Color Detection Research Articles

Evaluation of Visual Acuity and Perceptual Field of View Using the Varjo XR-3 Headset in a Virtual Environment

Understanding the capabilities and limitations of virtual reality head-mounted displays are key to using virtual reality devices appropriately to display complex 3D information. This study sought to replicate and compare tests characterizing visual acuity and perception using the Oculus Rift and HTC Vive Pro head-mounted displays against the Varjo XR-3 headset. 14 participants tested the Varjo XR-3 in a series of visual and perceptual tasks. Performance in the central field of view was assessed using the Snellen and Landolt tests of visual acuity as well as contrast sensitivity circles. Visual performance in the peripheral field was measured by evaluating symbol and color detection at increasing levels of eccentricity. The Varjo XR-3 provides significantly better visual acuity compared to previously tested headsets. Symbol recognition in the peripheral region is similar when viewed through the Varjo XR-3 compared to in the real world and head movements play a significant role in increasing perceptual field of view.

Plate Number Recognition based on Hybrid Techniques

Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.

How do computers see landscapes? comparisons of eye-level greenery assessments between computer and human perceptions

Landscape architects and planners have been assessing eye-level vegetation to develop evidence-based designs, including the relationships between urban nature and human health. Measuring eye-level vegetation was often subjective and time-consuming in the past. Recent advances in computer vision have made it feasible to automatically measure eye-level greenery at a large scale. However, researchers still know little about the agreements of recent machine-based methods with human perception. The research gap may lead to inaccurate or even misleading findings that may prevent effective design and planning.This study tested the agreements between eye-level greenery detected by two machine-based methods (Brown Dog Green Index Extractor (BDGI) and PSP-Net) and human perception (manual selection via Photoshop Histogram). These two machine-based tools were selected because of their distinctive mechanisms: color detection and semantic segmentation. Cronbach’s alpha, correlation test, and Bland-Altman’s Plots were used to test agreements. Then, logistic regressions were used to find relationships between shades and vegetation density and the disagreement odds. Both tools closely agreed with human assessment in predicting eye-level greenery, with BDGI slightly closer to human. Vegetation density, but not percentage of shade, predicted the higher disagreement odds between PSP-Net and others. This finding will help advancing computer-based assessment of urban nature and contribute to our knowledge in assessing and linking eye-level greenery with potential outcomes such as physical and mental health and other design assessments.

Colour Detection of an Object in an Assembly Line

Nowadays, vision systems are used in many things to run the industries error free with great accuracy, and for that, image processing is one of the keys that is used in this paper. This paper focuses on product differentiation using the colour detection method in real time with the help of MATLAB software. First task is to take snapshot from the web cam and convert that RGB image into HSV image, in this HSV image plays a vital role in colour description as it makes easier, they need luminous of the image. Threshold value of HSV used for differentiating colour, after applying the threshold values some of the corrections are done for avoiding any kind of error like erosion, dilution and holes.

Study on A face detection method based on elliptic skin color model

Human face is a natural structural target with abundant details, and its detection results are easily affected by facial details, expressions and posture changes. In color images, the distribution of skin color is not affected by the changes of facial details, expression and posture, and the speed of skin color detection is very fast. This paper proposes a face detection algorithm based on elliptic skin color model. In YCbCr color space, the elliptic skin color model and logistic regression analysis were used to determine the skin color probability of each point, and the pixels of each point were mapped to [0, 1]. Based on Ostu method, a parallel genetic algorithm was used to determine the threshold of skin color segmentation to segment the face region. The results show that this method improves the speed of face detection and has good robustness to posture and expression changes.

