Color Image Research Articles

RGB‐D road segmentation based on cross‐modality feature maintenance and encouragement

AbstractDeep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency.

Preparation and properties of micro-arc oxidation ceramic coatings using machine vision technology

In this paper, a machine vision system is combined with non-immersion micro-arc oxidation (MAO) technology, using color as a visual feature, and automatically determining the MAO process through a self-developed color image segmentation program for HSV channel. The robotic arm accurately regulates the machining position and duration. Dense ceramic oxide coatings of similar quality to the artificially prepared coatings were successfully prepared on aluminum (Al) surface. The surface morphology and elemental composition of the samples were characterized using scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and X-ray diffraction (XRD). The performance of the coatings was tested through thickness tests, hardness tests, and roughness tests. The tribological properties were tested by friction experiments, and its corrosion resistance was tested by electrochemical corrosion tests. The results confirm the effectiveness of a machine vision system for MAO process control, which is capable of preparing high-quality coatings comparable to manual operations. This not only solves the need for precise film formation in different application scenarios, but also provides a more efficient, controllable and innovative solution for the MAO process.

Explainable deep learning for sEMG-based similar gesture recognition: A Shapley-value-based solution

Surface electromyography (sEMG) based gesture recognition shows promise in enhancing human-robot interaction. However, accurately recognizing similar gestures is a challenging task, and the underlying mechanisms of gesture recognition are not well understood. To address these issues, we developed a new solution called the Shapley-value-based similar gesture recognition (SV-SGR) method. Our solution combines deep learning and game theory to achieve high recognition accuracy and interpretability. First, we devised a data preprocessing method that converts sEMG signals into sEMG color images, which can be more effectively utilized by deep learning techniques. Next, we established a deep-neural-network-based model for gesture recognition using the processed sEMG color images. Then, we designed a global explanation approach based on Shapley values to quantify the contribution of each channel to recognizing similar gestures. Finally, we carried out an explanation analysis, which provides feedback on the recognition model to enhance the precision of gesture recognition. Extensive comparisons and interpretable analyses have been conducted on real-world datasets, and the results demonstrate that the SV-SGR method outperforms other baselines under various experimental conditions. The interpretable analysis method based on Shapley values effectively enhances the performance of recognizing similar gestures and provides valuable insights into the decision-making process of recognition models.

Classification of plant leaf diseases using deep neural networks in color and grayscale images

For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

Automated classification of saffron and broadleaf weeds of Flixweed and Hoary Cress using deep learning and color images

Today, the use of precision agriculture strategies that lead to effective weed management and minimize damage to crops is of great importance. In this research, color images and deep learning were used to distinguish Saffron from two common broadleaf weeds, namely Hoary Cress and Flixweed. A total of 291 photos of the three classes were taken under completely natural field conditions. The image data were resized to 150 × 150 pixels, normalized to [0,1] range, and then augmented. A total of 24 deep learning models with varying architectures and parameters were evaluated to identify the most optimal structure. The proposed models were established on the Xception network and leveraged initial weights from ImageNet via the transfer learning approach. These models underwent fine-tuning and modification by introducing additional layers to the base model. Ultimately, two models were proposed based on their performance in accuracy and loss on test data. One model demonstrated impeccable performance, attaining 100% accuracy and a loss of 0.41, despite potential network instability. It successfully differentiated all classes with precision, recall, and F1-score all at 100%. The second model proposed exhibited a more stable performance with 95% accuracy and a loss of 3.78. It achieved precision ranging from 91% to 100%, recall from 88% to 100%, and F1-score from 91% to 96% across the three classes, showing promising results. The results of this research can be considered as a basis for the development of weed removal robots for the purpose of automatic weed control using precision agriculture strategy.

Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery.

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR) parameterized by multilayer perceptrons (MLPs), which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization, and utilize MLPs to paramterize factor functions of the tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

Vein pattern visualisation for biometric identification with cGAN on a New Zealand dataset

Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein disclosure, to enhance vein pattern detection. However, these methods still face limitations in reliability when compared to Near-Infrared (NIR) reference images. One of the biggest challenges of the studies is the limited number of available datasets that have synchronised colour and NIR images from body limbs. This paper introduces a new dataset comprising 602 pairs of synchronised NIR and RGB forearm images from a diverse population, ethically approved and collected in Auckland, New Zealand. Using this dataset, we also propose a conditional Generative Adversarial Networks (cGANs) model to translate RGB images into their NIR equivalents. Our evaluations focus on matching accuracy, vein length measurements, and contrast quality, demonstrating that the translated vein patterns closely resemble their NIR counterparts. This advancement offers promising implications for forensic identification techniques.

