Object Color Research Articles

Pieces Separation Applied to a RV-2AJ Industrial Manipulator

The objective of this work is to create a system for separating types of pieces for didactic purposes. An artificial vision system applied to a manipulator was used to perform this task of classifying and separating the pieces, through the use of digital image processing. The vision system uses a camera attached to the robot's end-effector, whose objective is to identify the area, perimeter and position of the centroid of the pieces to be separated. The separation of objects is performed between two classes: with round and non-round shapes. The tests were carried out with objects such as screws and nuts. After obtaining the positions of the objects to be separated by the vision system, some characteristics of these related to the reference system of the robot base are determined. Some difficulties were observed in the recognition of objects related to the colours and lighting conditions of the separation environment. To reduce these problems, black colour objects were used in order to reduce the reflectance. Another problem encountered in the separation process is related to the relative position of objects. In this case, two or more objects can be identified as a single object, depending on their relative positions. The system classifies with a rate of hits greater than 95%.

Syntax matters in shaping sensorimotor activation driven by nouns.

Existing evidence has shown that adjectives modulate the grasp-compatibility effect elicited by object nouns. The aim of the present study was to investigate the role of syntax on the sensorimotor activation elicited by nouns in a grasp-compatibility task. We assessed two languages with different syntactic rules, Italian in Experiment 1 and English in Experiment 2. In both experiments, an adjective-noun pair was shown on the screen. The adjective was always in a pre-nominal position and denoted either a disadvantageous quality of the object graspability (e.g., sharp) or the object colour (e.g., reddish). Participants had to categorize the object nouns as natural or artifact, performing a precision or a power reach-to-grasp movement. On different trials, the grasp response was compatible or incompatible with the grip typically used to manipulate the object indicated by the noun. In Experiment 1 (Italian language) the adjective-noun order violated the syntactic order and no difference emerged between reaction times on compatible and incompatible trials (no grasp compatibility effect). In Experiment 2(English language), the adjective-noun order followed the syntactic rule. Results showed a grasp-compatibility effect when a colour adjective was presented before a natural object noun. When a disadvantageous adjective preceded an artifact or a natural object noun, an inverted grasp-compatibility effect emerged with slower responses on compatible than incompatible trials. Taken together, these findings suggest that adjectives can shape the sensorimotor activation elicited by nouns of graspable objects only when the syntax is correct. Results are discussed with respect to embodied cognition theories.

Automated Object Sorting System with Real-Time Image Processing and Robotic Gripper Mechanism Control

This work represents an industrial sorting system where image processing is accompanied using a pick-and-place robotic gripper. The sorting of objects is done based on their shape and color. Here the color and shape of different objects are identified using image processing. For this, a webcam is used to capture images of the object in real-time and then process them via a digital computer. Python programming language is used for image processing in this work. After successfully identifying the color and shape of an object, the object is picked and placed at the desired position using the robotic gripper. Controlling the gripper mechanism is also executed using the Python programming language. It is controlled using the Arduino Uno microcontroller and a few DC servo motors. The gripper can move from 0° to 180°. The objects are brought in front of the camera using a belt conveyor system. After the complete fabrication and assembly, 4 objects of different shapes and colors are used to sort objects at 4 different angles. The objects are picked from 90° and is sorted in either 0°, 45°, 135°, or 180° position. This research work not only gives information about robotics but also can help industries sort complex objects automatically without any human interaction.

Design and Fabrication of Automatic Weight, Color and Height Based Sorting System

