Design Of Image Processing Algorithms Research Articles

IoT Based Image Processing Filters

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

IoT Based Image Processing Filters

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring

In 2014, we designed and implemented BeePi, a multi-sensor electronic beehive monitoring system. Since then we have been using BeePi monitors deployed at different apiaries in northern Utah to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth beehives. Since our first publication on BeePi in 2016, we have received multiple requests from researchers and practitioners for the datasets we have used in our research. The main objective of this article is to provide a comprehensive point of reference to the datasets that we have so far curated for our research. We hope that our datasets will provide stable performance benchmarks for continuous electronic beehive monitoring, help interested parties verify our findings and correct errors, and advance the state of the art in continuous electronic beehive monitoring and related areas of AI, machine learning, and data science.

Deposition height detection and feature point extraction in robotic GTA-based additive manufacturing using passive vision sensing

Automatic detection of deposition height is one of the key technologies for metal components fabricated in robotic gas tungsten arc (GTA) based additive manufacturing. In this research, the deposition height, defined as the tungsten tip to the top layer distance, is monitored by a passive vision sensor consisting of a camera and optical filters. Characteristic parameters of molten pool tail are extracted by designed image processing algorithms. An innovative tracking algorithm is developed to determine the location of the solid-liquid separation point at the molten pool tail by means of continuous images. Finally, the detection approach is verified through depositing multi-layer single-pass parts, proving the excellent effectiveness and anti-interference ability of the tracking algorithm in robotic GTA-based additive manufacturing.

Library for model-based design of image processing algorithms on FPGAs

This paper describes a library (XSGImgLib) that includes parameterizable blocks to implement low-level image processing tasks on FPGAs. A modelbased design technique provided by Xilinx System Generator (XSG) has been used to design the blocks, which implement point operation (binarization) and neighborhood operations (linear and non-linear filtering) in grayscale images. The blocks are parameterizable for input/output data precision, window size, normalization strategy, and implementation options (area versus speed optimization). The paper includes the implementation results obtained after fixing these options and exemplifies the combination of several blocks of the library to build a complete design for image segmentation purposes.

An image representation of infrastructure based on non-classical receptive field

Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. A series of statistical analyses revealed that this model not only produces compact and abstract approximations of images, but also retains their primary visual features. In addition, the improved representation was found to substantially facilitate contour detection and image segmentation. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.

General Structure Design for Fast Image Processing Algorithms Based upon FPGA DSP Slice

Abstract Increasing the speed and accuracy for a fast image processing algorithms during computing the image intensity for low level 3x3 algorithms with different kernel but having the same parallel calculation method is our target to achieve in this paper. FPGA is one of the fastest embedded systems that can be used for implementing the fast image processing image algorithms by using DSP slice module inside the FPGA we aimed to get the advantage of the DSP slice as a faster, accurate, higher number of bits in calculations and different calculated equation maneuver capabilities. Using a higher number of bits during algorithm calculations will lead to a higher accuracy compared with using the same image algorithm calculations with less number of bits, also reducing FPGA resources as minimum as we can and according to algorithm calculations needs is a very important goal to achieve. So in the recommended design we used as minimum DSP slice as we can and as a benefit of using DSP slice is higher calculations accuracy as the DSP capabilities of having 48 bit accuracy in addition and 18 x 18 bit accuracy in multiplication. For proofing the design, Gaussian filter and Sobelx edge detector image processing algorithms have been chosen to be implemented. Also we made a comparison with another design for proofing the improvements of the accuracy and speed of calculations, the other design as will be mentioned later on this paper is using maximum 12 bit accuracy in adding or multiplying calculations.

Design of Linear Equalizers Optimized for the Structural Similarity Index

We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm.

Design of image exploring agent using genetic programming

This paper presents a new methodology for the design of image processing algorithms. The presented approach is based on the conception and design of “image exploring agents”. Image exploring agents iteratively run a sense-compute-act loop. While all loop processing is performed within the image on a local base, the trace of the agent, which results from the repetition of this loop, is a global image property. Hence, a given global recognition task is represented by a corresponding task of finding a suitable, local-based computation. The exploitation of genetic programming for finding suitable computational parts of the agent is considered. A case study is included, in which a crack detector is designed and improved by means of an image exploring agent. The functionality of this agent is studied, and it is used for designing an image processing algorithm for crack detection. Also, further improvements of the presented agent concept are given in this paper.

Tests for connectivity preservation for parallel reduction operators

Connectivity preservation is a concern in the design of image processing algorithms for parallel reduction processes like thinning where connected components in 2-D images are reduced to medial axis approximations. Tests and proof techniques, which use local support for their computations, have been presented by Rosenfeld, Ronse and others to prove preservation of connectivity for reduction classes of thinning algorithms. In this paper the earlier Rosenfeld proof techniques are characterized as connectivity preservation tests for rectangular and hexagonal tessellations and simplified versions of these tests are developed. The later Ronse tests are characterized for rectangular and hexagonal tessellations; the Ronse test complexities are evaluated; and relevant equivalences are identified between the Rosenfeld and Ronse test forms. It is shown that forms of the Ronse tests for rectangular and hexagonal tessellations are derivable directly and simply from tests derived from the earlier Rosenfeld proof techniques. Arguments are given to establish under what operator support restrictions these tests are necessary and sufficient proofs for key connectivity preservation properties. Finally, a specific computer-based implementation of the Ronse test is described with extensions for reduction operators with larger supports and with reduction operators using subfields notions; and time performance results are given for this implementation.