Surface-enhanced Raman spectroscopy enables highly accurate identification of different brands, types and colors of hair dyes

ABSTRACT: Hair is present at nearly all crime scenes. Forensic analysis of hair can be used to establish a connection between a suspect and a crime scene or demonstrate the absence of such connection. Almost half of people around the world color their hair. However, there is no robust and reliable forensic approach that can be used for a confirmatory analysis of artificial colorants present on hair. A growing body of evidence suggests that surface-enhanced Raman spectroscopy (SERS), a modern analytical technique, can be used to detect and identify colorants present on hair. In the current work, we examined the potential of SERS in identification of more than 30 different colorants. We found that the accuracy of detection and identification of individual hair colorants is 97%, on average. We also investigated the extent to which SERS can be used to differentiate between different brands and types of colorants, as well as to identify hair color regardless of the type and brand of the colorant used to dye hair. Our results showed that individual colorants could be identified with on average 97% accuracy, whereas different brands can be predicted with nearly 100% accuracy. We also found that SERS offered nearly 100% accurate identification of the type of the colorant and on average 97.95% accurate prediction of the hair color. These results demonstrate that SERS can facilitate the forensic analysis of hair providing highly important information about the artificial colorants present on the analyzed specimens.

Real Time Skin Color Detection Based on Adaptive HSV Thresholding

The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.

"Comparison of Color Classification Using Computer Vision and Deep Neural Network"

Research on artificial intelligence and machine learning is currently ongoing and is focused on real-world problems. Machine learning is used by computers to make predictions based on the provided data set or existing knowledge. The main goal of our project is to use machine learning to categorize different colors while separating CNN from computer vision. In this work, we used supervised learning to categorize different hues using a binary classification approach. Color detection is the technique of identifying a color. In this scenario, humans can recognize the hue and choose with ease. A computer, however, cannot quickly recognize color. It is challenging to get a computer to quickly detect the color. Given that, we decide to pursue this initiative. Pandas, OpenCV, and the Naive Bayes algorithm are all used in Python. Naive Bayes classifiers are models that assign category labels to issue occurrences that are represented as vectors of feature values, where the category labels are selected from a finite set. There isn't a single method for training these classifiers; rather, there is a family of algorithms built on the premise that, given a category variable, the value of one feature is independent of the value of the other feature. Open-Source Computer Vision Library OpenCV was designed to be computationally effective and with a major emphasis on real-time applications. specialized video encoding for the cloud. Panda may be a platform that runs in the cloud and provides infrastructure for encoding audio and video.

FireFly: An Insect-Scale Aerial Robot Powered by Electroluminescent Soft Artificial Muscles

Light production in natural fireflies represents an effective and unique method for communication and mating. Inspired by bioluminescence, we develop a 650 mg aerial robot powered by four electroluminescent (EL) dielectric elastomer actuators (DEAs) that have distinct colors and patterns. To enable simultaneous actuation and light emission, we embed EL particles in a DEA that has highly transparent electrodes. During robot flight, a strong ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 40 V/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m) and high frequency (400 Hz) electric field is generated within the DEA, exciting the EL particles to emit light. Compared to a regular DEA, our new design and fabrication methods require small additional weight (2.4%) and actuation power (3.2%) without adversely impacting the DEA’s output power or lifetime. We further develop a position and attitude tracking method using vision-based color detection. We demonstrate a series of closed-loop flights and compare camera-tracked results with that of the state-of-the-art Vicon motion tracking system. The root-mean-square (rms) position and attitude errors are 2.55 mm and 2.60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> , respectively. This work illustrates a novel and effective design for communication and motion tracking in extreme payload-constrained microscale aerial systems, and it further shows the potential of achieving coordinated swarm flights without using well-calibrated in door tracking systems.

Clinical justification of the effectiveness of the use of digital devices for determining the color of the tooth