Sorting is a necessary task that is frequently performed in daily life. Manual sorting is an expensive, difficult, and drawn-out operation. Additionally, it slows down manufacturing operations. To solve these issues the sorting system now uses Low-Cost Automation which aims to decrease labor costs, manufacturing times, and processing complexity while also enhancing product quality, boosting output, etc. In this research, an efficient technique for automatically sorting the object based on color, height, and weight has been devised. The objects are recognized, separated, and collected using a collection bin along with a conveyor belt, strain gauge load cell, TCS34725 RGB color sensor, ultrasonic sensor, extra channel path, etc. according to their colors. The Arduino Uno controls the entire operation of this system. Here, three types of products are sorted based on their weight, color, and height. These three colors are red, blue, and green. Firstly, the weight is measured using strain gauge load cells. Then the color of the product has been detected by the color sensor at the same stage, and according to the color collecting bin rotates to the desired angle. Finally, the height is measured by an ultrasonic sensor. An extra channel is used at the end of the conveyor belt. At the end of the channel, a three-portion collecting bin is kept. The bin can be rotated at the desired angle by a servo motor after detecting the specified color object, and objects are being collected from this bin. Thus, the objects are sorted out based on their weight, color, and height. The experimental results show that the system fits the design requirements, is dependable, and has high sorting accuracy.

AUTOMATED DELIVERY ROBOT SYSTEM DESIGN FOR OFFICE USING ESP32 TECHNOLOGY

The emergence of Industry 4.0 has brought about major changes in various industries, including the use of Internet of Things (IoT) technology. One of the technologies of Industry 4.0 is IoT (Internet of Things), which allows the connection between tools and other devices. The use of smart robots as a solution for task completion in office areas is increasing rapidly. This smart robot can be equipped with sensor modules and ESP32 microcontroller processing modules to enhance its functionality. In addition, integration with smartphones through Android applications and websites allows this robot to be controlled and monitored remotely (online). The design of this IoT-based delivery robot with the integration of the ESP32 microcontroller and advanced sensors shows promising potential in improving convenience in offices. The use of TCS34725, infrared sensor, and HC-SR04 Ultrasonic Sensor enhances color detection and object recognition capabilities, enabling the execution of tasks as a delivery robot in an office environment. Integration of Android app and website as online control and monitoring function of the robot with real time

The Prevalence of Congenital Color Vision Abnormality Among Patients Attending a Tertiary Eye Care Center in Southern India.

Background and objective Color vision abnormality is a condition where the faculty to identify one or more primary colors is either defective (anomalous) or absent (anopia). Occupations like armed forces, merchant navy, navigation, and police and fire services require normal color vision. There is a scarcity of data in the literature regarding the prevalence of congenital color vision abnormality in patients in South India. In light of this, the present study aimed to find the prevalence of congenital color vision abnormality in patients attending the Outpatient Department (OPD) at a tertiary eye care center in South India. Materials and methods This was a descriptive cross-sectional study conducted at a tertiary eye care center in South India from December 2014 to December 2016. Patients with a best-corrected visual acuity of 20/20, normal direct and consensual pupillary light reflex, normal anterior segment, and undilated fundus examination were included. Color vision was assessed using Ishihara pseudoisochromatic plates, American Optical Hardy-Rand-Rittler (AO HRR), and then the Farnsworth Munsell D15 arrangement test. All the results were tabulated and statistically analyzed. Statistical significance was calculated using the ANOVA test. Results A total of 371 patients were screened for color vision abnormality; 184 (49.59%) patients were males and 187 (50.40%)were females. Out of 371 patients in the study, 363(98.10%) had normal color vision while eight patients (2.16%) had color vision abnormality. Of the eight patients with color vision abnormalities, six (75%) had abnormal color vision on all three tests and two(25%) had an abnormality on only two tests (Ishihara and AO HRR). Out of 184 males in the study, eight patients (4.34%) had abnormal color vision while none of the 187 females had color vision abnormality; this difference was statistically significant (p=0.03). Conclusions Early diagnosis and providing education and awareness of this condition are necessary as part of guiding young people with regard to their career options and we recommend that all children should undergo color vision screening before entering high school.

Object Tracking Based on Camera Using Anfis and Fuzzy Classifier for RGB Color

Image processing technology has a wide range of applications, such as in the medical, military, surveillance, and robotics industries. Analyzing objects in images is crucial when it comes to image processing. This study focuses on image processing to track objects of red, green, and blue (RGB) colors through the utilization of a camera. There are two research schemes: image processing and data classification. The data classification method used is the fuzzy and adaptive neuro-fuzzy inference system (ANFIS). The methods of image subtracting and region properties are commonly utilized for image processing. Based on the classification data results, the fuzzy logic classification demonstrated a higher accuracy rate of 86% when compared to Anfis' 65%. This was observed when both classification models were tested using a random sample. The value of Anfis is small due to the limited size of the training data used. As a result, it is recommended to use a fuzzy classifier for object color tracking for good performance.

