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In the paper, we present a new transfer subspace learning algorithm termed discriminative transfer regression (DTR) for cross-domain image recognition, in which low-rank representation (LRR), discriminative regression, local geometry preserving, and different norm minimization are integrated in to a united framework for transfer learning. Firstly, both the global structure and the local geometry information of the original data are preserved by imposing low-rank and sparse constraints on the reconstruction coefficient matrix. Secondly, DTR algorithm can overcome the disturbance of outliers and noises. Thirdly, the local manifold structure of the observed samples with the same semantics from the source and target domains is captured by the adaptive weight graph. Fourthly, the discriminative information of the samples from the source domain is encoded to the target domain based on ridge regression (RR). Meanwhile, the small-class problem confronted by RR and its extensions, that the obtained projection matrix is limited by the number of classes, can be effectively solved. In addition, the convergence and computational complexity of DTR algorithm is analyzed. An extensive range of experiments on several cross-domain image databases demonstrate the superiority of the DTR algorithm. [ABSTRACT FROM AUTHOR]

This paper for the first time treats the interpretation of electrochemical noise time-frequency spectra as an image classification problem. It investigates the application of a convolutional neural network (CNN) for deep learning image classification of electrochemical noise time-frequency transient information. Representative slices of these spectra were selected by our transient analysis technique and served as input images for the CNN. Corrosion data from two types of pitting corrosion processes serve as test cases: AISI304 and AA2024-T3 immersed in a 0.01M HCl and 0.1M NaCl solution between 0 and 1ks after immersion, respectively. Continuous wavelet transform (CWT) spectra and modulus maxima (MM) are used to train the CNN, either individually or in a combined form. The classification accuracy of the CNN trained with the combined dataset is 0.97 and with the two individual datasets 0.72 (only CWT spectrum) and 0.84 (only MM). The ability to additionally classify a more progressed form of pitting corrosion of AA2024-T3 between 9 and 10ks after immersion indicates that the proposed method is sufficiently robust using combined datasets with CWT spectra and MM. The pitting processes can effectively be detected and classified by the proposed method. The most important contribution of the present work is to introduce a novel procedure that decreases the classical need for large amounts of raw data for training and validation purposes, while still achieving a satisfactory classification robustness. A relatively small number of individual signals thereby generates a multitude of input images that still contain all relevant kinetic information about the underlying chemo-physical process. [Display omitted] • Electrochemical noise interpretation is treated as an image classification problem. • Continuous wavelet transform spectra are divided into single transient spectra. • A small number of individual signals generates a multitude of input images. • Amount of raw data needed for training the convolutional neural network is reduced. • Modulus maxima increase the classification accuracy of pitting corrosion. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods require the assumption that training and test data are drawn from the same distribution, which proves challenging in real-world applications. Moreover, deep learning models require a substantial amount of labeled data for training in classification tasks and limited samples may lead to overfitting. In many real-world scenarios, there is an insufficient supply of labeled samples within the target domain for learning. Transfer learning offers an effective solution, allowing knowledge from a source domain to be transferred to a target domain. Additionally, data augmentation enhances model generalization by increasing data samples, particularly beneficial when dealing with limited target domain data. In this paper, we synergistically enhance the model's performance on classification tasks by integrating transfer learning techniques with a data augmentation strategy. By conducting numerous experiments across various datasets, we verified the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Detecting objects in hospital indoor environments is critical for scene understanding and can have various applications in healthcare. Deep learning algorithms have proven to be effective in object recognition from images or videos, but the availability of annotated datasets plays a crucial role in their successful application. However, there is a shortage of datasets for object detection in hospital settings, hindering the advancement of hospital indoor object detection algorithms. In this paper, we present the Hospital Indoor Object Detection (HIOD) dataset, consisting of 4,417 images covering 56 object categories. The HIOD dataset represents the frequently encountered objects in hospitals and comprises 51,869 annotated objects. The dataset is characterized by dense annotation, with an average of 11.7 objects and 6.8 object categories per image. An object detection benchmark was established using the HIOD dataset and eight state-of-the-art object detectors. The benchmark provides a comprehensive evaluation of the performance of the selected object detectors on a large and diverse set of images of objects commonly seen in hospital environments. The results of the benchmark can be used to compare and analyze the performance of different object detectors and identify their strengths and weaknesses for use in hospital environments. In the benchmark, one-stage detectors have shown superior performance compared to two-stage detectors of similar parameter sizes. In particular, YOLOv6-L was able to attain a mean Average Precision (mAP) of 51.7% while operating at a detection speed of 255 FPS. The benchmark and dataset can serve as a valuable resource for researchers and practitioners in the field of computer vision and robotics, helping to advance the development of more effective and efficient object detection algorithms for developing automated operations in hospitals such as robotic disinfection and patient assistance. • A new object detection dataset for artificial intelligence applications in hospitals. • A benchmark to compare and analyze the performance of different object detectors. • Improve object detection via hyperparameter optimization and transfer learning. • Evaluate the effects of image size and backbone on object detection performance. [ABSTRACT FROM AUTHOR]

