Panning (camera)

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
Example of a panning technique photo (shutter speed: 1/80)

In cinematography and photography panning means swivelling a still or video camera horizontally from a fixed position. This motion is similar to the motion of a person when they turn their head on their neck from left to right. In the resulting image, the view seems to "pass by" the spectator as new material appears on one side of the screen and exits from the other, although perspective lines reveal that the entire image is seen from a fixed point of view.

The term panning is derived from panorama, suggesting an expansive view that exceeds the gaze, forcing the viewer to turn their head in order to take everything in. Panning, in other words, is a device for gradually revealing and incorporating off-screen space into the image.

Panning should never be confused with tracking or "travelling," in which the camera is not just swivelled but is physically displaced left or right, generally by being rolled parallel to its subject.

In video technology, panning refers to the horizontal scrolling of an image wider than the display.

For 3D modeling in computer graphics, panning means moving parallel to the current view plane.[1] In other words, the camera moves perpendicular to the direction it is pointed.

The technique also has limited applications in still photography.

In other disciplines, this motion is called yaw.

Using panning in still photography[edit]

Panning shot of a chicken running, at a slow shutter speed of 1/40 second
Panning of Hawker Sea Fury FB 10 at Hahnweide, shutter speed is 1/125 second

When photographing a moving subject, the panning technique is achieved by keeping the subject in the same position of the frame for the duration of the exposure. The exposure time must be long enough to allow the background to blur due to the camera movement as the photographer follows the subject in the viewfinder.

The exact length of exposure required will depend on the speed at which the subject is moving, the focal length of the lens and the distance from the subject and background. An F1 car speeding along a straight might allow the photographer to achieve a blurred background at 1/250 second, while the photographer might need to go as slow as 1/40 to achieve the same amount of blur for a picture of a running man.[2]

The faster shutter speed allowed by fast moving subjects are easier to capture in a smoothly panned shot. With slower moving subjects, the risk is that the panning motion will be jerky, and it is also harder to keep the subject in the same position of the frame for the longer period of time.

To aid in capturing panned pictures, photographers use aids such as tripods and monopods, which make it easy to swing the camera along one plane, while keeping it steady in the others.

