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Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

• An automatic method for pavement patch detection is proposed. • Image processing is used to compute pavement image texture. • Least Squares Support Vector Machine is used for data classification. • Differential Flower Pollination is employed for model optimization. • The new model can achieve a good prediction result (CAR = 95%). Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C#.NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

Tomato disease image recognition plays a crucial role in agricultural production. Today, whilemachine vision methods based on deep learning have achieved some success indiseaserecognition, theystill faceseveralchallenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanismcalled DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust tonoisylabels, aimedat reducing the impact of noisy labels on the LDAM Net model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios. [ABSTRACT FROM AUTHOR]

The endmember spectrum method can improve image classification quality based on the spectral features of pure pixels in remote sensing images. The CART (Classification and Regression Tree) is a powerful machine learning algorithm that can also be used for remote sensing image classification. In this study, the Tamarix chinensis forest in the Changyi National Marine Ecological Special Reserve in Shandong Province was taken as the research object, and the endmember spectrum method and the CART decision tree method were used to compare and analyze the results of land-use/cover-type classification extraction. In the extraction process, the land use/cover types of the Tamarix forest in the study area were first divided into forested land types such as high-density forest land, medium-density forest land, and low-density forest land, as well as non-forested land types such as water bodies, roads, dams, buildings, and bare soil. Through analysis, the following conclusions could be drawn: while the overall forest cover of the Tamarix forest is high, there is still some room for further afforestation and ecological restoration in the protection area; from the results of land-use/cover extraction results based on the endmember spectrum method in the study area, it can be seen that this method has better results when extracting well-grown forested land, such as high-density Tamarix chinensis forests and medium-density Tamarix chinensis forests, and poorer results when extracting non-forested land, such as low-density tamarisk forests, roads, buildings, dams, and water bodies; from the results of land use/cover extraction based on a CART decision tree in the study area, it can be seen that this method is more effective when extracting non-forested land, such as roads, buildings, dams, and water bodies, but less effective when extracting forested land, such as high-density Tamarix chinensis forests, medium-density Tamarix chinensis forests, and low-density Tamarix chinensis forests. The relevant research results and conclusions of this study can provide some reference for the classification and extraction of large-scale shrub forest cover types based on remote sensing images. [ABSTRACT FROM AUTHOR]

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model's outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process. [ABSTRACT FROM AUTHOR]

Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions. [ABSTRACT FROM AUTHOR]

Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be taught with limited computer resources. Three types of training bases were investigated, starting with a simple base verifying five colours, then recognizing two different orthogonal elements, followed by more complex images from different families. This research aimed to demonstrate the capabilities of the models based on training base parameters such as the number of images and epoch types. Architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of the different models based on the TensorFlow and Keras libraries in the Jupiter environment based on the Python programming language. An artificial intelligence model with a combination of MobileNet, proposed by Siemens, and Efficient and Inception, selected by the authors, allows for further work to be conducted on image classification, but with limited computer resources for industrial implementation on a programmable logical controller (PLC). The study showed a 90% success rate, with a learning time of 180 s. [ABSTRACT FROM AUTHOR]

The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874. [ABSTRACT FROM AUTHOR]

In today's interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software. [ABSTRACT FROM AUTHOR]

Breast cancer has become the most prominent type of cancer in the world. Early detection of breast cancer plays an important role in optimal treatment planning to decrease mortality. Breast ultrasound is widely used in diagnosing breast masses. Applications of machine learning in ultrasound imaging‐based classification have shown promising potential for early and accurate detection of breast cancer. In this study, a new computer‐aided diagnosis system based on machine learning techniques for breast cancer classification is proposed. Feature space is extended by using hybrid feature representations that combine both global and local texture statistics. A two‐step feature selection process is implemented using Boruta all‐relevant feature selection algorithm and iterative correlation analysis. A grid‐search strategy is followed along with 20 times repeated 10‐fold randomly stratified cross‐validation to optimize machine learning algorithms. Fourteen classification models based on random forest (RF) and support vector machine trained using all combinations of global features and the features driven from gray‐level co‐occurrence matrix and local binary patterns are tested. The experiments showed that the RF classifier on the hybrid feature vector that combines all global and local features achieved the best classification performance with average accuracy and area under the curve of 97.81% and 99.80%, respectively. The results suggest that the proposed system efficiently improves the classification performance of breast lesions on ultrasound images and can assist clinical decision‐making. [ABSTRACT FROM AUTHOR]

