Dataflow orchestration of image processing algorithms using high-level petri nets

Author(s):  
Bjorn Wagner ◽  
Andreas Dinges ◽  
Paul Muller
2020 ◽  
Vol 12 (3) ◽  
pp. 407-414
Author(s):  
Mohanad Abdulhamid ◽  
◽  
Lwanga Wanjira ◽  

Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.


2011 ◽  
Vol 12 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Imran Moez Khan

Visual CAPTCHAs are widely used these days on the Internet as a means of distinguishing between humans and computers. They help protect servers from being flooded by requests from malicious scripts. However, they are not very secure. Numerous image processing algorithms are able to discern the characters used in the CAPTCHAs. It has been suggested that CAPTCHAs can be made more secure if they are distorted in ways that makes segmentation difficult. However, out of all the reviewed distortions present in current CAPTCHAs there are none that allow for a high level of segmentation difficulty. Furthermore, CAPTCHAs also need to be used by humans who may not find certain distortions tolerable. Thus, the problem of selecting a good distortion becomes a tradeoff between user acceptability and computer solvability. It is hypothesized in this paper that rather than use low-level image distortions, optical distortions based on the Gestalt laws of perception that govern human visual system models should be applied. These distortions would ensure widespread user acceptability (as they are based on the internal workings of the HVS), and be very difficult for computers to solve (as HVS perception models have been difficult to implement in computers). This paper aims to explore the feasibility of employing Gestalt-inspired distortion in CAPTCHAs by first implementing a CAPTCHA cracker and then evaluating the performance of some manually generated Gestalt CAPTCHA’s against some existing CAPTCHAs.


Author(s):  
César D. Fermin ◽  
Dale Martin

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.


Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.


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