Digital Image Processing Applications Research Articles

Digital Image and Video Processing: Algorithms and Applications

Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings. When it comes to image and video processing, the input is an image of poor quality, and the output is an image and video of higher quality. Research on algorithms and applications of digital image and video processing is the primary purpose of this study, which aims to investigate these topics extensively. The methodology employed in this study is qualitative research technique. In accordance with the findings of this research, "Image Processing" refers to the process of analyzing images with the objective of determining the significance of objects and identifying them. Image analysts analyze data that has been remotely sensed and attempt to detect, identify, classify, measure, and evaluate the significance of physical and cultural objects, as well as their patterns and spatial relationships. One subcategory of signal processing is known as video processing, and it is distinguished by the fact that the signals that are input and output are video files or video streams. Technology such as television sets, videocassette recorders (VCRs), DVD players, and other devices all make use of video processing algorithms. The processing of images and videos is extremely useful in a variety of contexts.

Image compression and reconstruction using improved Stockwell transform for quality enhancement

Image compression is an important stage in picture processing since it reduces the data extent and promptness of image diffusion and storage, whereas image reconstruction helps to recover the original information that was communicated. Wavelets are commonly cited as a novel technique for image compression, although the production of waves proceeding smooth areas with the image remains unsatisfactory. Stockwell transformations have been recently entered the arena for image compression and reconstruction operations. As a result, a new technique for image compression based on the improved Stockwell transform is proposed. The discrete cosine transforms, which involves bandwidth partitioning is also investigated in this work to verify its experimental results. Wavelet-based techniques such as multilevel Haar wavelet, generic multiwavelet transform, Shearlet transform, and Stockwell transforms were examined in this paper. The MATLAB technical computing language is utilized in this work to implement the existing approaches as well as the suggested improved Stockwell transform. The standard images mostly used in digital image processing applications, such as Lena, Cameraman and Barbara are investigated in this work. To evaluate the approaches, quality constraints such as mean square error (MSE), normalized cross-correlation (NCC), structural content (SC), peak noise ratio, average difference (AD), normalized absolute error (NAE) and maximum difference are computed and provided in tabular and graphical representations.

KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method.  The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data.  The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit.  The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.

FPGA Implementation of DFT Processor using Vedic Multiplier

Many a times Mathematical computations are easier in frequency domain than time domain. Discrete Fourier transform (DFT) is a tool to convert signals from time domain to frequency domain which is widely used mainly in Digital Signal and Image Processing applications. Fast Fourier Transform (FFT) is a method to find DFT in time constraint applications which is affected by number of complex multiplier. In this paper 16-bit DFT using Vedic Multiplier, a high- speed multiplier is proposed with an objective to replace conventional complex multiplier and to decrease computation time. The proposed system is implemented in Virtex-4 FPGA and the results show that FFT using Urdhva Tiryakbhyam algorithm of Vedic multiplier is faster than other methods of DFT

Automated precision beekeeping for accessing bee brood development and behaviour using deep CNN.

Bees play a significant role in the health of terrestrial ecosystems. The decline of bee populations due to colony collapse disorder around the world constitutes a severe ecological danger. Maintaining high yield of honey and understanding of bee behaviour necessitate constant attention to the hives. Research initiatives have been taken to establish monitoring programs to study the behaviour of bees in accessing their habitat. Monitoring the sanitation and development of bee brood allows for preventative measures to be taken against mite infections and an overall improvement in the brood's health. This study proposed a precision beekeeping method that aims to reduce bee colony mortality and improve conventional apiculture through the use of technological tools to gather, analyse, and understand bee colony characteristics. This research presents the application of advanced digital image processing with computer vision techniques for the visual identification and analysis of bee brood at various developing stages. The beehive images are first preprocessed to enhance the important features of object. Further, object is segmented and classified using computer vision techniques. The research is carried out with the images containing variety of immature brood stages. The suggested method and existing methods are tested and compared to evaluate efficiency of proposed methodology.

Recovering sign bits of DCT coefficients in digital images as an optimization problem

Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication. This paper investigates the recovery of sign bits in DCT coefficients of digital images, by proposing two different approximation methods to solve a mixed integer linear programming (MILP) problem, which is NP-hard in general. One method is a relaxation of the MILP problem to a linear programming (LP) problem, and the other splits the original MILP problem into some smaller MILP problems and an LP problem. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate their performances. The experimental results showed that the proposed methods outperformed other existing methods by a substantial margin, both according to objective quality metrics and our subjective evaluation.