Damage to reinforced concrete (RC) facilities occurs through the process of natural deterioration. Machine learning can be employed to effectively identify various damage areas and ensure safety. The performance of machine vision methods depends on image quality. In this study, five image types (Types I–V) with combinations of image deficiencies pertaining to uniform illuminance, uneven illuminance, orthoimage, tilt angle, and image blur were used to evaluate the damage recognition capabilities of maximum likelihood (MLH), support vector machine (SVM), and random forest (RF) methods. Type I images were orthoimages with uniform illuminance, Type II images were tilted images with uniform illuminance, Type III images were orthoimages with uneven illuminance, Type IV images were tilted images with uneven illuminance, and Type V images were tilted, blurred images with uneven illuminance. MLH was most accurate (98.6%) in Type I images, and RF was the least accurate (62.8%) in Type V images. Image tilt (in Type II images) did not diminish the damage recognition capabilities of the three types of machine learning methods (mean accuracy = 97.2%). For tilted images with uneven illuminance (Type IV), a severe expansion effect was produced, reducing the mean accuracy to 70.1%. Type III images were recognized with a mean accuracy of 87.1%; uneven illuminance increased the error rate for three classes of damage. By testing various image types, the impact of image quality on the variability of machine learning recognition is understood, and the ability of automated machine learning recognition in the future is improved. [ABSTRACT FROM AUTHOR]

Motivated by researching new image texture modeling that improves state-of-the-art LBP variants and non-LBP descriptors, this paper proposes a novel approach for constructing local image descriptors, which are suitable for histogram based image representation. Instead of heuristic code constructions, the proposed approach is based on local concave-and-convex characteristics, which have high ability to extract discriminative and stable texture representation. Different from the majority of descriptors that only encode relationships between the pixels in doublets around central pixel (within 3  ×  3 neighborhood), the proposed approach encodes relationships between the pixels in triplets by including the central pixel in the modeling. We build two distinct descriptors by dividing local features into two distinct groups, i.e., local concave and convex microstructure patterns (LCvMSP and LCxMSP), according to relationships between the pixels inside the triplets, formed along closed path around the central pixel of a 3 × 3-grayscale image patch. To make the descriptors more insensitive to noise and invariant to monotonic gray scale transformation, two supplementary triplets are added in the modeling. These triplets are formed using the central pixel and four virtual pixels set to the median of the grey-scale values of the 3 × 3 neighbourhood and the whole image and the average local and global gray levels respectively. The histograms obtained from the single scale descriptors LCvMSP and LCxMSP are concatenated together to build multi-scale histogram feature vector referred to as local concave-and-convex micro-structure pattern (LCCMSP), that is expected to better represent salient local texture structure. We evaluated the effectiveness of the proposed methods on thirteen challenging representative widely-used texture datasets, and found that the proposed LCvMSP, LCxMSP and LCCMSP operators achieve performances that are competitive or better than a large number of recent most promising state-of- the-art LBP variants and non-LBP descriptors. Statistical comparison based on Wilcoxon signed rank test demonstrated that the proposed methods are the top three over all the tested datasets. [ABSTRACT FROM AUTHOR]

The advent in graphic rendering software and technological progress in hardware can generate or modify photorealistic computer graphic (CG) images that are difficult to identify by human observers. Computer-generated images are used in magazines, film and advertisement industry, medical and insurance agencies, social media, and law agencies as an information carrier. The forged computer-generated image created by the malicious user may distort social stability and impacts on public opinion. Hence, the precise identification of computer graphic and photographic image (PG) is a significant and challenging task. In the last two decades, several researchers have proposed different algorithms with impressive accuracy rate, including a recent addition of deep learning methods. This comprehensive survey presents techniques dealing with CG and PG image classification using machine learning and deep learning. In the beginning, broad classification of all the methods in to five categories is discussed in addition to generalized framework of CG detection. Subsequently, all the significant works are surveyed and are grouped into five types: image statistics methods, acquisition device properties-based techniques, color, texture, and geometry-based methods, hybrid methods, and deep learning methods. The advantages and limitations of CG detection methods are also presented. Finally, major challenges and future trends in the CG and PG image identification field are discussed. [ABSTRACT FROM AUTHOR]

