AN INTELLIGENT CAMERA FOR ACTIVE VISION

1996 ◽  
Vol 10 (01) ◽  
pp. 33-42 ◽  
Author(s):  
L.-F. PAU
Keyword(s):  
Image Processing ◽  
Traffic Control ◽  
Active Vision ◽  
Image Data ◽  
Sensors And Actuators ◽  
Industrial Companies ◽  
Generic Library ◽  
Processor Unit ◽  
Commercial Applications ◽  
Almost All

Much research is currently going on about the processing of one or two-camera imagery, possibly combined with other sensors and actuators, in view of achieving attentive vision, i.e. processing selectively some parts of a scene possibly with another resolution. Attentive vision in turn is an element of active vision where the outcome of the image processing triggers changes in the image acquisition geometry and/or of the environment. Almost all this research is assuming classical imaging, scanning and conversion geometries, such as raster based scanning and processing of several digitized outputs on separate image processing units. A consortium of industrial companies comprising Digital Equipment Europe, Thomson CSF, and a few others, have taken a more radical view of this. To meet active vision requirements in industry, an intelligent camera is being designed and built, comprised of three basic elements: – a unique Thomson CSF CCD sensor architecture with random addressing – the DEC Alpha 21064 275MHz processor chip, sharing the same internal data bus as the digital sensor output – a generic library of basic image manipulation, control and image processing functions, executed right in the sensor-internal bus-processor unit, so that only higher level results or commands get exchanged with the processing environment. Extensions to color imaging (with lower spatial resolution), and to stereo imaging, are relatively straightforward. The basic sensor is 1024*1024 pixels with 2*10 bits addresses, and a 2.5 ms (400 frames/second) image data rate compatible with the Alpha bus and 64 bits addressing. For attentive vision, several connex fields of max 40 000 pixels, min 5*3 pixels, can be read and addressed within each 2.5 ms image frame. There is nondestructive readout, and the image processing addressing over 64 bits shall allow for 8 full pixel readouts in one single word. The main difficulties have been identified as the access and reading delays, the signal levels, and dimensioning of some buffer arrays in the processor. The commercial applications targeted initially will be in industrial inspection, traffic control and document imaging. In all of these fields, selective position dependent processing shall take place, followed by feature dependent processing. Very large savings are expected both in terms of solutions costs to the end users, development time, as well as major performance gains for the ultimate processes. The reader will appreciate that at this stage no further implementation details can be given.

scholarly journals Learning Photogrammetry with Interactive Software Tool PhoX

2016 ◽  
Vol XLI-B6 ◽  
pp. 39-44 ◽  
Author(s):  
T. Luhmann
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Software Tool ◽  
Quality Parameters ◽  
University Teaching ◽  
Analysis Tool ◽  
Orientation Data ◽  
Wide Range ◽  
High Level ◽  
Almost All

Photogrammetry is a complex topic in high-level university teaching, especially in the fields of geodesy, geoinformatics and metrology where high quality results are demanded. In addition, more and more black-box solutions for 3D image processing and point cloud generation are available that generate nice results easily, e.g. by structure-from-motion approaches. Within this context, the classical approach of teaching photogrammetry (e.g. focusing on aerial stereophotogrammetry) has to be reformed in order to educate students and professionals with new topics and provide them with more information behind the scene. Since around 20 years photogrammetry courses at the Jade University of Applied Sciences in Oldenburg, Germany, include the use of digital photogrammetry software that provide individual exercises, deep analysis of calculation results and a wide range of visualization tools for almost all standard tasks in photogrammetry. During the last years the software package PhoX has been developed that is part of a new didactic concept in photogrammetry and related subjects. It also serves as analysis tool in recent research projects. PhoX consists of a project-oriented data structure for images, image data, measured points and features and 3D objects. It allows for almost all basic photogrammetric measurement tools, image processing, calculation methods, graphical analysis functions, simulations and much more. <br><br> Students use the program in order to conduct predefined exercises where they have the opportunity to analyse results in a high level of detail. This includes the analysis of statistical quality parameters but also the meaning of transformation parameters, rotation matrices, calibration and orientation data. As one specific advantage, PhoX allows for the interactive modification of single parameters and the direct view of the resulting effect in image or object space.

scholarly journals Learning Photogrammetry with Interactive Software Tool PhoX

2016 ◽  
Vol XLI-B6 ◽  
pp. 39-44
Author(s):  
T. Luhmann
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Software Tool ◽  
Quality Parameters ◽  
University Teaching ◽  
Analysis Tool ◽  
Orientation Data ◽  
Wide Range ◽  
High Level ◽  
Almost All

