 ◽  
Vol 07 (04) ◽  
pp. 283-299
Author(s):  
Jae-Ho Choi ◽  
Bong-Tae Kim ◽  
Won-Koo Kim
Keyword(s):  
Moving Objects ◽  
Filter Banks ◽  
Temporal Frequency ◽  
Motion Vector ◽  
Infrared Image ◽  
Estimation Algorithm ◽  
Image Sequences ◽  
Natural Scenes ◽  
Recursion Method ◽  
Frequency Space

A motion vector selective moving object estimation algorithm that preserves the exact shapes and textures of moving objects is presented. In order to extract multiple moving objects with arbitrary motion vectors embedded in the sequence of image frames of cluttered stationary background as alleviating the aliasing effects, both 3D spectral filter banks, called velocity-tuned filter banks, and time-recursive Kalman filter are incorporated to work in parallel. Furthermore, using the fact that the motion energy for each one of the moving objects takes a unique part of the spectrum in the 3D spatio-temporal frequency space, the rotation invariant multiple moving objects detection is also possible when using the proposed filter banks. Simulations have been run to analyze the performance of our filtering algorithm utilizing image sequences of natural scenes. The accurate and robust sets of estimation results are observed down to signal-to-noise ratios of 12 dB.

scholarly journals Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2461 ◽  
Author(s):  
Cong Zhang ◽  
Dongguang Li
Keyword(s):  
Motion Vector ◽  
Computational Cost ◽  
Infrared Image ◽  
Image Sensor ◽  
Projection Algorithm ◽  
Video Stabilization ◽  
Sensors And Actuators ◽  
Simulation And Experiment ◽  
Transformation Relation ◽  
Trajectory Correction

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze’s workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile’s motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze.

Orthopteran DCMD neuron: a reevaluation of responses to moving objects. I. Selective responses to approaching objects

1992 ◽  
Vol 68 (5) ◽  
pp. 1654-1666 ◽  
Author(s):  
F. C. Rind ◽  
P. J. Simmons
Keyword(s):  
Moving Objects ◽  
Temporal Frequency ◽  
Angular Acceleration ◽  
Spike Rate ◽  
Single Peak ◽  
Wide Field ◽  
Movement Detector ◽  
Star Wars ◽  
Wide Range ◽  
Real Objects

1. The "descending contralateral movement detector" (DCMD) neuron in the locust has been challenged with a variety of moving stimuli, including scenes from a film (Star Wars), moving disks, and images generated by computer. The neuron responds well to any rapid movement. For a dark object moving along a straight path at a uniform velocity, the DCMD gives the strongest response when the object travels directly toward the eye, and the weakest when the object travels away from the eye. Instead of expressing selectivity for movements of small rather than large objects, the DCMD responds preferentially to approaching objects. 2. The neuron shows a clear selectivity for approach over recession for a variety of sizes and velocities of movement both of real objects and in simulated movements. When a disk that subtends > or = 5 degrees at the eye approaches the eye, there are two peaks in spike rate: one immediately after the start of movement; and a second that builds up during the approach. When a disk recedes from the eye, there is a single peak in response as the movement starts. There is a good correlation between spike rate and angular acceleration of the edges of the image over the eye. 3. When an object approaches from a distance sufficient for it to subtend less than one interommatidial angle at the start of its approach, there is a single peak in response. The DCMD tracks the approach, and, if the object moves at 1 m/s or faster, the spike rate increases throughout the duration of object movement. The size of the response depends on the speed of approach. 4. It is unlikely that the DCMD encodes the time to collision accurately, because the response depends on the size as well as the velocity of an approaching object. 5. Wide-field movements suppress the response to an approaching object. The suppression varies with the temporal frequency of the background pattern. 6. Over a wide range of contrasts of object against background, the DCMD gives a stronger response to approaching than to receding objects. For low contrasts, the selectivity is greater for objects that are darker than the background than for objects that are lighter.

scholarly journals The determination of high-resolution spatio-temporal glacier motion fields from time-lapse sequences

2017 ◽  
Vol 5 (4) ◽  
pp. 861-879 ◽  
Author(s):  
Ellen Schwalbe ◽  
Hans-Gerd Maas
Keyword(s):  
High Resolution ◽  
Vector Fields ◽  
Motion Vector ◽  
Image Sequence ◽  
Image Sequences ◽  
Glacier Surface ◽  
Sequence Processing ◽  
Image Sequence Processing ◽  
Matching Techniques

Abstract. This paper presents a comprehensive method for the determination of glacier surface motion vector fields at high spatial and temporal resolution. These vector fields can be derived from monocular terrestrial camera image sequences and are a valuable data source for glaciological analysis of the motion behaviour of glaciers. The measurement concepts for the acquisition of image sequences are presented, and an automated monoscopic image sequence processing chain is developed. Motion vector fields can be derived with high precision by applying automatic subpixel-accuracy image matching techniques on grey value patterns in the image sequences. Well-established matching techniques have been adapted to the special characteristics of the glacier data in order to achieve high reliability in automatic image sequence processing, including the handling of moving shadows as well as motion effects induced by small instabilities in the camera set-up. Suitable geo-referencing techniques were developed to transform image measurements into a reference coordinate system.The result of monoscopic image sequence analysis is a dense raster of glacier surface point trajectories for each image sequence. Each translation vector component in these trajectories can be determined with an accuracy of a few centimetres for points at a distance of several kilometres from the camera. Extensive practical validation experiments have shown that motion vector and trajectory fields derived from monocular image sequences can be used for the determination of high-resolution velocity fields of glaciers, including the analysis of tidal effects on glacier movement, the investigation of a glacier's motion behaviour during calving events, the determination of the position and migration of the grounding line and the detection of subglacial channels during glacier lake outburst floods.

Removal of Moving Objects from a Street-View Image by Fusing Multiple Image Sequences

2010 ◽  
Author(s):  
Hiroyuki Uchiyama ◽  
Daisuke Deguchi ◽  
Tomokazu Takahashi ◽  
Ichiro Ide ◽  
Hiroshi Murase
Keyword(s):  
Moving Objects ◽  
Image Sequences ◽  
Multiple Image ◽  
Street View
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