Identification of exfoliated graphene flakes and classification of the thickness are important in the nanomanufacturing of advanced materials and devices. This paper presents a deep learning method to automatically identify and classify exfoliated graphene flakes on Si/SiO 2 substrates from optical microscope images. The presented framework uses a hierarchical deep convolutional neural network that is capable of learning new images while preserving the knowledge from previous images. The deep learning model was trained and used to classify exfoliated graphene flakes into monolayer, bi-layer, tri-layer, four-to-six-layer, seven-to-ten-layer, and bulk categories. Compared with existing machine learning methods, the presented method showed high accuracy and efficiency as well as robustness to the background and resolution of images. The results indicated that the pixel-wise accuracy of the trained deep learning model was 99% in identifying and classifying exfoliated graphene flakes. This research will facilitate scaled-up manufacturing and characterization of graphene for advanced materials and devices. • A machine intelligence approach is developed to classify exfoliated graphene flakes. • A hierarchical deep convolutional neural network is applied for the classification task. • The neural network can learn from new images while preserving the learned knowledge. • The approach is robust to the background and resolution of microscopy images. • Advanced vision techniques are incorporated to handle highly imbalanced datasets. [ABSTRACT FROM AUTHOR]

In this work ear recognition of a moving person with the help of a single fixed-in-position video camera is investigated, a novel problem undertaken to the best of our knowledge and belief. The challenges associated with this work are that during data capturing process each moving subject appears in unconstrained views together with random noise, motion blur and different level of illumination. Secondly, the collection of large number of redundant samples for each subject makes the training database bulky, which eventually increases classification time. So, in this paper, we introduce a novel, reduced time, metaface dictionary learning approach which by employing Frobenius norm based regularization reduces redundancy of training database with much lesser time as compared to other available database learning methods. In general, ear samples with random poses, severe motion blur and different levels of illumination loose significant class specific information and hence inflict severe nonlinearity to the system. The simple and well accounted solution for the above problem is kernel framework which makes samples of different classes linearly separable by elevating them to higher dimensions. In our proposed solution, we have used novel l 2 -norm regularized affine hull based kernel collaborative representation based classification scheme, which represents each query set as an affine hull and then collaboratively represents this hull over the linear span of gallery sets of all classes in the high dimensional space. Finally, the query set is assigned to that particular class which gives least representation or residual error among all the available classes. Results of extensive experimentations carried out over an indigenously developed database in our laboratory (named ERVIDJU) aptly demonstrate that our proposed method is a valid biometric identifier and it can produce superior performance compared to several other contemporary algorithms, developed for similar purposes. [ABSTRACT FROM AUTHOR]

This paper presents a visual object tracking algorithm using an eigenspace representation. Previous approaches to eigenspace methods for object tracking use vectorized image regions as observations. Here, feature vectors associated to pixels of the target template are considered to be individual observations of the target object. The collection of observations is learned using non-linear subspace projection to arrive at an eigenspace representation. This representation allows tracking pixel-wise but the pixels are tied together using subspace representation which provides a robust and compact representation of the object. Localization and segmentation are carried out by deriving a similarity function in the eigenspace representation. A gradient descent and mean-shift based techniques are derived to maximize the similarity function with respect to the transformation parameters. The de-noising and clustering capabilities of the eigenspace representation lead to a localization procedure that is robust to noise and partial occlusion. For fast moving objects and to recover from total occlusion, a probabilistic search strategy, based on particle filter, is also developed. A unique feature of our approach is that it permits segmentation in addition to localization when multiple templates of the target are given. The performance of the algorithm is tested on real world tracking examples. [ABSTRACT FROM AUTHOR]

In this paper a multi-modal unsupervised Deep Learning based algorithm for fault detection is proposed. Such method is applied to real data from a testing procedure implemented on an industrial production line. Both thermal images and current and power measurements coming from industrial refrigerators are collected. The considered dataset is highly unbalanced with the vast majority of samples being healthy. Thermal images are processed via a Deep Convolutional neural network. The features extracted from the thermal images are thus merged to structured data of power, current and temperature. Therefore, a Deep Auto-Encoder is trained on the dataset to signal anomalies corresponding to faults in the refrigerators. Three different methods are trained and compared: (1) an automatic method in which an expert extracts relevant features from thermal images without using the image recognition module; (2) a semi-automatic method where the convolutional neural network is applied to regions of interest within the thermal images selected by an expert operator; (3) a fully automatic method in which the Deep convolutional network processes the whole thermal image without any human intervention. The three methods show comparable results with nevertheless slight differences. [ABSTRACT FROM AUTHOR]

In many real-world applications of Machine Learning it is of paramount importance not only to provide accurate predictions, but also to ensure certain levels of robustness. Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points. Unfortunately, one of the main issues in adversarial training is that robustness w.r.t. gradient-based attackers is always achieved at the cost of prediction accuracy. In this paper, a new algorithm, called Wasserstein Projected Gradient Descent (WPGD), for adversarial training is proposed. WPGD provides a simple way to obtain cost-sensitive robustness, resulting in a finer control of the robustness-accuracy trade-off. Moreover, WPGD solves an optimal transport problem on the output space of the network and it can efficiently discover directions where robustness is required, allowing to control the directional trade-off between accuracy and robustness. The proposed WPGD is validated in this work on image recognition tasks with different benchmark datasets and architectures. Moreover, real world-like datasets are often imbalanced: this paper shows that when dealing with such type of datasets, the performance of adversarial training are mainly affected in term of standard accuracy. [ABSTRACT FROM AUTHOR]