Medical image categorization is essential for a variety of medical assessments and education functions. The purpose of medical image classification is to organize medical images into useful categories for the purpose of illness diagnosis or study, making it one of the most pressing issues in the field of image recognition. On the other hand, traditional methods have plateaued in their effectiveness. Additionally, a substantial amount of time and energy is required when employing them to extract and choose categorization features. Alzheimer's disease is one of the most frequent sources of dementia in elderly patients. Metabolic diseases affect a huge population worldwide, and henceforth, there is a vast scope of applying machine learning to find treatments to these diseases. As a relatively new machine learning technique, deep neural networks have shown great promise for a variety of categorization problems. In this research, a model for diagnosing and tracking the development of Alzheimer's disease that is both accurate and easy to understand has been developed. By following the developed procedure, medical professionals may make deliberations with solid justification. Early diagnosis utilizing these machine learning algorithms has the potential to minimize mortality rates associated with Alzheimer's disease. This research work has developed a convolutional neural network using a shallow convolution layer to identify Alzheimer's disease in medical image patches. The total accuracy of proposed classifications is around 98%, which is greater than the accuracy of the most popular existing approaches. [ABSTRACT FROM AUTHOR]

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta. [ABSTRACT FROM AUTHOR]

Modern earth observation sensors have revolutionized the remote sensing community by improving remote sensing image quality. However, Pixel-based image analysis methods have challenges in handling very high-resolution (VHR) imagery. Geographic Based Image Analysis (GEOBIA) yielded promising results, but it is not inflexible in capturing domain experts' expressions, therefore geographic information system professionals shifted to ontologies for remote sensing science. This paper advocates for the adoption of knowledge representation using ontologies in remote sensing. To this end, a survey of GEOBIA studies for image analysis and classification is presented, and the limitations of existing methods in reaching the remote sensing expert-level expectation are clarified. New GEOBIA development techniques as well as opportunities for improving GEOBIA models have been looked into. Recent studies that adopted ontologies in forest image classification are analyzed and recommendations for the remote sensing science community are provided, to highlight the advantages of ontologies in interpreting satellite images. [ABSTRACT FROM AUTHOR]

In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition. [ABSTRACT FROM AUTHOR]

Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent. [ABSTRACT FROM AUTHOR]

Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

Convolutional neural networks also encountered some problems in the development of image recognition. The most prominent problem is that it is costly and time-consuming to collect data sets and train models. Limited data sets will cause the trained models to overfit. This paper proposes two methods to reduce overfitting based on the residual neural network architecture. The first type of method proposes a method of cross-combining waivers, reducing the size of the convolution kernel, and reducing the number of convolution kernels. The fitting method uses cross-combination to make the accuracy of Kaggle cat and dog data on the validation data set reach 95.37% and 90.31% on 30 types of engineering practice verification data set. The second method is based on the finetune residual neural network. A method of recurrent finetune residual neural network is proposed to improve the accuracy of the model. The accuracy of the finetune residual neural network on the Kaggle cat and dog validation dataset is 99.37%, and the accuracy of the dataset is verified in 30 types of engineering practice. The accuracy is 99.30%. The residual neural network method achieves 99.68% accuracy in the Kaggle cat and dog validation dataset and 99.61% in the validation dataset for 30 types of engineering practice. [ABSTRACT FROM AUTHOR]

The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly picked from the patients' images as the independent test set to test its searching ability on suspicious tumor regions. Furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. Efficient-Det has demonstrated remarkable progress in general image recognition tasks with specific advantages of locating and identifying tumor areas simultaneously. Compared to the manual method (mean accuracy: 95.3% and 60 s per image) and traditional feature engineering method (mean accuracy: 90% and 15 s per image), our Efficient-Det has the capability of providing a competitive (mean accuracy: 92.6%) and fast (0.06 s per image) classification result. The deployment of Efficient-Det in our local breast cancer discrimination task exhibits specific applicability within real clinical workflows. [ABSTRACT FROM AUTHOR]

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition. [ABSTRACT FROM AUTHOR]

Recognition of small moving targets in space has become one of the frontier scientific researches in recent decade. Most of them focus on detection and recognition in star image with sidereal stare mode. However, in this research field, few researches are about detection and recognition in star image with track rate mode. In this paper, a novel approach is proposed to recognize the moving target in single frame by machine learning method based on elliptical characteristic extraction of star points. The technical path about recognition of moving target in space is redesigned instead of traditional processing approaches. Elliptical characteristics of each star point can be successfully extracted from single image. Machine learning can achieve the classification goal in order to make sure that all moving targets can be extracted. The experiments show that our proposed approach can have better performance in star images with different qualities. [ABSTRACT FROM AUTHOR]

