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An intelligent cleaning vehicle improves the efficiency of road cleaning to a great extent. In this case, road garbage recognition is fundamental and crucial. Usually, the pebbles are too small, and the sand has an inconspicuous boundary and unfixed shape on the roads. These issues make the stones and sand too hard to be detected by a garbage recognition system. Hence, we designed a novel semantic segmentation network that acquires the areas and categories of road garbage. We designed a Deep Supervision and High Fusion (DSHF) block to improve the road garbage segmentation accuracy. The designed block with a backbone network of HFCN and UNet++ is comparable with a model that only has either a deep supervision block or a high fusion block. According to the collected road garbage segmentation dataset comprising four categories (stones, leaves, sand and bottles), the model we designed has improved the metric value of MPA by 3% over the state-of-art methods and achieved the highest MIoU (Mean Intersection over Union) with the value of 77.92%. The results of our experiments show that the semantic segmentation of road garbage is feasible and that the proposed DSHF block is practical for improving the segmentation effect of road garbage.

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

The Commercial Vehicle Safety Alliance (CVSA) aims to achieve uniformity, compatibility and reciprocity of commercial motor vehicle inspections and enforcement by certified inspectors dedicated to driver and vehicle safety. Commercial vehicles that pass a CVSA inspection are eligible for a decal representing a commitment to safety. In this work, we propose a two‐step automatic CVSA decal recognition system using deep convolutional neural network architectures. The first step localizes a vehicle's windshield and the CVSA decal within, and classifies the decal colour. The CVSA decal is cropped and used as input to the second stage, which localizes and classifies a digit and the corner‐cut of a CVSA decal. With the corner cut, colour, and digit, the system can determine the decal's date of issue. We use as our baseline the MobileDet architecture, customizing the backbone to our tasks. Our first custom architecture is larger than the baseline because it needs more representational power to detect small decals within an image. The second architecture is much smaller because digit and corner‐cut recognition is a simpler task. Our custom architectures reduce processing time and exceed accuracies relative to pre‐trained architectures. We implemented our models on different edge hardware accelerators (e.g. the Google Coral, Nvidia Jetsons, and Intel NCS) and compared the performance when processing a real‐time video stream. Our system can predict frames at 173.31 FPS on an NVIDIA Jetson AGX Xavier with 98.5% mean average precision @ 0.5 IoU.

Technology is becoming constantly important for customers. Automatic number plate Recognition (ANPR) is a device which enables the identification of a number plate in real time. For an intelligent car service, ANPR helps to promote growth, customize the classic app and increase consumer and employee productivity. Within the specification, the principal function of ANPR lies of removing the characteristics from an illustration of a license plate. An application that enables customers to display automobile repairs through the license platform number only derived from a loaded picture is augmented by a smart car service. Technological progress is that, so it is thought that improvement is important in this region too, so the best choice for automotive services is a smart car company. This work proposed a methodology to detect the numbers from car license plate using convolutional neural network. In the preprocessing of photographs on license plates, the WLS and FFT filters were included. The images are then fed into the convolutional trainings neural network. On more plates and tests is reported during the testing. Therefore, the findings indicate that the proposed solution can be taken in less time from the license model to accurately identify the characters. The experimental result shows the significance of proposed research by achieving an accuracy of 98% for the localization and true recognition of license plates from the video frames.

Alzheimer's disease (AD) is an advanced and incurable neurodegenerative disease that causes progressive impairment of memory and cognitive functions due to the deterioration of brain cells. Early diagnosis is substantial to avoid permanent memory loss and develop treatments that will be subtracted in the future. Deep learning (DL) is a vital technique for medical imaging systems for AD diagnostics. The problem is multi-class classification seeking high accuracy. DL models have shown strong performance accuracy for multi-class prediction. In this paper, a proposed DL architecture is created to classify magnetic resonance imaging (MRI) to predict different stages of AD-based pre-trained Convolutional Neural Network (CNN) models and optimization algorithms. The proposed model architecture attempts to find the optimal subset of features to improve classification accuracy and reduce classification time. The pre-trained DL models, ResNet-101 and DenseNet-201, are utilized to extract features based on the last layer, and the Rival Genetic algorithm (RGA) and Pbest-Guide Binary Particle Swarm Optimization (PBPSO) are applied to select the optimal features. Then, the DL features and selected features are passed separately through created classifier for classification. The results are compared and analyzed by accuracy, performance metrics, and execution time. Experimental results showed that the most efficient accuracies were obtained by PBPSO selected features which reached 87.3% and 94.8% accuracy with less time of 46.7 sec, 32.7 sec for features based ResNet-101 and DenseNet-201, receptively.