BACKGROUND: The procedure for determining the color of teeth can be carried out visually using colors or special devices, or both methods at the same time. The visual definition of shades using popular instrumental techniques, although ubiquitous, is nevertheless very subjective. The hardware method for determining the color of teeth consists in an objective computer analysis of the image obtained under ideal optical conditions. Ambient light should not affect the color detection results obtained by spectrophotometers and colorimeters. The main advantage of the hardware method of determining the color of teeth is the elimination of subjectivity. The world scientific literature describes many studies comparing the quality of color determination by hardware and visual methods. However, these data differ, and the conclusions are not unambiguous in the choice of a particular method, therefore, the clinical effectiveness of digital technologies for determining the color of teeth, as well as the scientific justification of their use, is an urgent problem of modern dentistry.&#x0D; AIM: To evaluate the accuracy of determining the color of teeth with digital dental devices in comparison with the visual assessment of color by an expert group.&#x0D; MATERIAL AND METHODS: In order to achieve this goal and form the design of the study, we analyzed the sources of scientific literature. The information search was carried out in the scientific electronic databases eLibrary, Scopus, PubMed in the period from 2002 to 2020, according to the keywords of the query: methods for determining the color of teeth, comparison of methods for determining the color of teeth, hardware methods for determining the color of teeth. The results of the study of special literature on this topic have identified the most accurate and frequently used methods for determining color in practical healthcare. Of the visual methods, preference is given to determining the color of teeth using the colors VITA Toothguide 3D-Master (VITA Zahnfabrik, Germany) and VITA Classical (VITA Zahnfabrik, Germany). Of the devices, among spectrophatometers, most authors prefer VITA Easyshade (VITA Zahnfabrik, Germany) and Spectro Shade (Micro High Technologies, Italy), among hybrid devices (intraoral scanners) 3Shape TRIOS (3Shape, Denmark). The evaluation of the accuracy of determining the color of teeth by digital dental devices was carried out on 20 natural right medial central incisors of 20 patients in a dental office. The effectiveness of these devices was determined in comparison with the teeth color indicators determined by the expert group. For each pair of the studied methods, Cohens kappa was determined a statistical coefficient for determining the mutual reliability of appraisers. When calculating Cohens mouthguard, the results of color determination for each third of the tooth surface were compared separately by compiling multi-field tables. Statistical processing was carried out in Excel 2016.&#x0D; RESULTS: According to the interpretation intervals of Cohens kappa values, mutual reliability of evaluators is defined as excellent at values from 0.75 to 1.0, good from 0.6 to 0.74, acceptable and bad from 0.4 to 0.59 and less than 0.4, respectively. The Spectra Shade device showed excellent consistency with experts in determining the color of the tooth in all three parts of the surface. Vita Easyshade V showed less consistency with the expert assessment, but its level is interpreted as good. The color detection data using the 3Shape Trios digital scanner did not agree well with the opinion of experts. The devices showed consistency between themselves in the range from acceptable to good, while mutual reliability in the Spectra Shade - Vita Easyshade V pair turned out to be better than others.&#x0D; CONCLUSION: Taking into account the fact that the accuracy of the devices does not depend on the factors to which a person is exposed, such as subjectivity, illumination, time of day, eye fatigue, the color of nearby teeth, etc., it is advisable to use the Spectra Shade and Vita Easyshade V devices in clinical practice to increase the objectivity of color determination and improve patient satisfaction with dental treatment.

IOT-based Automated Object Colour Sorting and Counting machine Using colour Sensor, Node MCU and Servo Motor

In the current period, each and every process in different sections of diligence similar as manufacturing, processing, packaging sections, etc. are being revolutionized with automated systems, due to certain downsides in the homemade system. The end of the system is to reduce the downsides of the homemade system similar as low delicacy, low thickness, high charges, etc. The proposed system feathers and counts objects grounded on their colors and the collected data is transferred to the pall. In this system Node MCU is being used as control unit and transmits the entire process.TCS3200 color detector is used to sort out the objects and are being directed with the help of servo motor.

Soybean seed scanning for size, genotype color and Cercospora blight detection

Soybean seed scanning for size, genotype color and Cercospora blight detection

Video Processing from a Virtual Unmanned Aerial Vehicle: Comparing Two Approaches to Using OpenCV in Unity

Virtual reality (VR) simulators enable the evaluation of engineering systems and robotic solutions in safe and realistic environments. To do so, VR simulators must run algorithms in real time to accurately recreate the expected behaviour of real-life processes. This work was aimed at determining a suitable configuration for processing images taken from a virtual unmanned aerial vehicle developed in Unity using OpenCV. To this end, it was focused on comparing two approaches to integrate video processing in order to avoid potential pitfalls such as delays and bottlenecks. The first approach used a dynamic link library (DLL) programmed in C++, and the second an external module programmed in Python. The native DLL ran internally on the same Unity thread, as opposed to the Python module that ran in parallel to the main process and communicated with Unity through the Message Queue Telemetry Transport (MQTT) protocol. Pre-transmission processing, data transmission and video processing were evaluated for a pair of typical image-processing tasks like colour and face detection. The analysis confirmed that running the Python module in parallel does not overload the main Unity thread and achieves better performance than the C++ plugin in real-time simulation.

