Recognition Success Rate Research Articles

A Hybrid Approach for Automated Plant Leaf Recognition Using Hybrid Texture Features and Machine Learning-Based Classifiers

Automated plant recognition performs a significant role in various applications used by environmental experts, chemists, and botany experts. Humans can recognize plants manually, but it is a prolonged and low-efficiency process. This paper introduces an automated system for recognizing plant species based on leaf images. A hybrid texture and colour-based feature extraction method was applied on digital leaf images to produce robust feature, and a further classification model was developed. A combination of machine learning methods, such as SVM (support vector machine), KNN (k-nearest neighbours), and ANN (artificial neural network), was applied on dataset for plant classification. This dataset contains 32 types of leaves. The outcomes of this work proved that success rate of plant recognition can be enhanced up to 94% with ANN classifier when both shape and colour features are utilized. Automatic recognition of plants is useful for medicine, foodstuff, and reduction of chemical wastage during crop spraying. It is also useful for identification and preservation of species.

Door Access System Using Face Recognition

This is a security system to restrict access to rooms using Raspberry Pi as main controller and implementation of Histogram of Oriented Gradients (HOG) feature extraction method. Upon face detection, the system is able to do face recognition for person identification by matching face embedding of the person face with one that already stored in database. This system also presents a random head-pose challenge to identify a real person from an image and then activate a solenoid to unlock the door. In this experiment, if the person is not wearing any face attributes, this system is tested to have 100% successful detection and recognition rate at 60 cm distance. If the person is wearing mask attribute, this system is tested to have 70% successful rate for detection and 45% successful rate for recognition at 60 cm distance.

An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.

A Field-Tested Harvesting Robot for Oyster Mushroom in Greenhouse

The fully autonomous harvesting of oyster mushrooms in the greenhouse requires the development of a reliable and robust harvesting robot. In this paper, we propose an oyster-mushroom-harvesting robot, which can realize harvesting operations in the entire greenhouse. The two crucial components of the harvesting robot are the perception module and the end-effector. Intel RealSense D435i is adopted to collect RGB images and point cloud images in real time; an improved SSD algorithm is proposed to detect mushrooms, and finally, the existing soft gripper is manipulated to grasp oyster mushrooms. Field experiments exhibit the feasibility and robustness of the proposed robot system, in which the success rate of the mushroom recognition success rate reaches 95%, the harvesting success rate reaches 86.8% (without considering mushroom damage), and the harvesting time for a single mushroom is 8.85 s.

Emotion Analysis using Facial Expressions in Video

The topic of human computer interaction is one of the increasingly popular topics in recent times. Human facial expression and emotion analysis with the computer is one of the complex problems as well as interesting. In this paper, emotion analysis was made on human images. In the study, 5 different emotional states, being happy, angry, sad, surprised and neutral, were analyzed. The proposed algorithm basically consists of 3 steps. The first is the preprocessing of the images required for the SVM model. The second is the creation of the SVM model that could perform emotion analysis. The final step is to assign facial expressions to the relevant emotion class. In this study, JAFFE dataset and many images available from Google were used. The recognition success rates of 5 different emotions determined for the study were found between 80% and 100%.

RETRACTED ARTICLE: Remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering

With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveying and mapping work. This article mainly studies the application of remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. First, perform grayscale processing on the image in the experiment, then remove the noise in the image and smooth the image through the median filter method and finally use the Canny operator to perform edge detection to obtain a binarized image containing only the target object, and this is done by image feature extraction. After using three-dimensional scanning modeling to extract the image feature points, the target manager is used for sample analysis. Obtain the projection matrix through the interface, and then perform coordinate conversion to complete the positioning of the target scene. In this paper, the BRISK feature point detection algorithm with fast speed and small calculation is used to detect the target, and SVM is used for remote sensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remote sensing image processing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.