Digital image processing applications in agriculture with a machine learning approach

Abstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production.

Digital image processing (DIP) applied to the analysis of the use and coverage of São Sebastião, federal district, Brazil

The article explores the application of Digital Image Processing (DIP) and Remote Sensing (RS) techniques in analyzing land use and land cover changes in the Administrative Region of São Sebastião, Federal District, Brazil. DIP plays a pivotal role in automated data analysis and interpretation, particularly in the context of RS, where it efficiently processes Earth's surface data collected by sensors on satellites, aircraft, and Unmanned Aerial Vehicles (UAVs). The study covers the period from 2000 to 2020, utilizing data from satellites such as Landsat to provide a continuous view of the region. The spatial and temporal analyses incorporate considerations of hypsometry, slope, and surface temperature, aiming to understand the dynamics of land use and its impact on the microclimate. The methodology involves a comprehensive literature review, acquisition of spatial data from government agencies, and the use of computational tools such as ArcGIS and ENVI. Classification of Landsat images employs false-color compositions and Maximum Likelihood Classification to identify urban areas, water bodies, vegetation, shrubland, and exposed soil. The analysis also includes generating land surface temperature maps. Results indicate significant urban expansion, particularly in the Southeast direction, with a concentration of urban activities. The study highlights a continuous growth trend over the years, emphasizing the importance of understanding and monitoring these transformations for sustainable planning. The article contributes to the field by providing valuable insights into the dynamics of urban growth, offering a clear spatial distribution of changes over time. The detailed methodology ensures the accuracy and reliability of the analysis, supporting informed decision-making in the context of environmental exploration and regional development planning.

Application of digital image processing to mortar crack analysis

Image analysis techniques can be used to detect and interpret the degradation processes that a material undergoes and to help identify the causes and mechanisms of degradation. Structures and morphological changes are also analysed to establish hypotheses about physical changes. Together with complementary analytical techniques, chemical and mineralogical changes can be evaluated.The methodological process consists of a sequential simplification of the initial micrograph: segmentation of the image, cleaning and isolation of the crack from associated elements, and crack skeletonisation. This method allows the previous image to be processed, thus successfully isolating the microcracks. It is also valid for their quantification.

FAC: A Fault-Tolerant Design Approach Based on Approximate Computing

This article introduces a new fault-tolerant design approach based on approximate computing, called FAC, for designing redundant circuits and systems. Traditionally, triple modular redundancy (TMR) has been used to ensure complete tolerance to any single fault or a faulty processing unit, where the processing unit may be a circuit or a system. However, TMR incurs more than 200% overhead in terms of area and power compared to a single processing unit. Alternative redundancy approaches have been proposed in the literature to mitigate these overheads associated with TMR, but they provide only partial or moderate fault tolerance. Among the alternatives, majority voting-based reduced precision redundancy (MVRPR) may be useful for error-resilient applications such as digital signal processing. While MVRPR guarantees only moderate fault tolerance, the proposed FAC is well-suited for error-resilient applications and ensures 100% tolerance to any single fault or a faulty processing unit, like TMR. In this work, we evaluate the performance of TMR, MVRPR, and FAC for a digital image processing application. The image processing results obtained demonstrate the effectiveness of FAC. Moreover, when the processing unit is implemented using a 28-nm CMOS technology, FAC achieves significant improvements over TMR, including a 15.3% reduction in delay, a 19.5% reduction in area, and a 24.7% reduction in power. Compared to MVRPR, FAC exhibits notable enhancements, with an 18% reduction in delay, a 5.4% reduction in area, and an 11.2% reduction in power. When considering the power-delay product, which reflects energy efficiency, FAC demonstrates a 36.2% reduction compared to TMR and a 27.2% reduction compared to MVRPR. When considering the power-delay-area product, which represents design efficiency, FAC achieves a 48.7% reduction compared to TMR and a 31.1% reduction compared to MVRPR.

