Color Edge Detection Research Articles

Furniture design based on image color extraction algorithm

With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.

Segmentation Method of Concrete Small Cracks Based on UAV Images

Introduction:: Cracks are one of the major problems in modern concrete buildings, especially in locations that are difficult to map manually, such as bridges and high-rise buildings. Accurate analysis of unmanned aerial vehicle (UAV) images has become the key to determining whether a building needs maintenance. Methods:: Traditional image processing methods are easily interfered by high-frequency background. Neural network methods need fine datasets, which increase labor costs. Therefore, this paper proposes a segmentation algorithm based on UNet3+ network. After obtaining the UAV image, the rough location of the crack can be obtained by only rough labeling. And then, the sample balance can be carried out by clipping the target area. The UNet3+ network is used to train the processed datasets and extract the region of interest to ignore the non-target texture. Finally, the region of interest is further segmented by color clustering and edge detection methods. Results:: The proposed method can detect the cracks accurately. In all test images, the relative errors are less than 13%. Especially in test images whose width is less than 0.2mm, the maximum absolute error is only 0.0237mm, which is completely acceptable in actual production. The proposed method has higher practicability in the detection of concrete crack images taken by UAV. The results show that the proposed method outperforms the cutting-edge method published in the journal "Sensor", when the background is complex. Conclusion:: The proposed method can segment and detect cracks effectively, which can remove the high-frequency interference region from the images.

A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use

Gaze-tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but fully automatic systems require large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well-controlled, it is possible to make a much simpler system that uses the initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer’s task further, this test uses the Southampton Hand Assessment Procedure (SHAP) to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here concerns the development of a software tool capable of identifying and tracking the points of regard and areas of interest throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects and was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users.

Design of Fatigue Driving Behavior Detection Based on Circle Hough Transform.

Chronic fatigue symptoms of jobs are risk factors that may cause errors and lead to occupational accidents. For instance, occupational injuries and traffic accidents stem from overlooking long-term fatigue. According to statistics for fatigue driving, it was found that fatigue driving is one of the main causes of traffic accidents. The resulting decrease in the quality of traffic, as well as impaired traffic flow efficiency and functioning, contributes markedly to the societal costs of fatigue. This article proposes a noninvasive physical method for fatigue detection using a machine vision image algorithm. The main technology was implemented using a software framework based on optimized skin color segmentation and edge detection, as well as eye contour extraction. By integrating machine vision and an optimized Hove transform algorithm, our method mainly identifies fatigue based on the detected target's face, head gestures, mouth aspect ratio (MAR), and eye condition, and then triggers an alarm through an intelligent auxiliary device. Our evaluation results of facial image data analysis showed that with an ideal eye threshold of 0.3, PERCLOS-80 standard, MAR, and head gesture-nod frequency, the method can be used to detect fatigue data accurately and systematically, thereby fulfilling the purpose of alerting a group of high-risk drivers and preventing them from engaging in high-risk activities in an involuntary state.

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

The similarity search approach used for image Data Warehouse (DW) can provide better insights into discovering the most similar images compared to the input query. Due to the later innovation improvement, the mixed media complexity is discernibly expanded and modern inquires about regions are opened depending on comparable mixed media substance recovery. Content-Based Image Retrieval (CBIR) algorithms are utilized for the retrieval of images related to the inquiry image from gigantic databases or DW. The queries that are used for DW are complex, take a lot of time to process and many give less accurate results. For these reasons, this paper needs to have an effective technique to improve the similarity search query process that reflects a more positive result. In this paper, show how to extract features from a set of images (color, shape, and texture features) by using CBIR algorithm with Color Edge Detection (CED) method. Once these features are extracted, the proposed method will minimize the distance between these features vectors and the query image one using a Genetic Algorithm (GA). This paper illustrates the extraction of endless strong and imperative features from the database of the images, therefore, the capacity of these features in storing within the frame of features vectors. Accordingly, an imaginative closeness assessment with a metaheuristic algorithm (Genetic Algorithm (GA) with Simulating Annealing (SA)) has been attained between the query image features and those having a place in the database image. This paper introduces a new algorithm CEDF (Color Edge Detection with Gaussian Blur Filter) that applies the Gaussian Blur Filter after using CED method for feature detection of the image. Experimental results show that CEDF method gives better result than the other already-known methods.