Freehand Sketching Portrait Recognition with Least Square CycleGAN

Freehand sketching portrait recognition refers to the recognition of sketched portraits and face photos drawn by artists. Existing research mainly involves inputting a given real portrait and converting it into a similar sketch portrait, and then matching the face with the sketch portrait in the real portrait database. This paper starts from the idea of inputting a sketch image to identify the real portrait in the database, focuses on the research based on the method of portrait synthesis, and introduces the existing methods of sketch portrait identification. We use the CycleGAN improved by least squares to achieve the translation from sketch to portrait, and finally PCA was used to complete face matching. The results show that LS_CycleGAN has certain advantages. Compared with other methods, its synthesis results are most close to the portrait. The average score of SSIM and PSNR is 0.844 and 17.331, and the average success rate of recognition is 88.2% for Rank10.

Face Recognition Based on Haar Like and Euclidean Distance

In order to improve the recognition rate of multi-face image, this paper proposes a face image recognition method based on haar-like and Euclidean distance. First of all, the initial features of Haar are moved in the image and gradually enlarged, the pixel sum of the feature area is obtained quickly by using the integral graph. The Haar feature is obtained by calculating the difference between black and white pixels in the feature region, and the threshold of weak classifier is calculated. Then, the extracted facial feature data is trained and classified to form a cascade classifier for face detection. Finally, the face of the image to be detected is compared with the training samples by using Euclidean distance. The experimental results show that the time of one attendance is 5-10 seconds and the success rate of face recognition is 91.1%, which verifies the advantage of the algorithm in improving the efficiency of attendance.

Deep Localization of Static Scans in Mobile Mapping Point Clouds

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

Identification of soil piping-related depressions using an airborne LiDAR DEM: Role of land use changes

The detection of soil piping features is still a challenging issue that requires further studies, especially at larger scale. It seems that field mapping as a time-consuming method can be supported by the new technologies. This study has exploited the freely available airborne LiDAR (Light Detection and Ranging) digital elevation model (DEM) to explore piping-related depressions. The aim was to test the possibility of digital detection of piping forms using sink-fill method and different derivatives of airborne LiDAR DEM (slope, Topographic Position Index, Topographic Ruggedness Index, Positive and Negative Openness Index) in order of its utility in regional scale studies. As geological structure, soils and climate are similar in the whole study area, the analysis was extended to include the changes in land use/land cover (LULC) in the last two centuries (1862–2017). The results were verified by the detailed geomorphological mapping in the field. The study was conducted in a small catchment in the Bieszczady Mts. (SE Poland) covered by forests and grasslands/pastures, where in the past transition from pastures to forests was observed. The findings have revealed the great impact of LULC changes on soil piping, thus also on the possibility of digital mapping which is often based only on topographic information. The success rate in recognition was satisfactory on grasslands/pastures (76% for individual forms and 80% for piping systems). Areas covered by forests since the mid-19th century are unlikely places to develop piping forms (just 2 forms found) in the study area, whereas places where transition from pastures to forest has been observed may be prone to soil piping. However, the digital identification of piping forms in areas with rough topography is difficult (a success rate of 45% for individual forms and 50% for piping systems). The digital mapping of piping forms cannot rely only on the topographic information but should be always accompanied by an analysis of factors controlling piping erosion.

An efficient enhanced automatic target recognition approach

This paper presents a new efficient enhanced automatic target recognition approach based on hybrid feature and intensity based methods. The proposed approach is based on comparison and matching between the acquired target image and the stored database images through edge detection, morphological operation, image transformation, image histogram matching and mutual information for both target and stored database images. Sobel operator is used to detect edges in both acquired target image and the stored database images. Hough transform is used as an image transformation which groups the extracted edge points in edged images to an appropriate set of lines which helps in features extraction and matching processes in both of target and stored database images. This gives an initial indication about realization and recognition between target image and its corresponding database image. Comparisons of image histogram and mutual information for both target and database images are finally used to emphasize the recognition of the target image and its verification with its corresponding database image. The proposed recognition approach passed through four stages which are: edge detection by Sobel edge detector, thinning as a morphological operation, Hough transformation, matching process using median deviation and finally histogram matching and measuring the mutual information between target and the available database images. The experimental results proved that the proposed hybrid target recognition approach gives a more accurate and successful recognition rate than other techniques which depend on using feature based methods or intensity based methods.

Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data

With the advent of the era of big data, great changes have taken place in the insurance industry, gradually entering the field of Internet insurance, and a large amount of insurance data has been accumulated. How to realize the innovation of insurance services through insurance data is crucial to the development of the insurance industry. Therefore, this paper proposes a ciphertext retrieval technology based on attribute encryption (HP‐CPABKS) to realize the rapid retrieval and update of insurance data on the premise of ensuring the privacy of insurance information and puts forward an innovative insurance service based on cloud computing. The results show that 97.35% of users are successfully identified in test set A and 98.77% of users are successfully identified in test set B, and the recognition success rate of the four test sets is higher than 97.00%; when the number of challenges is 720, the modified data block is less than 9%; the total number of complaints is reduced from 1300 to 249; 99.19% of users are satisfied with the innovative insurance service; the number of the insured is increased significantly. To sum up, the insurance innovation service based on cloud computing insurance data can improve customer satisfaction, increase the number of policyholders, reduce the number of complaints, and achieve a more successful insurance service innovation. This study provides a reference for the precision marketing of insurance services.

Removing light interference to improve character recognition rate by using single-pixel imaging

Optical Character Recognition (OCR), which is generally based on figure recognition, is mainly effective for extracting text from pictures. However, it is a challenge to maintain a high recognition rate without being affected by light interference in a complicated environment. To reduce the light interference for improving the OCR recognition rate, this article proposes a method utilizing single-pixel imaging to obtain images without light interference for character recognition. Unlike the traditional CCD/CMOS-based imaging relying on image post-processing to eliminate interference, the method proposed without the need of any image post-processing which is a cumbersome process and often leads to a deterioration in the quality of the obtained images. Theoretical analysis shows that single-pixel imaging can eliminate the light interference in certain conditions completely. The verification experiments demonstrate that single-pixel imaging can only image the target and ignore an introduced highlighted stripe, whereas traditional CCD/CMOS-based imaging can not eliminate the effects of the stripe without image post-processing. Comparison experiments in contrast with the character recognition rate of the picture obtained from traditional imaging demonstrate the counterpart obtained from single-pixel imaging increased from 88.64% to 97.73%. The evaluation index of correlation and image quality illustrates that the picture obtained by single-pixel imaging has a higher correlation with the target picture and better image quality than the counterpart of the picture obtained from traditional imaging. When the character recognition rate of the picture obtained by traditional imaging is 0% by adding a serious interference pattern from a projector, the counterpart by single-pixel imaging is 95.45%. This approach provides a welcome boost to the development of the application of single-pixel imaging in character recognition by reducing the light interference, which provides a potential direction for the development of OCR to obtain higher recognition success rate under a complex environment.

A Comparative Analysis of Biometrics Types: Literature Review

This study provides a thorough analysis of the various biometric types, including their advantages and disadvantages. It compares the different types and provides details about false acceptance and false rejection rates, along with their equations. Biometric screening systems are used to test and identify persons using their physiological or behavioral characteristics. Using only one recognition device is not suitable for identification systems. Multi-factor authentication can improve system security by using two or more kinds of security types, such as passwords and cards, but this is not an ideal security scheme. Passwords may be forgotten or inputted incorrectly, or the identification card may be stolen. To verify and classify people using their physiological attributes, biometric devices are used. These technologies can be classified as either behavioral or physiological biometrics. The former has many shortcomings, such as noisy data, inter-class similarity, intra-class variability, spoofing and universality, which reduce the system’s accuracy. The success rate of recognition and verification is, however, substantially improved by multimodal biometric sensing and processing systems, which leverage the detection or processing of two or more behavioral or physiological traits.

Design of a downlink algorithm for rotary steering (logging while drilling) systems

For logging while drilling (LWD) systems, it is necessary to adjust the working state of the downhole tools in real-time according to different operating conditions. In this paper, on the basis of the characteristics of LWD systems, a mud pressure-apperceived downlink system was examined. For the design of this system, a signal acquisition and processing board was created based on a piezoelectric ceramic sensor to acquire the mud pressure signal. The error sources of the downlink command sending process were analyzed, and an error accumulation compensation processing algorithm was proposed to improve the recognition success rate of the downhole system. Moreover, to reduce noise interference on the characteristics of the mud impulse signal, a filtering algorithm was proposed based on the empirical mode decomposition method, and the success rate of instruction issuance was determined by identifying feedback instructions. Field tests were conducted to further improve the system, the results of which suggested that the system had good mud adaptability, high recognition success rate, and a certain application value.

