The use of high level tools for developing volume graphic and video sequence processing applications

1999 ◽  
Author(s):  
S.J. Hill
Keyword(s):  
Video Sequence ◽  
Sequence Processing ◽  
High Level

Dynamic measurement of fabric wrinkle recovery angle by video sequence processing

2013 ◽  
Vol 84 (7) ◽  
pp. 694-703 ◽  
Author(s):  
Lei Wang ◽  
Jianli Liu ◽  
Ruru Pan ◽  
Weidong Gao
Keyword(s):  
Video Sequence ◽  
Dynamic Measurement ◽  
Sequence Processing ◽  
Recovery Angle

scholarly journals Photo Identification of Individual Salmo trutta Based on Deep Learning

2021 ◽  
Vol 11 (19) ◽  
pp. 9039
Author(s):  
Marius Pedersen ◽  
Ahmed Mohammed
Keyword(s):  
Salmo Trutta ◽  
Video Sequence ◽  
Space Representation ◽  
Shape Variation ◽  
Photo Identification ◽  
Identification Method ◽  
Tag Loss ◽  
Video Recordings ◽  
Individual Fish ◽  
High Level

Individual fish identification and recognition is an important step in the conservation and management of fisheries. One of most frequently used methods involves capturing and tagging fish. However, these processes have been reported to cause tissue damage, premature tag loss, and decreased swimming capacity. More recently, marine video recordings have been extensively used for monitoring fish populations. However, these require visual inspection to identify individual fish. In this work, we proposed an automatic method for the identification of individual brown trouts, Salmo trutta. We developed a deep convolutional architecture for this purpose. Specifically, given two fish images, multi-scale convolutional features were extracted to capture low-level features and high-level semantic components for embedding space representation. The extracted features were compared at each scale for capturing representation for individual fish identification. The method was evaluated on a dataset called NINA204 based on 204 videos of brown trout and on a dataset TROUT39 containing 39 brown trouts in 288 frames. The identification method distinguished individual fish with 94.6% precision and 74.3% recall on a NINA204 video sequence with significant appearance and shape variation. The identification method takes individual fish and is able to distinguish them with precision and recall percentages of 94.6% and 74.3% on NINA204 for a video sequence with significant appearance and shape variation.

Transcription factor/DNA interactions visualized by electron spectroscopic imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 450-451
Author(s):  
David P. Bazett-Jones ◽  
Mark L. Brown
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Phosphorus Content ◽  
Spectroscopic Imaging ◽  
Electron Spectroscopic Imaging ◽  
Dna Backbone ◽  
Two Parameters ◽  
High Level

A multisubunit RNA polymerase enzyme is ultimately responsible for transcription initiation and elongation of RNA, but recognition of the proper start site by the enzyme is regulated by general, temporal and gene-specific trans-factors interacting at promoter and enhancer DNA sequences. To understand the molecular mechanisms which precisely regulate the transcription initiation event, it is crucial to elucidate the structure of the transcription factor/DNA complexes involved. Electron spectroscopic imaging (ESI) provides the opportunity to visualize individual DNA molecules. Enhancement of DNA contrast with ESI is accomplished by imaging with electrons that have interacted with inner shell electrons of phosphorus in the DNA backbone. Phosphorus detection at this intermediately high level of resolution (≈lnm) permits selective imaging of the DNA, to determine whether the protein factors compact, bend or wrap the DNA. Simultaneously, mass analysis and phosphorus content can be measured quantitatively, using adjacent DNA or tobacco mosaic virus (TMV) as mass and phosphorus standards. These two parameters provide stoichiometric information relating the ratios of protein:DNA content.

Advances in Stem Instrumentation for Biology

1990 ◽  
Vol 48 (1) ◽  
pp. 362-363
Author(s):  
J. S. Wall
Keyword(s):  
Scanning Transmission Electron Microscope ◽  
Carbon Films ◽  
Specimen Preparation ◽  
Material Deposition ◽  
Active User ◽  
Percent Improvement ◽  
Scanning Transmission ◽  
The Moment ◽  
Film Substrate ◽  
High Level

The forte of the Scanning transmission Electron Microscope (STEM) is high resolution imaging with high contrast on thin specimens, as demonstrated by visualization of single heavy atoms. of equal importance for biology is the efficient utilization of all available signals, permitting low dose imaging of unstained single molecules such as DNA.Our work at Brookhaven has concentrated on: 1) design and construction of instruments optimized for a narrow range of biological applications and 2) use of such instruments in a very active user/collaborator program. Therefore our program is highly interactive with a strong emphasis on producing results which are interpretable with a high level of confidence.The major challenge we face at the moment is specimen preparation. The resolution of the STEM is better than 2.5 A, but measurements of resolution vs. dose level off at a resolution of 20 A at a dose of 10 el/A2 on a well-behaved biological specimen such as TMV (tobacco mosaic virus). To track down this problem we are examining all aspects of specimen preparation: purification of biological material, deposition on the thin film substrate, washing, fast freezing and freeze drying. As we attempt to improve our equipment/technique, we use image analysis of TMV internal controls included in all STEM samples as a monitor sensitive enough to detect even a few percent improvement. For delicate specimens, carbon films can be very harsh-leading to disruption of the sample. Therefore we are developing conducting polymer films as alternative substrates, as described elsewhere in these Proceedings. For specimen preparation studies, we have identified (from our user/collaborator program ) a variety of “canary” specimens, each uniquely sensitive to one particular aspect of sample preparation, so we can attempt to separate the variables involved.

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