In order to strengthen the management of English classroom discipline and improve the efficiency of students’ English classroom learning, students’ English classroom behavior based on intelligent image recognition is analyzed in IoT (Internet of things). The working scenes and practical significance of deep learning and IoT are analyzed and then the effects of four models on students' behavior analysis in English classroom are discussed. The results show that the classroom behavior analysis model proposed is feasible. The recognition system judges whether the students are listening seriously from three aspects, namely students' side face, head up and down, and their eyelid opening. The comparison of the four models of VGG16, ResNet18, ResNet50 and AlexNet shows that the accurate recognition rate of VGG16 for students' behavior in English classroom reaches 94.15%. Experiments show that the method provides a more objective evaluation of students’ classroom behavior. As a whole, students’ classroom behavior analysis based on IIRT (intelligent image recognition technology) in IOT is practicable for improving English classroom efficiency.

Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.

Animals watching is a common hobby but to identify their species requires the assistance of Animal books. To provide Animal watchers a handy tool to admire the beauty of Animals, we developed a deep learning platform to assist users in recognizing species of Animals endemic to using app named the Imagenet of Animals (IoA). Animal images were learned by a convolutional neural network (CNN) to localize prominent features in the images. First, we established and generated a bounded region of interest to the shapes and colors of the object granularities and subsequently balanced the distribution of Animals species. Then, a skip connection method was used to linearly combine the outputs of the previous and current layers to improve feature extraction. Finally, we applied the SoftMax function to obtain a probability distribution of Animals features. The learned parameters of Animals features were used to identify pictures uploaded by mobile users. The proposed CNN model with skip connections achieved higher accuracy of 99.00 % compared with the 93.98% from a CNN and 89.00% from the SVM for the training images. As for the test dataset, the average sensitivity, specificity, and accuracy were 93.79%, 96.11%, and 95.37%, respectively.

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

This paper presents a system called NPR (Number Plate Recognition) which is based on image processing and is used to detect the number plates of vehicles and process them to record the information. In a fast-growing world, it has become almost impossible to track illegal vehicles and store vehicle information. This is eventually leading to a rise in the crime rate, especially due to manual errors. The proposed system first captures the vehicle image and the vehicle number plate region is extracted using Image Segmentation in an image. The resulting data is then used to compare with the records on a database to come up with specific information like the vehicle's owner, place of registration, address, etc. Further, the system is implemented and simulated in MATLAB for studying feasibility and accuracy on a real image.

Objectives: To create a tablet recognition system that recognizes and gives an audio output of the name of the tablet so that the visually impaired person may recognize it. Methods: In this, to create a user friendly system, such that a blind person does not require the help of another person to use it. These systems give medical assistive instrument to take the drug at the accurate time as per the doctor prescription and removes dependency on others for the visually challenged people. Various models were technologically advanced to compact with misidentification of remedies but are incompetent of formative exact pill picked by the person. Findings: The name of the medicine is obtained from the image. Then the name of the medicine is converted from text to speech using Google text to speech (gTTS). The proposed system will be helpful for visually challenged people, SQLite Database management system has been used along with Scale Invariant Feature Transform algorithm (SIFT) to identify and pronounce local features in the image. The image of the medicine given by the user is matched with the images in the database. Novelty: Developed a user-friendly voice based tablet recognition system for visually impaired people and this system is fast and accurate. Keywords SIFT algorithm, medicine identification, visually impaired people, gTTS, Tablet Recognition

The communication gap between the deaf and public is the concern for both parents and the government of Bhutan. The deaf school urges people to learn Bhutanese Sign Language (BSL) but learning Sign Language (SL) is difficult. This paper presents the BSL digits recognition system using the Convolutional Neural Network (CNN) and a first-ever BSL dataset which has 20,000 sign images of 10 static digits collected from different volunteers. Different SL models were evaluated and compared with the proposed CNN model. The proposed system has achieved 97.62% training accuracy. The system was also evaluated with precision, recall, and F1-score.