Low-Cost Online Handwritten Symbol Recognition System in Virtual Reality Environment of Head-Mounted Display

Virtual reality is an important technology in the digital media industry, providing a whole new experience for most people. However, its manipulation method is more difficult than the traditional keyboard and mouse. In this research, we proposed a new low-cost online handwriting symbol recognition system to accurately identify symbols by user actions. The purpose was low cost processing without requiring a server. Experimental results showed that the average success rate of recognition was 99.8%. The execution time averaged a significantly low 0.03395 s. The proposed system is, respectively, highly reliable and at a low cost. This implies that the proposed system is suitable for applications in real-time environments.

Automatic music transcription based on convolutional neural network, constant Q transform and MFCC

Automatic music transcription is a function that relies on the efficiency of the computer’s own calculation speed to recognize the pitch of external input audio and output it accurately. In the past methods, there are often problems such as inability to reflect music continuity or narrow identifiable frequency range. In order to avoid such problems, two methods different from the previous Mel Cepstral Coefficients and Constant Q Transform are selected to extract the features of music, and convolutional neural networks are used for training and recognition. Among them, the Mel Cepstrum coefficient is used to judge the timbre, and the constant Q transform is used to judge the pitch. After inputting the corresponding features into the neural network for training and learning, we can find that the recognition success rate has reached 95%.

Voice Communication in Noisy Environments in a Smart House Using Hybrid LMS+ICA Algorithm.

This publication describes an innovative approach to voice control of operational and technical functions in a real Smart Home (SH) environment, where, for voice control within SH, it is necessary to provide robust technological systems for building automation and for technology visualization, software for recognition of individual voice commands, and a robust system for additive noise canceling. The KNX technology for building automation is used and described in the article. The LabVIEW SW tool is used for visualization, data connectivity to the speech recognizer, connection to the sound card, and the actual mathematical calculations within additive noise canceling. For the actual recognition of commands, the SW tool for recognition within the Microsoft Windows OS is used. In the article, the least mean squares algorithm (LMS) and independent component analysis (ICA) are used for additive noise canceling from the speech signal measured in a real SH environment. Within the proposed experiments, the success rate of voice command recognition for different types of additive interference (television, vacuum cleaner, washing machine, dishwasher, and fan) in the real SH environment was compared. The recognition success rate was greater than 95% for the selected experiments.

A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system

Machine Learning (ML) has been applied in so many areas in reason of its robustness, usability, and reliability, mainly in hardware implementation. One of its well-known algorithms is the Support Vector Machine (SVM), the simplest to be applied in hardware because of its mathematical modeling. In this study, we propose the implementation in hardware of SVM multi-class classifiers within the asynchronous paradigm (i.e., without clock signal application) in a 4-stage pipeline architecture. With the purpose to evaluate the proposed architecture behavior, we used a Field Programmable Gate Array (FPGA) device to prototype the circuit. The proposed asynchronous SVM classifier is applied in a Speech Recognition system of 30 classes where a hybrid training algorithm was processed in the Matlab software, known as PSO-SVM training algorithm. Therefore, the training phase was processed in software in reason of its computational load. For the SVM classification phase, we propose, for the first time to the best of our knowledge, an asynchronous pipeline architecture of four stages with Multiply-Accumulator (MAC) unit application and three different control circuits described from Extended Burst-Mode (XBM) and State Transition Graph (STG) specifications, leading to energy-efficient design. In order to validate the SVM recognition results in the speech recognition application, the tests are from 60 speeches and 20 speakers, so it is a diversified and reliable data set of tests. The main goal here was to design a machine learning hardware implementation in a low power application and, through that, to prove that the asynchronous paradigm reduced power. As a result, we obtained a reduced power consumption of 5.72 mW, a fast average response time which was 0.61μs and the most area-efficient circuit (1315 LUTs); the accuracy in recognition success rate was another preoccupation, and it was very successful, 98% of success. Besides, we present comparisons with an asynchronous version of the same SVM datapath and with different synchronous architectures from literature to prove that our novelty is better in power consumption and area size. For hardware applications where low power and high performance are the sought features, the presented architecture revealed the best position when compared to similar works from the recent technical literature for pattern recognition systems.

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots.

This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.