A method for grasp detection of flexible four-finger gripper

The flexible four-finger gripper, as a specialized robotic end-effector, is highly valued for its ability to passively adapt to the shape of objects and perform non-destructive grasping. However, the development of grasping detection algorithms for flexible four-finger grippers remains relatively unexplored. This paper addresses the unique characteristics of the flexible four-finger gripper by proposing a grasping detection method based on deep learning. Firstly, the Acute Angle Representation model (AAR-model), which is based on the structure of the flexible four-finger gripper and consists of grasp points and angles, is designed as the grasping representation model that reduces unnecessary rotations of the gripper and improves its versatility in grasping objects. Then, the Flexible Gripper Adaptive Attribute model (FGAA-model) is proposed to represent the grasping attributes of objects, calculate the grasp angles that meet the criteria of the AAR-model, and aggregate the AAR-models on the image data into a unified set, thereby circumventing the time-consuming process of pixel-level annotation. Finally, the Adaptive Grasping Neural Net (AGNN), which is based on Adaptive Feature Fusion and the Grasp Aware Network (AFFGA), is introduced by eliminating redundant network detection headers, fusing color and depth images as inputs, and incorporating a Series Atrous Spatial Pyramid (SASP) structure to produce more accurate grasp poses. Our method not only attains a remarkable accuracy of 97.62% on the Cornell dataset but also swiftly completes grasping detection within 25 ms. In practical robotic arm grasping tests, where a robot is outfitted with a flexible four-finger gripper, it successfully grasps unknown objects with a 96% success rate. These results underscore the reliability and real-time performance of our method, significantly enhancing the gripper's adaptability and precision when handling objects of varying sizes and shapes. This advancement provides a powerful technical solution for robots utilizing flexible four-finger grippers, enabling autonomous, real-time, and highly accurate grasping maneuvers. Moreover, it addresses the persistent challenge of the scarcity of efficient grasping detection techniques tailored for flexible four-finger grippers.

6-DoF grasp estimation method that fuses RGB-D data based on external attention

6-DoF grasp estimation methods based on point clouds have long been a challenge in robotics due to the limitations of single data input, which hinder the robot’s perception of real-world scenarios, thus reducing its robustness. In this work, we propose a 6-DoF grasp pose estimation method based on RGB-D data, which leverages ResNet to extract color image features, utilizes the PointNet++ network to extract geometric information features, and employs an external attention mechanism to fuse both features. Our method is an end-to-end design, and we validate its performance through benchmark tests on a large-scale dataset and evaluations in a simulated robot environment. Our method outperforms previous state-of-the-art methods on public datasets, achieving 47.75mAP and 40.08mAP for seen and unseen objects, respectively. We also test our grasp pose estimation method on multiple objects in a simulated robot environment, demonstrating that our approach exhibits higher grasp accuracy and robustness than previous methods.

Reversible data hiding with automatic contrast enhancement for color images

Automatic contrast enhancement (ACE) is a technique that can automatically enhance the image contrast. Reversible data hiding (RDH) with ACE (ACERDH) can achieve ACE while hiding data. However, some methods with good performance for color images suffer from insufficient enhancement. Therefore, an ACERDH method based on the R, G, B, and V channels enhancement is proposed. First, histogram shifting with contrast control is proposed to enhance the R, G, and B channels. It can prevent contrast degradation and histogram shifting from stopping prematurely. Then, the V channel is enhanced. Since some RDH methods with non-ACE that can well enhance the V channel have a low automation level, histogram shifting with brightness control that can realize ACE very well is proposed. It can effectively avoid over-enhancement by controlling the brightness. Experimental results verify that the proposed method improves the image quality and embedding capability better than some state-of-the-art methods.

Dynamic Analysis, Image Encryption Application and FPGA Implementation of a Discrete Memristor-Coupled Neural Network

This paper presents a novel discrete memristor model that incorporates exponential and absolute value functions. A discrete coupled memristor neural network model is constructed based on this memristor design. The periodic and chaotic regions of the discrete neural network model are determined using bifurcation and Lyapunov exponent spectrum. Furthermore, by varying the initial values of the discrete memristive neural network, we observe the coexistence of chaos and periodic attractors, as well as periodic attractors. Additionally, an application to color image encryption based on the discrete system model is given. Security analysis is conducted in the aspects of key space, histogram analysis, correlation analysis, sensitivity analysis, Peak Signal-to-Noise Ratio (PSNR), and information entropy analysis. The analysis results show that the algorithm has a key space size of [Formula: see text], and the information entropy of baboon graph is 7.9993, which is very close to the ideal value of 8. It shows that the image encryption algorithm is feasible and effective. Finally, the implementation of the discrete memristive neural network model is realized using Field Programmable Gate Array (FPGA). The experimental implementation is conducted using the Verilog language on the Vivado 2018.3 platform, and the obtained results align with the numerical simulation results obtained through MATLAB.