Study on the Mechanism of the Pink Tooth Phenomenon Using Bovine Teeth: A Pilot Study.

The pink teeth phenomenon has occasionally been observed in forensic autopsies. This study aimed to establish an experimental pink tooth model and an objective color tone evaluation method in order to clarify changes in the color tone of teeth and the relationship with hemoglobin monoxide and its decomposition products and with red pigment-producing bacteria, under various external environmental factors. It was confirmed that the color tone evaluation with ΔE and the L*C*h color space was useful. The results of various examinations using this model showed that color development was suppressed under aerobic conditions, faded early under light, became bright red under a low temperature and showed a tendency to be reddish at 3 days under high humidity and in the presence of soft tissue. The biochemical analysis revealed a significant increase in carboxyhemoglobin at 7 days and a tendency toward increasing the total heme pigment and bilirubin levels over time. The bacteriological analysis revealed that red pigment-producing bacteria increased over time but that the color faded after 7 days. These results suggest that putrefaction greatly affects the pink teeth phenomenon, whereas red pigment-producing bacteria have little effect on the occurrence of pink teeth. However, further studies are needed to clarify bacteriological involvement.

Dyslexic Aba: a game to help children with dyslexia

This article presents a game to help professionals in the field of Education and Psychology using the Applied Behavior Analysis methodology in teaching literacy to children with dyslexia. The game is aimed at dyslexic children, aiming to improve learning and the achievement of reading and writing, aiming to challenge the user to form scrambled words with inverted, similar or facing down letters according to a picture shown, such as the word "car”, which for the dyslexic will show “artrph”. together with some inverted or rotated "r", the word size increases as the user changes levels, the game uses a "drag and drop" style, in which the user drags the letter from the shuffled frame and places it in the proper position to form the word. The methodology does not adopt negative stimuli "Game Over" and in parallel works with positive stimuli using sound and visual effects and obtaining points. The application has a functional logical structure that is easy to use, contextualizing different environments of a house, with personalized design, standardized, animated and colorful objects, uniting gameplay, education, teaching and positive stimuli in the literacy process of dyslexic people.

A Novel Approach to Quantitative Characterization and Visualization of Color Fading

Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time has passed. The current color of objects of interest can only be compared with old photographs or the observer’s perception at the time of reference. Color fading and color darkening rates between two or more points in time in the past can only be determined using photographic images from the past. For objective characterization of color difference between two or more different times, quantification of color in either digital or printed photographs is required. A newly developed image analysis and comparison software (PicMan) has been used for color quantification and pixel-by-pixel color difference mapping in this study. Images of two copies of Japanese wood-block prints with and without color fading have been selected for the exemplary study of quantitative characterization of color fading and color darkening. The fading occurred during a long period of exposure to light. Pixel-by-pixel, line-by-line, and area-by-area comparisons of color fading and darkening between two images were very effective in quantifying color change and visualization of the phenomena. RGB, HSV, CIE L*a*b* values between images and their differences of a single pixel to areas of interest in any shape can be quantified. Color fading and darkening analysis results were presented in numerical, graphical, and image formats for completeness. All formats have their own advantages and disadvantages over the other formats in terms of data size, complexity, readability, and communication among parties of interest. This paper demonstrates various display options for color analysis, a summary of color fading, or color difference among images of interest for practical artistic, cultural heritage conservation, and museum applications. Color simulation for various moments in time was proposed and demonstrated by interpolation or extrapolation of color change between images, with and without color fading, using PicMan. The degree of color fading and color darkening over the various moments in time (past and future) can be simulated and visualized for decision-making in public display, storage, and restoration planning.

