Automation of the preoperative image processing steps for ultrasound based navigation

2009 ◽  
pp. 680-683
Author(s):  
Claudia Dekomien ◽  
S. Winter
Keyword(s):  
Image Processing ◽  
Preoperative Image ◽  
Processing Steps

scholarly journals Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in a Cs-Corrected TEM

2008 ◽  
Vol 16 (6) ◽  
pp. 36-39 ◽  
Author(s):  
E. Voelkl ◽  
B. Jiang ◽  
Z.R. Dai ◽  
J.P Bradley
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Information Content ◽  
Exposure Time ◽  
Image Acquisition ◽  
Ccd Camera ◽  
Automated Data Acquisition ◽  
Processing Steps

Image acquisition with a CCD camera is a single-press-button activity: after selecting exposure time and adjusting illumination, a button is pressed and the acquired image is perceived as the final, unmodified proof of what was seen in the microscope. Thus it is generally assumed that the image processing steps of e.g., “darkcurrent correction” and “gain normalization” do not alter the information content of the image, but rather eliminate unwanted artifacts.

scholarly journals STEREO RECONSTRUCTION OF ATMOSPHERIC CLOUD SURFACES FROM FISH-EYE CAMERA IMAGES

2016 ◽  
Vol XLI-B3 ◽  
pp. 49-56
Author(s):  
G. Katai-Urban ◽  
V. Otte ◽  
N. Kees ◽  
Z. Megyesi ◽  
P. S. Bixel
Keyword(s):  
Image Processing ◽  
Camera Calibration ◽  
Camera System ◽  
Stereo Camera ◽  
Stereo Reconstruction ◽  
Point Set ◽  
The Creation ◽  
Fish Eye ◽  
Processing Steps

In this article a method for reconstructing atmospheric cloud surfaces using a stereo camera system is presented. The proposed camera system utilizes fish-eye lenses in a flexible wide baseline camera setup. The entire workflow from the camera calibration to the creation of the 3D point set is discussed, but the focus is mainly on cloud segmentation and on the image processing steps of stereo reconstruction. Speed requirements, geometric limitations, and possible extensions of the presented method are also covered. After evaluating the proposed method on artificial cloud images, this paper concludes with results and discussion of possible applications for such systems.

scholarly journals Automatic Analysis of Bees’ Waggle Dance

2020 ◽  
Author(s):  
Jordan Reece ◽  
Margaret Couvillon ◽  
Christoph Grüter ◽  
Francis Ratnieks ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Background Subtraction ◽  
Time Frame ◽  
Waggle Dance ◽  
Automatic Analysis ◽  
Learning Techniques ◽  
Low Pass ◽  
Processing Steps ◽  
Traditional Image

AbstractThis work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques.

A Basic Introduction to Diffusion Tensor Imaging Mathematics and Image Processing Steps

2017 ◽  
Vol 06 (02) ◽  
Author(s):  
Venkateswaran Rajagopalan ◽  
Zhiguo Jiang ◽  
Guang H Yue ◽  
Jelena Stojanovic Radic ◽  
Erik P Pioro ◽  
...  
Keyword(s):  
Image Processing ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Processing Steps

APPLICATION OF MORPHOLOGICAL IMAGE PROCESSING TO REMOVE HAIR NOISES WHILE ENHANCING PATTERNS OF NEAR-INFRARED HUMAN VEIN IMAGES

2021 ◽  
pp. 69-75
Author(s):  
Thuan
Keyword(s):  
Image Processing ◽  
Near Infrared ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Structural Element ◽  
New Approach ◽  
Adaptive Histogram Equalization ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Processing Steps

Recently, the innovative vein finder system has been studied extensively and has many practical uses in healthcare and security. Developing a better vein finder system often relies on image processing procedures which help to enhance the vein images. Conventional image processing procedures as median filtering and adaptive histogram equalization have shown benefit in enhancing vein patterns. However, in some cases when there are hairs present in the images, most of these procedures are less effective in removing noises from hairs. In this work, we present a new approach employed additional morphological image processing procedures to efficiently remove hair noises. We have successfully constructed a vein finder device to acquire vein images and demonstrate the advantage of our approach. Effects of the size and shape of the structural element in different morphological image processing steps were studied and optimized to achieve the best enhancement effect. Our approach can be applied widely to other vein finder systems and enhance vein images from various parts of the human body.