This paper presents a sign language recognition system based on color stream and skeleton points. Several approaches have been established to address sign language recognition problems. However, most of the previous approaches still have poor recognition accuracy. The proposed approach uses Kinect sensor based on color stream and skeleton points from the depth stream to improved recognition accuracy. Techniques within this approach use hand trajectories and hand shapes in combating sign recognition challenges. Therefore, for a particular sign a representative feature vector is extracted, which consists of hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD) is applied to obtain a discriminative dictionary. Based on that, the system was further developed to a new classification approach for better results. The proposed system was fairly evaluated based on 21 sign words including one-handed signs and two-handed signs. It was observed that the proposed system gets high recognition accuracy of 98.25%, and obtained an average accuracy of 95.34% for signer independent recognition. Keywords: Sign language, Color stream, Skeleton points, Kinect sensor, Discriminative dictionary.

Background: Automatic licence plate recognition system is used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. Objective: In this paper, a convolutional neural network and support vector machine-based licence plate recognition system is proposed. Method: Firstly, from the input image of the vehicle, the characters are extracted and segmented. Then features of the segmented characters are extracted. The extracted features are classified using convolutional neural networks and support vector machine for the final recognition of the licence plate. Results: The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The obtained results for the proposed hybrid automatic licence plate system is compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. Conclusion: The proposed hybrid ALPR system performs better than NN, SVM, and CNN based ALPR systems.

The incidence and mortality of colon cancer in China are increasing each year. At present, treatment selection for colon cancer patients mainly depends on imaging results, which require a large number of radiologists to interpret. In China, there is a shortage and uneven distribution of experienced radiologists, which leads to delays and bias in the evaluation of imaging data. Based on these considerations, the Colorectal Surgery Group of the Surgery Branch of the Chinese Medical Association in collaboration with experts at Beihang University has independently developed an artificial intelligence (AI)-based recognition system for the preoperative determination of colon cancer stage to partially replace the work of and relieve the pressure on radiologists. These guidelines aim to standardize the use of AI-based recognition systems in the preoperative staging of colon cancer and guide their clinical application.

Gender recognition has been seen as an interesting research area that plays important roles in many fields of study. Studies from MIT and Microsoft clearly showed that the female gender was poorly recognized especially among dark-skinned nationals. The focus of this paper is to present a technique that categorise gender among dark-skinned people. The classification was done using SVM on sets of images gathered locally and publicly. Analysis includes; face detection using Viola-Jones algorithm, extraction of Histogram of Oriented Gradient and Rotation Invariant LBP (RILBP) features and trained with SVM classifier. PCA was performed on both the HOG and RILBP descriptors to extract high dimensional features. Various success rates were recorded, however, PCA on RILBP performed best with an accuracy of 99.6% and 99.8% respectively on the public and local datasets. This system will be of immense benefit in application areas like social interaction and targeted advertisement.

Vehicle Number Plate Detection and Recognition system are very helpful and necessary for efficient traffic monitoring and management. From various angles, the interested vehicle's images can be captured by using some of the strong image processing technique devices for the growth of the smart transport system. Identifying the number plates with the side view images, images with moving distance, numbering, and the number plate type by identifying the background can be improved by using a recognition technique that may contain the combination of the image processing technology combined with neural networks. The tilted images and side view images of moving vehicles can be identified by using a neural network. This can be further useful for the identification of vehicle owners, vehicle model identification, controlling the traffic, controlling the vehicle speed, and tracking of vehicle location. Some algorithms like super-resolution of images can be used for the improvement of low-resolution images. In the future, to gain a better accuracy rate, high-resolution cameras can be used which contain high frames.

Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.

Atrial fibrillation (AF) is a major cardiovascular disease that has affected thousands of individuals worldwide. The electrocardiogram (ECG) is the most extensively applied approach to detect AF at present, while the traditional detection strategy based on the visual observation of ECG data is often laborious and inefficient. In this work, we specially designed an intelligent recognition system based on a novel convolutional neural network that utilizes the multi-scale convolution kernel and bidirectional gated recurrent unit with attention mechanism for AF detection. Also, two standard control groups using 10-fold cross-validation were performed to assess the validity of the proposed model. The empirical results not only demonstrate the high efficiency of multi-scale convolution kernel, but also show that the model has a more superior classification performance to several state-of-the-art methods with an accuracy of 98.3% and 97.7% on two public databases, respectively. Due to its high performance, we plan to develop the model into portable devices to benefit more individuals such as the elderly and athletes.