Fuzzy Salt and Pepper Noise Removing for Color Images

Within the field of digital image processing applications, observed images frequently exposed to noise corruption stemming from image acquisition or transmission processes. This noise degradation reduces image quality and yields unfavorable outcomes in subsequent processing stages (e.g., segmentation, pattern recognition, and enhancement). Consequently, the mitigation of noise in images assumes paramount importance in the domain of image processing. This study introduces an algorithm centered around fuzzy logic for removing impulse noise from color images. The efficiency of the proposed algorithms is assessed by comparing their performance against various noise reduction methods. Objective metrics, namely peak signal-to-noise ratio and mean square error, substantiate that the proposed algorithms yield commendable outcomes in noise reduction and the preservation of intricate image details across a wide spectrum of noise densities

Retracted: Application of Digital Image Processing and GIS Technology in the Evaluation of Rationality of Urban and Rural Planning Spatial Layout

Retracted: Application of Digital Image Processing and GIS Technology in the Evaluation of Rationality of Urban and Rural Planning Spatial Layout

A Low Latency Approximate Adder Design Based on Dual Sub-Adders With Error Recovery

This paper presents a novel dual sub-adder based approximate adder that splits a precise adder into two to significantly reduce the latency. The proposed error recovery and reduction technique effectively compensates for the catastrophic accuracy degradation incurred by the split. Implemented in a 65- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$nm$</tex-math></inline-formula> CMOS technology, our design reduces the delay and energy consumption by up to 68% and 78%, respectively, compared to a traditional adder, and outperforms other existing approximate adders in hardware and accuracy joint metrics. The proposed design's efficacy is shown through digital image processing applications where our design makes processed output images closest to the one using the accurate adder over other approximate adders. Furthermore, the proposed design is proven to be a general adder model that can be employed in existing approximate adders to enhance hardware resource and error characteristics considerably.

Analysis of Redundancy Techniques for Electronics Design—Case Study of Digital Image Processing

Electronic circuits/systems operating in harsh environments such as space are likely to experience faults or failures due to the impact of high-energy radiation. Given this, to overcome any faults or failures, redundancy is usually employed as a hardening-by-design approach. Moreover, low power and a small silicon footprint are also important considerations for space electronics since these translate into better energy efficiency, less system weight, and less cost. Therefore, the fault-tolerant design of electronic circuits and systems should go hand in hand with the optimization of design metrics, especially for resource-constrained electronics such as those used in space systems. A single circuit or system (also called a simplex implementation) is not fault-tolerant as it may become a single point of failure and is not used for a space application. As an alternative, a triple modular redundancy (TMR) implementation, which uses three identical copies of a circuit or system and a voter to perform majority voting of the circuits and systems outputs, may be used. However, in comparison with a simplex implementation, a TMR implementation consumes about 200% more area and dissipates 200% more power when circuits or systems are triplicated. To mitigate the area and power overheads of a TMR implementation compared to a simplex implementation, researchers have suggested alternative redundancy approaches such as selective TMR (STMR) insertion, partially approximate TMR (PATMR), fully approximate TMR (FATMR), and majority voting-based reduced precision redundancy (VRPR). Among these, VRPR appears to be promising, especially for inherently error-tolerant applications such as digital image/video/audio processing, which is relevant to space systems. However, the alternative redundancy approaches mentioned are unlikely to be suitable for the implementation of control logic. In this work, we analyze various redundancy approaches and evaluate the performance of TMR and VRPR for a digital image processing application. We provide MATLAB-based image processing results corresponding to TMR and VRPR and physical implementation results of functional units based on TMR and VRPR using a 28-nm CMOS technology.

Image defogging algorithm and artificial intelligence technology innovation application in digital media image processing

Image defogging algorithm and artificial intelligence technology innovation application in digital media image processing

Real-time approximate and combined 2D convolvers for FPGA-based image processing

Convolution widely has been used as the main part of the improvement in digital image processing applications. In convolutional computations, a large number of memory accesses and a huge amount of computations challenge its performance. Many of the related proposed convolvers are based on exact computations. Although exact convolvers keep the accuracy of the convolution operation at the top level, sometimes by missing a negligible amount of accuracy, the performance can be improved. Approximate computing is a new technique for solving computation overhead problems. In this paper, approximate 2D convolvers are presented which minimize the memory access rate and computations by a special factor of multiply-and-accumulate (MAC) terms. On the other hand, to preserve the flexibility for supporting different required accuracy, the proposed approximate convolvers are combined with the exact designs with real-time pre-processing stages by exploiting innovative methods which manage the hardware overhead. In comparison with conventional convolvers, the proposed designs improve the number of active resources which causes a significant reduction in power consumption. For 3 × 3 kernel size, the evaluation results on the Xilinx Virtex-7 (XC7V2000t) FPGA device show 34% and 20% power optimization of the proposed approximate and combined convolvers, respectively, in comparison with exact convolver (EC). Also, this improvement grows by increasing the kernel size. Finally, a comparison based on RMSE and PSNR for different sample images and filters reveals that the error rate and image quality reduction are acceptable for many real-time image processing applications.