Identification of Eutectic Microstructure by HPP Cellular Automata in Aluminum Alloys

2017 ◽  
Vol 885 ◽  
pp. 228-233
Author(s):  
Kornél Bortnyik ◽  
Péter Barkóczy
Keyword(s):  
Image Processing ◽  
Aluminum Alloys ◽  
Cellular Automata ◽  
Cellular Automaton ◽  
Evaluation Method ◽  
Eutectic Structure ◽  
Computational Method ◽  
Materials Engineering ◽  
Analysis Workflow ◽  
Processing Steps

The eutectic structure of aluminum alloys has different morphology. To describe these a different image processing and analysis workflow needs to be built. To assign the different image processing steps to the different structure automatically a computational method had to be developed. In the image processing methods several cellular automata operate. For this expediently a cellular automaton was developed to classify the different eutectic structures. In materials engineering applications a HPP automata is used extensively therefore this type of automata were chosen to solve the mentioned problem. This article shows the simplicity of this method as well as the desired evaluation method.

scholarly journals Digital camera zooming based on unified CFA image processing steps

2004 ◽  
Vol 50 (1) ◽  
pp. 15-24 ◽  
Author(s):  
R. Lukac ◽  
K. Martin ◽  
K.N. Platanoitis
Keyword(s):  
Image Processing ◽  
Digital Camera ◽  
Processing Steps

scholarly journals MORPHO-SPECTRAL RECOGNITION OF DENSE URBAN OBJECTS BY HYPERSPECTRAL IMAGERY

2015 ◽  
Vol XL-3/W3 ◽  
pp. 433-438 ◽  
Author(s):  
S. Gadal ◽  
W. Ouerghemmi
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Urban Areas ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Object Oriented ◽  
Components Analysis ◽  
Spectral Recognition ◽  
Processing Steps ◽  
Spectral Database

This paper presents a methodology for recognizing, identifying and classifying built objects in dense urban areas, using a morphospectral approach applied to VNIR/SWIR hyperspectral image (HySpex). This methodology contains several image processing steps: Principal Components Analysis and Laplacian enhancement, Feature Extraction of segmented build-up objects, and supervised classification from a morpho-spectral database (i.e. spectral and morphometric attributes). The Feature Extraction toolbox automatically generates a vector map of segmented buildings and an urban object-oriented morphometric database which is merged with an independent spectral database of urban objects. Each build-up object is spectrally identified and morphologically characterized thanks to the built-in morpho-spectral database.

scholarly journals Camera ISP Modification to Enable Image De-rendering

2020 ◽  
Vol 2020 (28) ◽  
pp. 199-204
Author(s):  
Abhijith Punnappurath ◽  
Michael S. Brown
Keyword(s):  
Image Processing ◽  
Mapping Function ◽  
Practical Solution ◽  
Wide Range ◽  
Final Output ◽  
Rendering Pipeline ◽  
Image Signal Processor ◽  
Reverse Mapping ◽  
Processing Steps ◽  
Dedicated Hardware

A camera's image signal processor (ISP) is dedicated hardware that performs a series of processing steps to render a captured raw sensor image to its final display-referred output suitable for viewing and sharing. It is often desirable to be able to revert – or de-render – the ISP-processed image back to the original raw sensor image. Undoing the ISP rendering, however, is not an easy task. This is because ISPs perform many nonlinear routines in the rendering pipeline that are difficult to invert. Moreover, modern cameras often apply scene-specific image processing, resulting in a wide range of possible ISP parameters. In this paper, we propose a modification to the ISP that allows the ISP-rendered image to be reverted back to a raw image. Our approach works by appending a fixed-sampling of the raw sensor values to all captured images. The appended raw samples comprise no more than 8 rows of pixels in the full-sized image and represent a negligible overhead given that 12–16 MP sensors typically have 3000 rows of pixels or more. The appended pixels are rendered along with the captured image to the final output. From these rendered raw samples, a reverse mapping function can be computed to undo the ISP processing. We demonstrate that this method performs almost on par with competing state-ofthe-art approaches for ISP de-rendering while offering a practical solution that is integrable to current camera ISP hardware.

Sign in / Sign up

Export Citation Format

Share Document