Finger vein(s) based biometrics is another way to deal with individual's distinguishing proof and has recently received much consideration. The strategy in light of low-level components, like the dark surface of finger vein is taken as standard. However, it is typically looked with numerous difficulties that involves affectability to noise and low neighbourhood consistency. Generally finger vein recognition in view of abnormal state highlights the portrayal that has ended up being a promising method to successfully defeat the above restrictions and enhance the framework execution. This research work proposes finger vein-based recognition technique making use of Hybrid BM3D Filter along with grouped sparse representation for image denoising and feature selection (Local Binary Pattern – LBP, Scale Invariant Feature Transform – SIFT) to evaluate features, key-points and perform recognition. The experimental results on two open databases of finger vein, i.e., HKPU and SDU show that the proposed method has enhanced the overall performance of finger vein pattern recognition system compared with other existing methods.

This paper presents an efficient and layout‐independent Automatic License Plate Recognition (ALPR) system based on the state‐of‐the‐art you only look once (YOLO) object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post‐processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade‐off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end‐to‐end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR‐EU, SSIG‐SegPlate and UFPR‐ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. The authors' system also achieved impressive frames per second (FPS) rates on a high‐end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that the authors manually labelled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

Purpose This paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region. Design/methodology/approach Gesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods. Findings This paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments. Originality/value The modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

Behavior recognition is a fundamental yet challenging task in intelligent surveillance system, which plays an increasingly important role in the process of “Industry 4.0”. However, monitoring the workflow of both workers and machines in production procedure is quite difficult in complex industrial environments. In this paper, we propose a novel workflow recognition framework to recognize the behavior of working subjects based on the well-designed encoder-decoder structure. Namely, attention-based workflow recognition framework, termed as AWR. To improve the accuracy of workflow recognition, a temporal attention cell (AttCell) is introduced to draw dynamic attention distribution in the last stage of the framework. In addition, a Rough-to-Refine phase localization model is exploited to improve localization accuracy, which can effectively identify the boundaries of a specific phase instance in long untrimmed videos. Comprehensive experiments indicate a 1.4% mAP@IoU= 0.4 boost on THUMOS’14 dataset and a 3.4% mAP@IoU= 0.4 boost on hand-crafted workflow dataset detection challenge compared to the advanced GTAN pipeline respectively. More remarkably, the effectiveness of the workflow recognition system is validated in a real-world production scenario.

Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.

Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG signals to identify more gestures without limiting the number of sEMG sensors. However, the residual muscles of an amputee are limited. Therefore, the point of a successful recognition system is to decrease the channels of sEMG signals to classify more gestures. To achieve this goal, we proposed a novel gesture recognition system, in which three channels of sEMG signals can classify nine gestures. In this recognition system, the time domain features, root mean square ratio, and autoregressive model, were selected to extract the features of the sEMG signals as compared with the time–frequency domain features. Furthermore, the linear discriminant analysis was adopted as the classifier. Consequently, the average accuracy rate of the presented system was 91.7%. Therefore, the proposed gesture recognition system is feasible to identify more gestures with less sensors.

In this study, we analyzed technologies for obtaining, processing, segmentation, and transmitting of microphotographs for subsequent recognition. We selected the most promising machine learning algorithms optimal for the processing of medical images, investigated the technologies of analyzing medical texts, studied the aspects of using the Watson neural network for analyzing the semantics of medical images, as well as the aspect of using the unified medical language UMLS for the needs of syndromic diagnostics for the evaluation of medical texts from medical histories in natural language. We also developed an interface for receiving, processing, segmenting, and transmitting microphotographs to artificial neural networks and an interface for the primary accepting and processing of microphotographs based on the OMERO medical image processing platform. To send data online, a demo script for jupiter was prepared. An interface for transmitting medical texts to the medical text semantics recognition system was also developed. The IBM Watson Annotator for Clinical Data was used to perform preliminary analysis of medical texts. We created a database of medical images of the bone marrow for neural network training. We made 3,500 color microphotographs (600 × 400 pixels) of bone marrow smears at a resolution of × 600 (light microscopy; hematoxylin and eosin staining). We performed marking of 11 types of bone marrow cells. We created a database of medical texts (167 patients, 40,000 words) to prepare a neural network. The database was stripped of all personal identifiers.