Cooling colors below ambient temperature

The colors of objects originate from reflection of light in certain directions and absorption of undesired light, producing substantial heating. Extensive efforts are expended to cool colorful objects to reduce their energy consumption. However, a strategy to cool colorful objects below ambient temperature while fully preserving their excellent color properties with high saturation and large viewing fields remains a significant challenge. Inspired by Morpho butterflies, we report a robust configuration consisting of a multilayer, disordered structure, and total reflection layer to cool colorful objects to overcome this challenge. Numerical simulations and experimental measurements demonstrated that our configuration can cool a class of colorful objects not only to a temperature of approximately 2°C below ambient temperature, but also with ultrahigh saturation (100%) and a wide range of viewing angles (±60∘). These colorful cooling films have significant potential for energy sustainability in buildings, vehicles, facilities, and equipment.

Short Distance Detection and Control System Based on Photoelectric Sensor and Its Application in Target Detection of Handling Robot

The photoelectric sensor is an electronic device that is widely used in industrial automation processes. The device uses light to detect whether an object exists. The photoelectric sensor detection control system is the core part of the photoelectric sensor. The system includes sensor processing chip, control circuit, detection optical structure and automatic teaching control algorithm. In this research, the core control chip of the automatic teaching system and the core part of the control circuit of the short-distance photoelectric sensor are set up. The modular circuit around the above chip is designed. The optical structure of the sensor is designed by Position sensitive sensor (PSD) photoelectric accessories and triangulation method. Meanwhile, the core circuit control chip (CSP) is applied to complete the basic functions of the short-distance photoelectric sensor. Among them, the optical structure is designed to collect and process the optical signal of the detected object. Based on this, the I/V conversion is needed after collecting the optical signal by PSD, and then the signal processing is carried out. The fuzzy PID control model is used to solve the problem that the control object cannot accurately establish the mathematical model. The designed photoelectric sensor is used for the handling robot, and the D-H mathematical model of the handling robot is established. The Robotics toolbox provided by MatLab is used to simulate the movement of the robot, and the sensor is used to detect the target of the robot grabbing. The experimental results show that the sensor uploads the coordinate information of the object to be grabbed to the software interface, and the handling robot can accurately grab the object with the help of the photoelectric sensor, and place it in the corresponding position according to the appearance color of different objects to be grabbed.

Rapid single-photon color imaging of moving objects.

This paper outlines an experimental demonstration of a Bayesian image reconstruction approach to achieve rapid single-photon color imaging of moving objects. The capacity to extract the color of objects is important in a variety of target identification and computer vision applications. Nonetheless, it remains challenging to achieve high-speed color imaging of moving objects in low-photon flux environments. The low-photon regime presents particular challenges for efficient spectral separation and identification, while unsupervised image reconstruction algorithms are often slow and computationally expensive. In this paper, we address both of these difficulties using a combination of hardware and computational solutions. We demonstrate color imaging using a Single-Photon Avalanche Diode (SPAD) detector array for rapid, low-light-level data acquisition, with an integrated color filter array (CFA) for efficient spectral unmixing. High-speed image reconstruction is achieved using a bespoke Bayesian algorithm to produce high-fidelity color videos. The analysis is conducted first on simulated data allowing different pixel formats and photon flux scenarios to be investigated. Experiments are then performed using a plasmonic metasurface-based CFA, integrated with a 64 × 64 pixel format SPAD array. Passive imaging is conducted using white-light illumination of multi-colored, moving targets. Intensity information is recorded in a series of 2D photon-counting SPAD frames, from which accurate color information is extracted using the fast Bayesian method introduced herein. The per-frame reconstruction rate proves to be hundreds of times faster than the previous computational method. Furthermore, this approach yields additional information in the form of uncertainty measures, which can be used to assist with imaging system optimization and decision-making in real-world applications. The techniques demonstrated point the way towards rapid video-rate single-photon color imaging. The developed Bayesian algorithm, along with more advanced SPAD technology and utilization of time-correlated single-photon counting (TCSPC) will permit live 3D, color videography in extremely low-photon flux environments.

Underwater Image Color Constancy Calculation with Optimized Deep Extreme Learning Machine Based on Improved Arithmetic Optimization Algorithm

To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models.