Photogrammetry is a complex topic in high-level university teaching, especially in the fields of geodesy, geoinformatics and metrology where high quality results are demanded. In addition, more and more black-box solutions for 3D image processing and point cloud generation are available that generate nice results easily, e.g. by structure-from-motion approaches. Within this context, the classical approach of teaching photogrammetry (e.g. focusing on aerial stereophotogrammetry) has to be reformed in order to educate students and professionals with new topics and provide them with more information behind the scene. Since around 20 years photogrammetry courses at the Jade University of Applied Sciences in Oldenburg, Germany, include the use of digital photogrammetry software that provide individual exercises, deep analysis of calculation results and a wide range of visualization tools for almost all standard tasks in photogrammetry. During the last years the software package PhoX has been developed that is part of a new didactic concept in photogrammetry and related subjects. It also serves as analysis tool in recent research projects. PhoX consists of a project-oriented data structure for images, image data, measured points and features and 3D objects. It allows for almost all basic photogrammetric measurement tools, image processing, calculation methods, graphical analysis functions, simulations and much more. <br><br> Students use the program in order to conduct predefined exercises where they have the opportunity to analyse results in a high level of detail. This includes the analysis of statistical quality parameters but also the meaning of transformation parameters, rotation matrices, calibration and orientation data. As one specific advantage, PhoX allows for the interactive modification of single parameters and the direct view of the resulting effect in image or object space.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joan Carles Puchalt ◽  
Antonio-José Sánchez-Salmerón ◽  
Eugenio Ivorra ◽  
Silvia Llopis ◽  
Roberto Martínez ◽  
...  
Keyword(s):  
Image Processing ◽  
Caenorhabditis Elegans ◽  
Culture Media ◽  
Active Vision ◽  
Automated System ◽  
Rank Test ◽  
P Value ◽  
Vision Systems ◽  
C Elegans ◽  
Processing Techniques

AbstractTraditionally Caenorhabditis elegans lifespan assays are performed by manually inspecting nematodes with a dissection microscope, which involves daily counting of live/dead worms cultured in Petri plates for 21–25 days. This manual inspection requires the screening of hundreds of worms to ensure statistical robustness, and is therefore a time-consuming approach. In recent years, various automated artificial vision systems have been reported to increase the throughput, however they usually provide less accurate results than manual assays. The main problems identified when using these vision systems are the false positives and false negatives, which occur due to culture media changes, occluded zones, dirtiness or condensation of the Petri plates. In this work, we developed and described a new C. elegans monitoring machine, SiViS, which consists of a flexible and compact platform design to analyse C. elegans cultures using the standard Petri plates seeded with E. coli. Our system uses an active vision illumination technique and different image-processing pipelines for motion detection, both previously reported, providing a fully automated image processing pipeline. In addition, this study validated both these methods and the feasibility of the SiViS machine for lifespan experiments by comparing them with manual lifespan assays. Results demonstrated that the automated system yields consistent replicates (p-value log rank test 0.699), and there are no significant differences between automated system assays and traditionally manual assays (p-value 0.637). Finally, although we have focused on the use of SiViS in longevity assays, the system configuration is flexible and can, thus, be adapted to other C. elegans studies such as toxicity, mobility and behaviour.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Color image analysis for vehicle speed measurement

1993 ◽  
Vol 20 (2) ◽  
pp. 228-235 ◽  
Author(s):  
Yean-Jye Lu ◽  
Xidong Yuan
Keyword(s):  
Image Analysis ◽  
Traffic Control ◽  
Road Traffic ◽  
Color Image ◽  
Image Data ◽  
Difficult Problem ◽  
Vehicle Speed ◽  
Color Image Analysis ◽  
Speed Measurement ◽  
Monochrome Image

Image analysis for traffic data collection has been studied throughout the world for more than a decade. A survey of existing systems shows that research was focused mainly on the monochrome image analysis and that the field of color image analysis was rarely studied. With the application of color image analysis in mind, this paper proposes a new algorithm for vehicle speed measurement in daytime. The new algorithm consists of four steps: (i) image input, (ii) pixel analysis, (iii) single image analysis, and (iv) image sequence analysis. It has three significant advantages. First, the algorithm can distinguish the shadows caused by moving vehicles outside the detection area from the actual vehicles passing through the area, which is a difficult problem for the monochrome image analysis technique to handle. Second, the algorithm significantly reduces the image data to be processed; thus only a personal computer is required without the addition of any special hardware. The third advantage is the flexible placement of detection spots at any position in the camera's field of view. The accuracy of the algorithm is also discussed. Key words: speed measurement, vehicle detection, image analysis, image processing, traffic control, traffic measurement and road traffic.

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