Object, Image, Space and Light: Paula Dawson's Artwork 1974-1990

Leonardo ◽  
1992 ◽  
Vol 25 (5) ◽  
pp. 467
Author(s):  
Paula Dawson ◽  
Rebecca Coyle
Keyword(s):  
Image Space ◽  
Object Image

A hybrid object/image space approach for efficient and robust line drawings

2011 ◽  
Author(s):  
Long Zhang ◽  
Ying He ◽  
Hock-Soon Seah ◽  
Wolfgang Mueller-Wittig
Keyword(s):  
Image Space ◽  
Object Image ◽  
Line Drawings ◽  
Space Approach

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.

Computer Vision Tagging the Metropolitan Museum of Art's Collection

2021 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Elena Villaespesa ◽  
Seth Crider
Keyword(s):  
Computer Vision ◽  
Selection Criteria ◽  
Object Image ◽  
Metropolitan Museum ◽  
Museum Collections ◽  
Vision Systems ◽  
Computer Vision Systems ◽  
Similarities And Differences ◽  
Ibm Watson

Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.

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