Image Space

GPU Pro ◽  
2010 ◽  
pp. 245-246
Keyword(s):  
Image Space

High Resolution Specimen Area Imaged Under Negligible Field Aberrations

1970 ◽  
Vol 28 ◽  
pp. 540-541 ◽  
Author(s):  
T. Yanaka ◽  
K. Shirota
Keyword(s):  
High Resolution ◽  
Field Distribution ◽  
Image Space ◽  
Beam Deflection ◽  
Objective Lens ◽  
Resolution Limit ◽  
Leakage Flux ◽  
Specimen Area ◽  
Points Of View ◽  
Aberration Theory

It is significant to note field aberrations (chromatic field aberration, coma, astigmatism and blurring due to curvature of field, defined by Glaser's aberration theory relative to the Blenden Freien System) of the objective lens in connection with the following three points of view; field aberrations increase as the resolution of the axial point improves by increasing the lens excitation (k2) and decreasing the half width value (d) of the axial lens field distribution; when one or all of the imaging lenses have axial imperfections such as beam deflection in image space by the asymmetrical magnetic leakage flux, the apparent axial point has field aberrations which prevent the theoretical resolution limit from being obtained.

Obraz, przestrzeń, rewolucja. Wszystkie sztuki okupacji

2013 ◽  
Author(s):  
W.J.T. Mitchell
Keyword(s):  
Image Space ◽  
Critical Inquiry ◽  
The Arts

Przekład tekstu "Image, Space, and Rewolution. The Arts of Occupation", który ukazał się w "Critical Inquiry" (2012, nr 39)

scholarly journals Learned Collaborative Stereo Refinement

2021 ◽  
Author(s):  
Patrick Knöbelreiter ◽  
Thomas Pock
Keyword(s):  
Structural Properties ◽  
Gradient Method ◽  
Color Image ◽  
Image Space ◽  
Activation Functions ◽  
Disparity Map ◽  
Proximal Gradient Method ◽  
Variational Structure ◽  
Disparity Maps ◽  
The Individual

AbstractIn this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps, which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.

Multi-image space resection based geometric calibration for Four bands CCD camera

2010 ◽  
Author(s):  
Xingfeng Chen ◽  
Xingfa Gu ◽  
Huibin Ge ◽  
Jinjin Zhang ◽  
Jiping Chen ◽  
...  
Keyword(s):  
Ccd Camera ◽  
Image Space ◽  
Geometric Calibration

Efficient Stochastic Algorithms on Locally Bounded Image Space

1993 ◽  
Vol 55 (6) ◽  
pp. 494-506 ◽  
Author(s):  
C.D. Yang
Keyword(s):  
Image Space ◽  
Stochastic Algorithms

scholarly journals Feasibility study for image-guided kidney surgery: Assessment of required intraoperative surface for accurate physical to image space registrations

2008 ◽  
Vol 35 (9) ◽  
pp. 4251-4261 ◽  
Author(s):  
Anne B. Benincasa ◽  
Logan W. Clements ◽  
S. Duke Herrell ◽  
Robert L. Galloway
Keyword(s):  
Feasibility Study ◽  
Image Space ◽  
Image Guided ◽  
Kidney Surgery

CHARACTERIZATION OF IMAGE SPACE OF A WAVELET TRANSFORM

2006 ◽  
Vol 04 (03) ◽  
pp. 547-557 ◽  
Author(s):  
CAIXIA DENG ◽  
YULING QU ◽  
LIJUAN GU
Keyword(s):  
Wavelet Transform ◽  
Truncation Error ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Sampling Theorem ◽  
Image Space ◽  
Kernel Space ◽  
Reproducing Kernel Space ◽  
Scope Of Application

In this paper, Journe wavelet function is introduced as a wavelet generating function. The expression of reproducing kernel function for the image space of this wavelet transform is obtained based on the fact that the image space of the wavelet transform is a reproducing kernel Hilbert space. Then the isometric identity of Journe wavelet transform is obtained. The connections between the image space of the wavelet transform and the image space of the known reproducing kernel space are established by the theories of reproducing kernel. The properties and the structures of the image space of the wavelet transform can be characterized by the properties and the structures of the image space of the known reproducing kernel space. Using the ideas of reproducing kernel, we consider there are relations between the wavelet transform and the sampling theorem. Meanwhile, the approximations in sampling theorems is shown and the truncation error is given. This provides a theoretical basis for us to study the image space of the general wavelet transform and broadens the scope of application of theories of the reproducing kernel space.

A Study on Image Space Design of Bong Joon-Ho's Films from Perspective of Localization

2021 ◽  
Vol 23 ◽  
pp. 169-188
Author(s):  
Xiao Lin ◽  
◽  
Jin-Soo Kim ◽  
Sang-Eun Lee
Keyword(s):  
Image Space ◽  
Space Design
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