Claire Thomas, Thierry Ranchin et,al [1] Introduced a framework to combine the excessive resolution multispectral image from lower resolution panchromatic image. Given more current fusion processes like substitution, primarily based methods, relative spectral contribution techniques, ARISIS based methods and advantages and disadvantages of each technique. Henrik Aanaes, Johmaes R et,al [2] Introduced the approach of pixel level satellite image fusion from the imaging sensor model. Pixel neighborhood regularization is offered for the regularization of the projected approach. The algorithm examined on Quick Bird, IKONOS, Meteosat data sets. The performance evaluation matrices used are Root Mean square Error(RMSE), Cross Correlation (CC), Structural Similarity Index(SSI), and Q4. The author shown that the projected technique will be compared to many existing technique. Faming Fang, Fang Li, et,al [3] proposed another variation image fusion technique depend on three suspicions i) gradient of PAN image is combination of image groups utilized in pan sharpened image. Ii) The gradient in the spectrum of the fused image should be resemble to low resolution MS image. The algorithm is tested on QuickBird, IKONOS data sets. The performance evaluation parameters are RMSE, CC, Structural Angle Mapper (SAM), Spatial Frequency (SF). Xinghao Ping, Yiyong Jiang et,al [4] Proposed a Bayesian non parametric lexicon learning version for image combination. The proposed strategy won’t request the first MS image for dictionary learning, rather it straightforwardly uses the reconstructed images for dictionary learning. The algorithm is tested on IKONOS, Pleiades,QuickBird data sets. The performance evaluation metrics used are RMSE, CC,ERGAS,Q4. S. Li and B. Yang et,al [5] proposed an image combination problem from compressed sensing theory. First the degradation model is constructed from low MS and high PAN as a sampling procedure. So the image fusion process is converted into restoration problem. Later the interest calculation is used to determine the reclamation issue. The QuickBird also IKONOS satellite images remain utilized to analysis image fusion algorithm. In this the presentation evaluation parameters used are CC, SAM, RMSE, ERGAS and Q4. F. Palsson, J. R. Sveinsson, et,al [6] proposed a pattern based image combination procedure. The model is dependent on the assumption that a straight mixes of the groups of the fused images gives a panchromatic image and the down sampling the fused image provides multispectral image. The algorithm is tested by using QuickBird data sets and performance evaluation metrics used are SAM, ERGAS, CC, and Q4. S. Leprince, S. Barbot, et,al [7] proposed a method to automatic co-register the optical satellite images by using ground deformation measurement. By using the proposed method the images are co-registered with 1/50 pixel accuracy. The algorithm is tested for SPOT satellite images in the case of non ceseismic deformation and in the case of large ceseismic deformation. M. L. Uss, B. Vozel, et,al [8] proposed latest execution bound for investigating the image enrollment approaches impartially. This proposed lower bond engaged with a geometric transformation accepted between the passing on image and templet images. The test results proved that the lower bound portrays all the more productively the execution of traditional estimators then different limits submitted in the writing. Y. Peng, A. Ganesh, et,al [9] proposed image registration technique called strong arrangement by inadequate and low rank disintegration for directly related images (RASL) which efficiently co-register linearly correlated satellite images. The accuracy of the introduced approach is very high and this introduced approach efficiently co-registers the data sets over extensive variety of practical misalignments and distortions. Miloud Chikr El-Mezouar, Nasreddine Taleb, et,al [10] Another combination approach that induces images with normal colors is introduced. Additionally, in this strategy, a high-resolution standardized contrast is proposed and utilized in outlining the vegetation. The method is performed in two different stages: MS combination utilizing the HIS strategy and vegetation improvement. Vegetation improvement is nothing but it’s a correction step, and it relies upon the program. The new methodology gives great outcomes as far as target quality measurements. Furthermore, visual examination demonstrates that the idea of the proposed approach is promising, and it enhances well combination quality by upgrading vegetated zones. M.E.Nasr,S.M.Elkaffas et,al [11] proposed image combination method, in light of incorporating together Intensity Hue Saturation (IHS) also Discrete Wavelet Frame Transform (DWFT), planned as advancing the nature of remote sensing images. A PAN and MS images taken away Landsat-7(ETM+) satellite fused utilizing the present fresh methodology. Exploratory outcome represents the latest introduced method gives best result for spatial and spectral quality of fused image. In addition, at the present procedure being connected via noisy and de-noisy remote sensing images that can save the nature of the fused image. Comparing and analyzing among various combination procedures are also additionally displayed that the proposed method beats alternate methods. Xia Chun-lina, Deng Jie, et,al [12]proposed another fused technique was exhibited—PWI change: First, multispectral image was changed by HIS, and after that they obtained brilliance part—I was changed by PCA to extricate the first principal component— PC1. Utilizing wavelet transform, the PC1 and panchromatic images were fused, and after that the outcomes were utilized to replace the brilliance part of the multispectral image. At long last the new multispectral image was gained by the backward IHS transform. The new strategy was better then the single one of three combination strategies of HIS, the wavelet transform and PCA, upgrade the presenting ability of image spatial detail to greater extent, and all around held the spectral information of the multispectral image. Hamid Reza Shahdoosti and Hassan Ghassemian et,al [13] Designing an ideal channel that can remove important and non redundant data from the PAN image is introduced in this type. The excellent channel coefficients extricated from factual properties of the images are most steady with sort and surface of the remotely detected images compared with different kernels, for example, wavelets. Visual and statistical assessments demonstrate that the proposed calculation neatly enhances the fusion quality as far as connection coefficient, relative dimensionless worldwide error in synthesis, spectral angle mapper, including enhanced intensity–hue–immersion, multiscale Kalman channel, Bayesian, enhanced non sub sampled contour let transform, and scanty fusion of image. Jianwen Hu and Shutao Li, et,al [14]provides a novel technique dependent on the created multiscale double two sided channel to combine high spatial PAN and high spectral MS image. Compared with multiresolution based strategies, the procedure of detail eradication deal with the attributes about PAN figure and MS figure at the same extent. The lower resolution MS image is re-unit via similar amount of high resolution PAN image also sharpened by infusing extracted information. The proposed combination technique is tried through QuickBird also IKONOS image again compared with 3 mainstream strategies. Qian Zhang, Zhiguo Cao, et,al [15]proposed an iterative optimization method, where it considers the registration and fusion forms of methods, was suggested for panchromatic (PAN) and also for multispectral (MS) images. Provided an registration strategy and a fusion technique, the collective advancement method is explained as finding the optimal registration parameters to gain profit in the optimal fusion performance. Hear in this methodology, the declining simplex calculation is adopted to filter the registration parameters alternatively. Examinations on arrangement of PAN also MS images of ZY-3 also GeoEye-1 demonstrate the invented method beats a few contending ones as far as registration accuracy and fusion quality


See also[edit]


  1. ^ "3ds Max Pan View".
  2. ^ "Pan for better action pictures". Illustrated Photography.
  3. ^ Langford, Michael (1986). Basic Photography. Focal Press. ISBN 0-240-51257-X.

External links[edit]

Media related to Panning at Wikimedia Commons