Fatigue crack initiation sites (FCISs) are crucial for fatigue investigations in metallic compounds, since they are closely related to the fatigue failure mechanisms and inform methods against crack initiation. However, the identification of FCISs requires professional knowledge and can be demanding and time consuming. Computer vision techniques show excellent performance for image texture recognition tasks and have been applied to some material domains. In order to explore more possibilities and widen the applications of computer vision techniques on material domains, as well as reduce the labor and knowledge requirements for FCISs recognition tasks, a state-of-the-art object detector – deeply supervised object detector which can train models from scratch, was introduced. The models trained from limited data show reasonable capacity for recognizing FCISs. Results demonstrate that increasing training dataset size can improve the accuracies of the models, while raising the number of epochs can result in a superior ability to recognize delicate features. Moreover, most of the images that cannot be recognized for FCISs possess common characteristics, such as, poor image quality, unclear features, insufficient training data and etc. Based on the above findings, several proposals are made for the future work to improve the performance of the same or relevant studies, as a reference. [ABSTRACT FROM AUTHOR]

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method. [ABSTRACT FROM AUTHOR]

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.5.053024] [ABSTRACT FROM AUTHOR]

Currently, large-scale spraying of herbicides pollutes the environment, destroys the quality of cultivated land, and increases the cost of agricultural production. For this reason, "precision farming," in which machine learning technology is used to detect weeds in crops and soil and then herbicides are sprayed at specific locations according to the distribution of weeds, is proposed. Machine learning is a multidisciplinary interdisciplinary major, covering knowledge of probability theory, statistics, approximate theory and complex algorithms. Machine learning uses computers as tools and is committed to simulating human learning methods in real time, and divides existing content into knowledge structures to effectively improve learning efficiency. We aim to use machine learning technology to study weed detection algorithms and provide feasible solutions for accurate weed classification and variable spraying of herbicides. Based on the research of four kinds of germinated corn and wheat weeds by Chinese and foreign experts, we use machine learning technology as the method of extracting and recognizing the combination of color features, shape features, and texture features. Plant development and software are used to process weeds to classify and identify wheat and corn. To better describe the gray distribution of each pixel and neighboring pixels, a more effective weed image classification function is required. The experimental results of this paper show that the machine learning-based weed image recognition classifier can describe the grayscale distribution of each pixel and adjacent pixels well, and it is beneficial to identify the characteristics of weeds, in which the correct rate of weed recognition exceeds 97.5%. [ABSTRACT FROM AUTHOR]

Recent trends in 3D scanning are aimed at the fusion of range data and color information from images. The combination of these two outputs allows to extract novel semantic information. The workflow presented in this paper allows to detect objects, such as light switches, that are hard to identify from range data only. In order to detect these elements, we developed a method that utilizes range data and color information from high-resolution panoramic images of indoor scenes, taken at the scanners position. A proxy geometry is derived from the point clouds; orthographic views of the scene are automatically identified from the geometry and an image per view is created via projection. We combine methods of computer vision to train a classifier to detect the objects of interest from these orthographic views. Furthermore, these views can be used for automatic texturing of the proxy geometry. [ABSTRACT FROM AUTHOR]

In practical application, facial image recognition is vulnerable to be attacked by photos, videos, etc., while some currently used artificial feature extractors in machine learning, such as activity detection, texture descriptors, and distortion detection, are insufficient due to their weak detection ability in feature extraction from unknown attack. In order to deal with the aforementioned deficiency and improve the network security, this paper proposes directional difference convolution for the deep learning in gradient image information extraction, which analyzes pixel correlation within the convolution domain and calculates pixel gradients through difference calculation. Its combination with traditional convolution can be optimized by a parameter θ. Its stronger ability in gradient extraction improves the learning and predicting ability of the network, whose performance testing on CASIA-MFSD, Replay-Attack, and MSU-MFSD for face anti-spoofing task shows that our method outperforms the current related methods. [ABSTRACT FROM AUTHOR]

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%. [ABSTRACT FROM AUTHOR]