Trichromatic vision in toads: evidence from preference for colour objects during mate choice

Abstract Input of different spectral types of photoreceptors in amphibian colour vision has been assessed by studying the behaviour of male toads Bufo bufo and B. gargarizans in a laboratory. This method is based on the males’ innate reaction of approaching and clasping any visual object during the breeding season. A pair of colour stimuli made of paper or displayed on a LCD monitor has been selected to match the toad’s RGB colour space so that they differ by excitation of only a single spectral class of retinal photoreceptors. With pairwise presentation, the males are found to prefer a more ‘bluish’, or a less ‘reddish’, or a less ‘greenish’ stimulus from each pair. A microspectrophotometry of isolated rod and cone photoreceptors and an observation of behavioural responses in toads have revealed a trichromatic mesopic visual system based on a positive input from blue-sensitive photoreceptors and negative inputs from red-sensitive cones and green-sensitive rods. It is supposed that the observed colour preference and involvement of different chromatic mechanisms determine social interactions of anurans during mate choice.

Visual Coherence for Augmented Reality

Introduction. The 2020s were marked by the emergence of a new generation of computer simulators using augmented reality. One of the promising advantages of augmented reality technology is the ability to safely simulate hazardous situations real-world. A prerequisite for realizing this advantage is to provide the visual coherence of augmented reality scenes: virtual objects must be indistinguishable from real ones. All IT leaders consider augmented reality as a next “big wave”; thus, the visual coherence is becoming a key issue for IT in general. However, it is in aerospace applications that the visual coherence has already acquired practical significance. An example is Boeing's development of an augmented reality flight simulator, which began in 2022. Visual coherence is a complex problem, one of the aspects of which is to provide the correct overall coloration of virtual objects in an augmented reality scene. The objective of the research was to develop a new method of such tinting.Materials and Methods. The developed method (called spectral transplantation) uses two-dimensional spectral image transformations.Results. A spectral transplantation technology is proposed that provides direct transfer of color, brightness, and contrast characteristics from the real background to virtual objects. An algorithm for automatic selection of the optimal type of spectral transformation has been developed.Discussion and Conclusion. Being a fully automatic process without recording lighting conditions, spectral transplantation solves a number of complex problems of visual coherence. Spectral transplantation can be a valuable addition to other methods of providing visual coherence.

Illumination-guided inverse rendering benchmark: Learning real objects with few cameras

The realm of 3D computer vision and graphics has experienced exponential growth recently, enabling the creation of realistic virtual environments and digital representations of real-world objects. Central to this progression are 3D reconstruction methods that facilitate the virtualization of shape, color, and surface details of real objects. Current methods predominantly employ neural scene representations, which despite their efficacy, grapple with limitations such as necessitating a high number of captured images and the complexity of transforming these representations into explicit geometric forms.An alternative strategy that has gained traction is the deployment of methods such as physically-based differentiable rendering (PBDR) and inverse rendering. These approaches require fewer viewpoints, yield explicit format results, and ensure a smoother transition to other representation methods. To meaningfully assess the performance of different 3D reconstruction methods, it is imperative to utilize benchmark scenes for comparison.Despite the existence of standard objects and scenes within the literature, there is a noticeable deficiency in real-world benchmark data that concurrently captures camera, illumination, and scene parameters — all critical to high-fidelity 3D reconstructions using PBDR and inverse rendering-based methods. In this research, we introduce a methodology for capturing real-world scenes as virtual scenes, integrating illumination parameters alongside camera and scene parameters to enhance the veracity of virtual representations. In addition, we introduce a set of ten real-world scenes, along with their virtual counterparts, designed as benchmarks. These benchmarks encompass a fundamental variety of geometric constructs, including convex, concave, plain, and mixed surfaces. Additionally, we demonstrate the 3D reconstruction results of state-of-the-art 3D reconstruction methods employing PBDR in real-world scenes, using both established methodologies and our proposed one.