Image Convolution to Obtain Color ROI after Segmentation Process with Fuzzy Cmeans

Image segmentation is still an important concern in terms of digital image processing. Segmentation refers to dividing an image into several parts based on similar characteristics or uniformity. Its use is quite important, especially related to the analysis and application of digital image processing. The challenge faced is separating the object image from its background in images with complex backgrounds. The aim of this research is to separate tomatoes from simple to complex backgrounds. This paper proposes a convolution method of segmented binary images and RBG images all based on contours using Fuzzy C-means and reconstruction operations to obtain the foreground from an image with a complex background. This method has been tested on ripe tomatoes with various backgrounds. This method has Indicated Performance Achievement Sc = 99.2%, Fpe = 0.6% and FNe = 0.4%. This shows that the method is suitable and robust for the dataset used in this study, especially if it will be continued for further work related to the classification of tomato maturity assessment.

Development of anthropomorphic computational phantoms at the UFPE

To evaluate the amount of energy deposited in radiosensitive organs and tissues of the human body, when an anthropomorphic phantom is irradiated, researchers in numerical dosimetry use the so-called exposure computational models (ECMs). One can imagine an ECM as a virtual scene composed of a phantom in a mathematically defined position in relation to a radioactive source. The source in these ECMs produces the initial state of the simulation:  the position, direction, and energy with which each particle enters the phantom are essential variables. For subsequent states of a particle history, robust Monte Carlo (MC) codes are used. For the subsequent states of a particle's history, robust Monte Carlo (MC) codes are used, which simulate the average free path that the particle performs without interacting, its interaction with the atoms in the medium and the amount of energy deposited per interaction. MC codes also evaluate normalization quantities, so the results are printed in text files in the form of conversion coefficients between the absorbed dose and the selected normalization quantity. From the 2000s, the authors have published ECMs where a voxel phantom is irradiated by photons in the environment of the MC code EGSnrc (EGS = Electron Gamma Shower; nrc = National Research Council Canada). The production of articles, dissertations and theses required the use of specific computational tools, such as the FANTOMAS, DIP (Digital Image Processing) and Monte Carlo applications, for the various steps of numerical dosimetry, which ranges from the preparation of input files to the execution from the ECM to the organization and graphical and numerical analysis of the results. This article reviews computational phantoms for dosimetry mainly those produced in DEN-UFPE dissertations and thesis.

Practical Application of Digital Image Processing in Measuring Concrete Crack Widths in Field Studies

Cracking in concrete structures is a ubiquitous problem and affects the serviceability of such structures. In many cases, it is imperative to properly measure and monitor a crack’s size to identify locations for repair and rehabilitation. This paper describes two field studies that used digital image processing to measure the width of cracks in concrete structures. The first field study compared crack measurements from digital image processing, a handheld microscope, and a crack gauge card. In the second field study, digital image processing was used to measure end region cracks in precast pretensioned concrete girders. Guidance is provided for engineers who wish to use digital image processing in field studies. Conditions where digital image processing may lead to errors are identified, and limitations of the methods are discussed. In general, the studies demonstrate that image processing methods can efficiently measure the size of cracks in concrete structures in field settings and that image-based measurements are comparable to microscope measurements.

IOT-Enabled Model for Weed Seedling Classification: An Application for Smart Agriculture

Smart agriculture is a concept that refers to a revolution in the agriculture industry that promotes the monitoring of activities necessary to transform agricultural methods to ensure food security in an ever-changing environment. These days, the role of technology is increasing rapidly in every sector. Smart agriculture is one of these sectors, where technology is playing a significant role. The key aim of smart farming is to use the technologies to increase the quality and quantity of agricultural products. IOT and digital image processing are two commonly utilized technologies, which have a wide range of applications in agriculture. IOT is an abbreviation for the Internet of things, i.e., devices to execute different functions. Image processing offers various types of imaging sensors and processing that could lead to numerous kinds of IOT-ready applications. In this work, an integrated application of IOT and digital image processing for weed plant detection is explored using the Weed-ConvNet model to provide a detailed architecture of these technologies in the agriculture domain. Additionally, the regularized Weed-ConvNet is designed for classification with grayscale and color segmented weed images. The accuracy of the Weed-ConvNet model with color segmented weed images is 0.978, which is better than 0.942 of the Weed-ConvNet model with grayscale segmented weed images.