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The second-order neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information quality and have an optimal image segmentation. Finally, the segmentation results of the proposed model are validated. Moreover, the classification accuracy of the tested data is assessed, and a comparison with other current models is conducted. The comparison results show that the proposed model outperforms the existing models in image segmentation.

Implementation of Bootstrap Technique in Detection of Road Sign using Machine Learning

Abstract: Traffic sign recognition is a driver assistance tool that can alert and warn the driver by showing any applicable limitations on the current stretch of road. Such limitations include signs such as 'traffic light approaching' or 'walking crossing.' The present research focuses on identifying Indian road and traffic signs in real time. Real-time footage from a moving automobile is captured by a computerized camera, and genuine traffic signs are retrieved using vision data. The network is divided into three stages: one for identification and the other for classification. The first stage created and constructed hybrid colour edge detection. In stage 2, a new and successful custom feature-based technique is used for the first time in a road sign identification strategy. Finally, a multilayer Convolution Neural Network (CNN) with Graphical User Interface (GUI) is being created to identify and analyse various traffic signals. It's tricky, despite the fact that it's been tested on both traditional and nontraffic signs and passed with flying colours..

New Aggregation Approaches with HSV to Color Edge Detection

The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F-measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others.

Experimental Study of Cavitation Damage to Marine Propellers Based on the Rotational Speed in the Coastal Waters

Cavitation in a propeller causes erosion, accelerating corrosion, and tearing off blades in severe cases. Despite the maintenance requirements caused by cavitation, few studies have investigated the cavitation resistance of small ship propellers. Therefore, in this study, these characteristics were investigated through a demonstration test in the coastal waters of South Korea. Cavitation erosion characteristics were analyzed according to the low- and high-rotational speed of the propeller, and the weight was measured every 10 h for 100 h of sailing. The erosion pits were qualitatively compared through liquid penetrant testing (PT) and quantitatively compared by image processing with color edge detection. The results showed that propeller weight loss at high speed was double that at low speed. The cavitation erosion exhibited one cycle each of incubation, acceleration, deceleration, and steady state at low speed, while the acceleration and deceleration periods repeated at high speed. According to PT and color edge detection, the concentration of pits in the low- and high-speed conditions increased towards the trailing and leading edges, respectively. Further, in the radial direction, the trend was similar in both conditions, and the largest number of pits were detected in the region of 0.7–0.9R, where R is the propeller radius.

Ultrasonic pseudo-color imaging in strongly attenuating materials with Filtered-Delay Multiply And Sum nonlinear beamforming

To detect flaws in strongly attenuating materials, we propose an ultrasonic phased array imaging method that combines Filtered-Delay Multiply And Sum (F-DMAS) nonlinear beamforming algorithm with pseudo-color technique. Based on the spatial coherence of signals, three filters with different frequency ranges are selected to reconstruct F-DMAS images from the full matrix capture (FMC) data. Then, three channels of F-DMAS images are combined with the RGB color model to obtain the pseudo-color image. Finally, an automatic flaw extraction algorithm is developed using the techniques of color image segmentation, data fusion, and edge detection. Experiments are performed on samples of polymer bonded explosives with side drilled holes. The results show that the proposed method has significant advantages over the linear beamformed Total Focusing Method (TFM) in terms of signal-to-noise ratio, detection ability, and visual presentation of flaws.