Total Variations for Hue, Saturation, and Value of a Color Image

Total Variations for Hue, Saturation, and Value of a Color Image

BBMS: Investigation to Single Colour Image Visibility Improvement in Turbid Media through Biorthogonal Wavelet Based Depth-map Estimation

Electronic imaging needs good quality, high resolution (HR) digital images for highlighting finer details of the image. Normally images captured by ordinary digital camera are post pro cessed with available software instead of high cost CMOS sensor phased camera. Problem commonly faced with these images may get degraded due to the scattering media which deteriorates contrast, shifts colour, and make overall image whitish. Information from the distant objects suffer from poor medium transmission as well as noise amplification. Biorthogonal wavelet denoising (BWD), compress sparsely in frequency -time space, removes noise from depth estimation and makes the transmission smooth. Moreover, single image recovery is a serious challenge and ill posed problem. The recovered image improves compare to degraded image significantly and dis cards possibility of false edge detection as well as prevent colour shift and low contrast. Good quality images are found with objective evaluation. Moreover, different wavelets, thresholds, and decomposition levels have been studied and compared. Time complexity is linear due to wavelet domain analysis which makes the technique fast, reliable, and visually pleasing.

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition

Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.

Virtual Canvas using OpenCV

Abstract: The main purpose of this project is to identify the human gestures according to that specific gesturesit will perform specific tasks. Project are going to use OpenCV library, Python programming language and also we will use colour detection and image segmentation techniques to achieve this. OpenCV is an open source computer vision library for performing various advanced image processing. In colour detection we can detect any colour in a given range of HSV colour space. The image segmentation is the process of labelling every pixel in an image, where each pixel shares the same certain characteristic. Python is use for developing this whole project. After processing is complete for those input gestures it will show output on screen according to that user inputs. The main objective of the project is that the “Virtual Canvas” and put before readers some useful information regarding our project. We have made sincere attempts and have tried to present this matter in precise and compact form, the language being as simple as possible. We are sure that the information contained this volume would certainly prove useful for better insight in the scope and the dimension of this project in this true prospective. The perseverance and deep involvement shown by our group members made this vague task look very interesting and simple. We have designed this software project completely from scratch and we have not incorporated any readymade material from the Internet or any other sources to make our project seemingly more attractive and meaningful

Developing Pop-Up Book Teaching Media for Teaching Procedure Text at Grade 7

This research aims to develop a pop-up book as a teaching media for teaching procedure text at grade 7. Pop-up book is a book that when opened could display images that appeared to pique student enthusiasm in studying and direct their focus. This type of research is Research and Development based on ADDIE model which consists of Analysis, Design, Development, Implementation, And Evaluation. However, in this study the researcher only conducted research up to the Development stage. The data were obtained from English teacher interviews, curriculum analysis, and validation sheets. The results revealed that the lack of media use during the learning process and it is also known that students like visual media consisting of colorful images. The researcher developed a pop-up book media that received good responses from material experts and media experts. The validation results of the pop-up book media for procedure text material in grade 7 are valid. Pop-up book is categorized as very feasible because it gets 100% percentage on material validation, and gets 100% percentage on media validation.

Optimizing Visual Sensor Node Image Compression for Resource-Constrained Environments

Abstract: An Approximate hybrid multiplier design is proposed to reduce the data-path width of lifting 2-D discrete wavelet transform (DWT) especially for wireless visual sensor node applications. The proposed multiplier design takes multiplier operand of size less by 3 bit and calculates full-width outputs with marginal loss of accuracy, which is significant. The proposed approximate 2-D DWT structure uses data-path DWT levels and produces reconstructed color images higher than the 70-dB peak-signal-to-noise ratio (PSNR). Compared with the existing fractional wavelet transform (FrWT)-based 2D DWT structures, the proposed structure has a smaller data-path width and free from overhead. Compared with the existing parallel structure

Research on Emotional Analysis and Emotional Color Extraction Algorithm of Chinese Language and Literature Based on Emotional Computing

In China, the emotional response of diverse cultural bearers, including literature, movies, and other media, frequently mirrors the country's cultural improvement. Consequently, emotional analysis of the text and paintings may be used to examine the cultural evolution, determine its growth dynamics, and sort out its context. The project aims to investigate how well sophisticated affective computing performs in identifying and analyzing the emotional aspects of traditional Chinese literature and language. The study selects the Double Channel based Self-organized Residual Network-50 V2 (DC-SoResNet 50V2) algorithm based on a combination of deep learning algorithms and further improving the (DC-SoResNet 50V2) algorithm using Emperor penguin Colony optimizer (EPCO). Moreover, the color image and text features from traditional Chinese literature are extracted using hummingbird-based term frequency-inverse document frequency (HB-TF-IDF) and Stochastic Color Harmony Algorithm (SCHA). Comparisons are made between the proposed and standard approaches using the training and test sets, respectively. The training time of the proposed approach is stable at around 25.1 s, while the test time is steady at about 18.5 s. The emotion identification accuracy of the proposed method achieved average of 99.11 and 99.23% in testing and training sets for the entire datasets, respectively. Comprehensive analysis of classic literary works can be possible with the help of this research, which offers fresh viewpoints and theoretical references.

Design of a Multimodal Detection System Tested on Tea Impurity Detection

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.