Stair region detection and recognition from a stair candidate image is a challenging work in the computer vision research area. In the last few decades, researchers use many recognition systems to recognize and verify the stair region from other analogous objects. However, all the verification systems such as vanishing point (VP) do not achieve the desired result for various reasons. In this regard, a method is proposed in this paper to investigate the vanishing point’s problem arising in the case of stair region verification based on the three basic criteria, i.e. focal angle of the camera, height of the camera from the ground, and distance of the camera from the stair image. For that, primarily, the stair region is extracted by utilizing the geometrical features of a stair. The detected stair candidate region is verified through the y coordinate value of the vertical VP, i.e. y y coordinate value of VP does not verify the stair region from all the scenarios. This paper investigates and justifies this problem utilizing the experimental analysis and introduces a mathematical model to estimate the location of the VP of the stair region. Finally, support vector machine (SVM) classifier is utilized instead of VP to recognize the stair candidate region and the performance of SVM is compared with respect to the VP. For that, rotational invariant uniform local binary pattern (LBP) is used for feature extraction. Stair images captured under different orientation and illumination conditions have been used to test the proposed method to evaluate the resultant accuracy.

Aerial image detection aims to find objects of interest and give their locations, and the goal of aerial image recognition is to classify the scenes or objects. Deep neural network is by far the best model for image detection and recognition. But still it fails to meet the requirements of high precision and low missing alarm. To solve this issue, we propose a two-step system including detection and fine-grained recognition modules. The first module is based on one-stage detection method YOLOv3. Furthermore, the metric-learning-based magnet loss is introduced to realize fine-grain recognition in the second step. The experiments prove the effectiveness of their combinations on improving precision and reducing missing alarm rate.

This paper presents a new recognition system for shockable arrhythmias for patients suffering from sudden cardiac arrest. In order to develop the recognition system, lots of electrocardiogram (ECGs) have been analyzed by using gabor wavelet transform (GWT). Although, there is a huge number of spectrum feature parameters, recognition performance for all combinations for spectrum feature parameters are evaluated, and on the basis of the evaluation results, useful and effective spectrum features for ECGs are extracted. As a result, the proposed recognition system based on the selected effective spectrum feature parameters can achieved good performance comparing with the existing results.

A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

This paper describes the algorithm used for personal identification based on features extracted from the palmprint. The local Gabor XOR (LGXP) features is built using Gabor filter with orientation. Initially, the palm print images are preprocessed using median filter. The algorithm is then modified, where features are extracted with different orientations of the Gabor filter called the multiple orientation LGXP (MOLGXP) features. The PCA feature is extracted and fused with MOLGXP and PCA using sum rule.

Automatic Number Plate Recognition (ANPR) is a specialized type of Optical Character Recognition System (OCR). It is a method of reading a vehicle's license plate using OCR to create vehicle registry or location data. ANPR is utilized by a variety of agencies around the world to enforce the law, including determining whether a vehicle is registered or not. Government entities, such as highways agencies, can categorize traffic movements for computerized toll collection. Images of the text from the license plate can be stored and processed using the ANPR system. Infrared cameras are often employed to take photographs in any lighting condition, whether it is day or night. To be more accurate ANPR technology should also consider plate variations from place to place.

Many sensing gesture recognition systems based on Wi-Fi signals are introduced because of the commercial off-the-shelf Wi-Fi devices without any need for additional equipment. In this paper, a deep learning-based sign language recognition system is proposed. Wi-Fi CSI amplitude and phase information is used as input to the proposed model. The proposed model uses three types of deep learning: CNN, LSTM, and ABLSTM with a complete study of the impact of optimizers, the use of amplitude and phase of CSI, and preprocessing phase. Accuracy, F-score, Precision, and recall are used as performance metrics to evaluate the proposed model. The proposed model achieves 99.855%, 99.674%, 99.734%, and 93.84% average recognition accuracy for the lab, home, lab + home, and 5 different users in a lab environment, respectively. Experimental results show that the proposed model can effectively detect sign gestures in complex environments compared with some deep learning recognition models.

The pattern recognition of a forest surface from remote sensing data (both airborne hyperspectral data and WorldView-2 multichannel satellite data of high spatial resolution) is investigated. Calculations have been performed using both the method developed earlier by the authors and the standard statistical approach. For three fragments of the forest surface, the range of changes in the accuracy of recognition calculations has been estimated for both airborne and satellite data, depending on the use of different databases developed for the recognition system. Some features of the pattern recognition of the underlying surface are discussed on the basis of both hyperspectral airborne data and multichannel satellite data of high spatial resolution.

Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the Gray Wolf Optimization algorithm (GWO) and Artificial Bee Colony algorithm (ABC), which can effectively recognize Hancitor in networks.

Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.