A Robust Color Edge Detection Algorithm Based on the Quaternion Hardy Filter

This paper presents a robust filter called the quaternion Hardy filter (QHF) for color image edge detection. The QHF can be capable of color edge feature enhancement and noise resistance. QHF can be used flexibly by selecting suitable parameters to handle different levels of noise. In particular, the quaternion analytic signal, which is an effective tool in color image processing, can also be produced by quaternion Hardy filtering with specific parameters. Based on the QHF and the improved Di Zenzo gradient operator, a novel color edge detection algorithm is proposed; importantly, it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique. From the experimental results, we conclude that the minimum PSNR improvement rate is 2.3% and the minimum SSIM improvement rate is 30.2% on the CSEE database. The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

Barcode Location in Financial Statement System Based on the Partial Differential Equation Image Recognition Algorithm

Financial statements are the basis of financial analysis. Most of them are still using traditional financial statement software, and financial data cannot achieve better information sharing. Using a barcode to improve the efficiency of data sharing is undoubtedly the most convenient and fast way to realize financial statement information sharing. This paper studies the problem of the barcode image location and recognition in the financial statement system and tries to apply the partial differential equation image recognition algorithm to the barcode location in the financial statement system. In this paper, image technology is used to preprocess the code 39 barcode label image in the captured image, including color space conversion, image enhancement, threshold segmentation, binarization, and edge detection. The barcode position location is realized by using the method based on integral projection peak analysis and Hough transform line detection. It is proved that the positioning function of the barcode makes the data in financial statements realize resource sharing. Then, the performance of the completed barcode positioning and recognition algorithm is tested. The reliability and effectiveness of the algorithm are verified on the manually made test set.

APLICAÇÃO DE REDES NEURAIS CONVOLUCIONAIS NO RECONHECIMENTO DE CARACTERES EM PLACAS INFORMATIVAS JAPONESAS

Unlike most Western countries, which have a Latin-derived base alphabet, Japan has two syllabic alphabets called Hiragana and Katakana, and a Chinese alphabet, called Kanji. The vast differences in the writing of these Eastern alphabets to Western alphabets, Western alphabet-based OCR algorithms tend not to efficiently detect Japanese characters. This work contributes to a methodology applying digital image processing techniques, such as color range-based segmentation, edge detection and mathematical morphology techniques, to detect Japanese traffic informationalplates correctly the perspective and segment the characters contained in it. A convolutional neural network wasused to perform the classification of Hiragana characters contained in the segmented plates, withaccuracyof 94.37%.

Digital Color Image Processing Using Intuitionistic Fuzzy Hypergraphs

Hypergraphs are considered a useful mathematical tool for digital image processing and analysis since they can represent digital images as complex relationships between pixels or block of pixels. The notion of hypergraphs has been extended in fuzzy theory leading to the concept of fuzzy hypergraphs, then in intuitionistic fuzzy theory conducting to the concept of intuitionistic fuzzy hypergraphs or IFHG. The latter is very suitable to model digital images with uncertain or imprecise knowledge. This paper deals with color image denoising, segmentation, and edge detection in a color image initially represented in RGB space using intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edge detection.

Color edge detection by learning classification network with anisotropic directional derivative matrices

In this paper, a neural network-based color edge detector is constructed by learning a classifier using anisotropic directional derivative (ANDD) matrices of a color image as input. The training stage on a color edge dataset with ground truth (GT) edges includes calculation of ANDD matrices, generation of feature matrices, and training a classifier. For each training image, a set of ANDD matrices are calculated from the ANDDs with different parameter setups for training and from which a set of the color edge strength maps (CESMs) are extracted by the singular vector decomposition. The CESMs and the GTs on edges of the image are combined into a feature matrix for training. Using the feature matrices of all the training images as input, a classification neural network is trained and it outputs the probability of a pixel to be an edge pixel. In the detection stage, for a color image, its ANDD matrices, CESMs, and the color edge direction maps (CEDMs) are first computed and then the CESMs are input into the classification neural network to obtain the edge probability map (EPM) of the image. Finally, the non-maximum suppression and hysteresis thresholding are applied to the EPM and CEDMs to generate the binary edge map. The proposed detector attains better performance than the existing gradient-based detectors and is competitive with learning-based detectors on three commonly-used color image datasets for edge and contour detection.

Color Image Processing Under Uncertainty

Most digital images have uncertainties associated with the intensity levels of pixels and/or edges. These uncertainties can be traced back to the acquisition chain, to uneven lighting conditions used during imaging or to the noisy environment. On the other hand, intuitionistic fuzzy hypergraphs are considered a useful mathematical tool for digital image processing since they can represent digital images as complex relationships between pixels and model uncertain or imprecise knowledge explicitly. This paper presents the approach for noisy color image segmentation and edge detection based on intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to the HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edges detection. The first experimentations showed that the proposed approach gave good results especially in the case of dynamic threshold.