Fluorimetric quantification of picric acid in aqueous medium via smartphone and invisible ink applications using pyrene based sensor

• Simple imidazole-pyrene based sensor for quantification of picric acid in aqueous medium. • The fluorescent probe became non-fluorescent in presence of picric acid. • Addition of picric acid caused bare-eye color change from colorless to yellow. • Colorimetric detection of picric acid using smartphone ‘Color detector App’. • Detection of picric acid by the means of invisible ink applications. A simple imidazole-pyrene based sensor (1) has been developed by simple one step reaction and characterized by using common spectroscopic techniques. When various nitroaromatic compounds were incorporated to CH 3 CN:H 2 O (1:2, v/v) solution of 1 , the fluorescent probe turned non-fluorescent in presence of picric acid, upon irradiation at 310 nm. This was accompanied by negligible changes with other molecules along with fluorescent color change from bluish-green to colorless in case of picric acid. Also, the fluorimetric sensing of picric acid was done using smartphone and invisible ink applications. The sensor has been found efficient for the recognition of picric acid in aqueous medium.

Design and Evaluation of Filterless RGB Sensor on Standard CMOS Process

In this study, a color detection phenomenon that does not utilize color filters is evaluated via the standard CMOS process. The device comprises multiple pn-junctions stacked in the depth direction, which enable division of the light responses by different light absorptions with respect to wavelength. To eliminate the color filters, we use rectifiers to cut the reverse currents corresponding to the long-wavelength infrared (Ir) regions. Numerical simulations predict the dynamic range of the sensor as 160 dB after removal of the filters that can decrease this value. Applications based on the Internet of Things can be realized via mass production at low cost by eliminating the additional processes required to form the conventional color filters in front of the sensor. We evaluate the proposed device by both numerical simulation and experiment as well as clarify that the sensing phenomenon requires sufficient PWell thickness to separate the depletion regions for successful operation. The appropriate process parameters for the CMOS process are revealed, providing useful information for testing under a wide range of process conditions.

The Use of Image Processing and Sensor in Tomato Sorting Machine by Color, Size, and Weight

Tomatoes are a popular vegetable in Indonesia, where production increases every year according to market demand. The large production requires proper post-harvest handling both in quality and time. It has been well-known that sorting and grading are the first and foremost processes in the post-harvest process of tomatoes. Sorting tomatoes can be conducted by color and adjusted to the target market. The automation process in the post-sorting and grading process can save time and resources. This research proposes a sorting system that sorts tomatoes based on color, size, and weight. Tomatoes are sorted by red, yellow, and green colors. The detection of color and size was carried out by image processing with the OpenCV library. The color detection was carried out by HSV's red, yellow, and green values. In comparison, the dimensional measurement was carried out by determining the outermost point of the detected object both vertically and horizontally. At the same time, tomatoes' weight was measured by a weight sensor. This system was implemented into a prototype sorting system with a webcam, Arduino, a conveyor, and motors. The final part was a storage box used to accommodate tomatoes based on grading. The implementation has the maximum results for detecting color with 100% accuracy and measuring weight with 95% accuracy. However, it still needs development for dimensional measurements. In this research, it has only 5% accuracy. This proposed system can be used both in software and hardware design as an inline tomato sorting tool.

Fire Sense

Abstract: The significance of putting out a fire as soon as possible can serve to warn others about the perils of disaster and death. Common sensor fire detection systems are limited, especially in a vast location, requiring a long time to detect a fire, and are unable to work in an open environment. This study developed a fire sense approach based on visual sensitivity and colourful variables such as colour, earth filling, light, backdrop, and fire detection time to assess the likelihood of a fire. The degree of error of fire detection was calculated in this study’s test. Keywords: Fire Detection, HSV, Time Frame Selection, Alert System, Fire Alarm

Pick and Place Cabot's' Arms for Color Detection

Pick and Place Cabot's' Arms for Color Detection

Pick and Place Cabot's' Arms for Color Detection

Pick and Place Cabot's' Arms for Color Detection

Facial Skin Color Segmentation Using Otsu Thresholding Algorithm

The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.