A multimodal feature fusion image dehazing method with scene depth prior

AbstractCurrent dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene. The authors propose a multimodal feature fusion image dehazing method with scene depth prior based on a decoder–encoder backbone network. The multimodal feature fusion module was first designed. In this module, affine transformation and polarized self‐attention mechanism are used to realize the fusion of image colour and depth prior feature, to improve the representation ability of the model for different scale ground haze feature in‐depth space. Then, the feature enhancement module (FEM) is added, and deformable convolution and difference convolution methods are used to enhance the representation ability of the model for the geometric and texture feature of the ground objects. The publicly available dehazing datasets are used for comparison and ablation experiments. The results show that compared with the existing classical dehazing networks, the peak signal‐to‐noise ratio (PSNR) and SSIM of the authors’ proposed method have been significantly improved, have a more uniform dehazing effect in different depth spaces, and maintain the colour and edge details of the ground objects very well.

Probing the unimaginable: The impact of aphantasia on distinct domains of visual mental imagery and visual perception

Different individuals experience varying degrees of vividness in their visual mental images. The distribution of these variations across different imagery domains, such as object shape, color, written words, faces, and spatial relationships, remains unknown. To address this issue, we conducted a study with 117 healthy participants who reported different levels of imagery vividness. Of these participants, 44 reported experiencing absent or nearly absent visual imagery, a condition known as “aphantasia”. These individuals were compared to those with typical (N = 42) or unusually vivid (N = 31) imagery ability. We used an online version of the French-language Battérie Imagination-Perception (eBIP), which consists of tasks tapping each of the above-mentioned domains, both in visual imagery and in visual perception. We recorded the accuracy and response times (RTs) of participants' responses. Aphantasic participants reached similar levels of accuracy on all tasks compared to the other groups (Bayesian repeated measures ANOVA, BF = .02). However, their RTs were slower in both imagery and perceptual tasks (BF = 266), and they had lower confidence in their responses on perceptual tasks (BF = 7.78e5). A Bayesian regression analysis revealed that there was an inverse correlation between subjective vividness and RTs for the entire participant group: higher levels of vividness were associated with faster RTs. The pattern was similar in all the explored domains. The findings suggest that individuals with congenital aphantasia experience a slowing in processing visual information in both imagery and perception, but the precision of their processing remains unaffected. The observed performance pattern lends support to the hypotheses that congenital aphantasia is primarily a deficit of phenomenal consciousness, or that it employs alternative strategies other than visualization to access preserved visual information.

Effects of broiler chilling methods on the occurrence of pale, soft, exudative (PSE) meat and comparison of detection methods for PSE meat using traditional and Nix colorimeters

The aims of this study were to i) estimate the occurrence of pale, soft, and exudative (PSE) meat in modern commercial Ontario broiler flocks, ii) determine the effects of the chilling method (water vs. air) on PSE meat, and iii) investigate a new inexpensive colorimeter (10% of the price of traditional color meters), the Nix Color Sensor, as an objective color measurement of chicken meat. Between June 2019 to March 2020, a total of 17 different broiler flocks were processed. The color of 1,700 boneless skinless Pectoralis major muscles was randomly measured (100/flock), where 255 samples were also measured for pH, water-holding capacity (WHC), cooking loss, and penetration force. In addition, a traditional Minolta colorimeter was used to measure random 95 samples from a single water-chilled flock and subsequently compared the values obtained with the Nix Color Sensor. Strong correlations of L* values (rho=0.75; P < 0.001), a* values (rho=0.72; P < 0.001), and b* values (rho=0.80; P < 0.001) were observed. When an L* value of 43 was used as the cut-off for the Nix, 12.5% of fillets were classified as PSE meat. Statistical differences (P < 0.05) were observed between the air and water-chill methods for L*, pH, and WHC. However, there were no significant differences observed between the 2 methods for cooking loss and penetration force values. The study indicated that PSE meat is still a challenge in Ontario broilers, and that the L*, pH, and WHC of breast meat (all indicate meat quality) are affected by the chilling method. In addition, the Nix was found to be an affordable, objective, and convenient sensor for measuring chicken meat color.