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

Color Edge Detection Based on the Fusion of Intensity and Chromatic Differences

Edge detection is the name for a set of mathematical methods which target at classifying points in an image at which the image intensity varies sharply or, has discontinuities. The paper tries to find the solution for detecting color edges based on color and intensity information of two new images H-image and T-image crafted on color space transformation, that will produce two-resulted edges derivates of H-image and T-image and are at last coalesced to obtain final edge.

Optimization of Modified Hidden Markov Model for Vision-Based Indonesian Sign Languages Recognition

Sign language is one of the most popular language which is used as a communication bridge that depends on hand movement. As it is used worldwide, the variety of sign languages is very large and most of the people doesn’t understand it which makes the communication between deaf and normal people interrupted. This makes sign language recognition popular as it makes people doesn’t need to understand the sign but still understand the meaning of the sign. But sign language itself has many problems such as the possibility of different dataset has the same movement but different meaning, the method used of each dataset could be ineffective in other dataset, and many other else which makes it difficult to be implemented. Other than that, vision-based recognition is not as popular as sensor-based recognition because of the difference in feature accuracy even though it could give more area of improvement. That’s why the aim of this paper is to presents the combination of methods used to recognize vision-based Indonesian Sign Language and enhance the method using optimization technique. The methodology used in this study follows four steps framework of sign language recognition which is dataset collection, preprocessing, feature extraction, and recognition. Each method is improved in order to be compared and checked what is the method combination and optimization method is the best for sign language recognition. For dataset collection, the dataset that is used is formal Indonesian Sign Language which is called Sistem Isyarat Bahasa Indonesia (SIBI) with some constraint in order to make sure the quality of the dataset is good. The preprocessing methods are differentiated into two categories which is image enhancement and hand detection. Image enhancement methods include no image enhancement, Gaussian blur, Bilateral blur, and Contrast Limited Adaptive Histogram Equalization and hand detection methods include skin detection using YCbCr color space and edge detection using Canny algorithm. Feature that is used in this study is pair of left and right-side movement for each frame that is extracted by calculating the average value of each pixels from left and right-side image that could be a representative value of each frame. Lastly the recognition system that is used is Gaussian Hidden Markov Model and its’ state optimization which includes no optimization, Latin Hypercube optimization, Hill Climbing optimization, and Combination of Latin Hypercube and Hill Climbing optimization. The experiment result of the proposed method could recognize up to 82% accuracy rate. The improvement of this research could be implemented on educational studies or game development that need vision-based sign language recognition system.

Automatic evaluation of crown preparation using image processing techniques: A substitute to faculty scoring in dental education

Background:This study presents a new and innovative experimental method, including software and its prerequisite instruments, to use image processing techniques for crown preparation analysis.Method:A platform was designed and constructed to take images from artificial teeth in different angles and directions and to process and analyze them by the proposed method to evaluate the quality and quantity of crown preparation. For each tooth, two series of images were taken from the artificial teeth before and after preparation, and image series were registered by two semi-automated and automated methods to transform them into one coordinate system. Region of interest was segmented by user interaction, and tooth region was segmented by substeps such as transformation to hue, saturation, and value color space, edge detection, morphology operations, and contour extraction. Finally, the amount and angle of crown preparation were computed and compared with standard measures to evaluate the quality of crown preparation. The proposed method was applied to a local dataset collected from Isfahan University of Medical Sciences.Results:Difference between the angle of crown preparation computed by the proposed method and that of the experts showed a mean absolute error of 7.17°. The correlation between the segmented regions by the proposed method and those of the experts was also evaluated by the Intersection over Union (IOU) criterion. The best and worst performances achieved in cases by IOU were 0.94 and 0.76, respectively. Finally, the segmentation results of the proposed method indicated an average IOU of 0.89 in all images.Conclusion:Students can use this method as an assessment tool in preclinical tooth preparation to compare their crown work with standard parameters.