Set Of Salient Features Research Articles

Dual-mode object detection algorithm based on feature enhancement and feature fusion

Abstract In complex conditions, visible light cannot meet the challenge of high-precision target detection requirements, so we propose a dual-modal target detection algorithm, named Dual-Mode Object Detection Based on Feature Enhancement and Feature Fusion (DEF). The algorithm in this paper is improved based on YOLOv5. First, we build a dual-branch feature extraction network for visible light and infrared images, separately extracting features of the images. Subsequently, the extracted feature maps are input separately into a feature enhancement module, which is constituted by a multi-branch convolutional architecture and the ECA (Efficient Channel Attention) mechanism, to acquire an augmented set of salient features. Finally, a module for feature fusion is devised to merge the modality-specific features that are extracted by the two branches. The integrated feature maps are then sent into the subsequent module to achieve dual-mode image target detection. The effectiveness of the proposed algorithm is confirmed through a series of experiments conducted using the FLIR and LLVIP datasets, showcasing its superior performance in practical applications.

Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM.

Object-level simultaneous localization and mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is Gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an inertial measurement unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object's surface and not the object's shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-Blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.

ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework.

An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) without manual intervention. The present study emphasises utilizing structural MRI and personal characteristic (PC) data for developing an automated diagnostic system for ADHD classification. Here, an age-balanced dataset of 316 ADHD and 316 Typically Developing Children (TDC) was prepared from the publicly available dataset. We extracted volumetric features from gray matter (GM) volumes from brain regions defined by Automated Anatomical Labelling (AAL3) atlas and cortical thickness-based (CT) features using the Destrieux atlas. A set of salient features were selected independently using minimum redundancy and maximum relevance (mRMR) and ensemble feature selection (EFS) methods. Decision models were trained using five well-known classifiers: K-nearest neighbours, logistic regression, linear Support Vector Machine (SVM), radial-based SVM (RBSVM), and Random Forest. The performance of the proposed system was evaluated using accuracy, recall, and specificity with ten runs of a ten-fold cross-validation scheme. We run seven experiments by considering different combinations of features. The maximum classification accuracy of 75% was obtained with CT and PC features with RBSVM and SVM with the EFS. An increase in GM volume in fifteen brain regions and loss of cortical thickness in twenty-seven brain regions were observed.

Flow guided mutual attention for person re-identification

Person Re-Identification (ReID) is a challenging problem in many video analytics and surveillance applications, where a person's identity must be associated across a distributed non-overlapping network of cameras. Video-based person ReID has recently gained much interest given the potential for capturing discriminant spatio-temporal information from video clips that is unavailable for image-based ReID. Despite recent advances, deep learning (DL) models for video ReID often fail to leverage this information to improve the robustness of feature representations. In this paper, the motion pattern of a person is explored as an additional cue for ReID. In particular, a flow-guided Mutual Attention network is proposed for fusion of bounding box and optical flow sequences over tracklets using any 2D-CNN backbone, allowing to encode temporal information along with spatial appearance information. Our Mutual Attention network relies on the joint spatial attention between image and optical flow feature maps to activate a common set of salient features. In addition to flow-guided attention, we introduce a method to aggregate features from longer input streams for better video sequence-level representation. Our extensive experiments on three challenging video ReID datasets indicate that using the proposed approach allows to improve recognition accuracy considerably with respect to conventional gated-attention networks, and state-of-the-art methods for video-based person ReID.

Stimulating Consumption at Low Budget – Evidence from a Large-Scale Policy Experiment Amid the COVID-19 Pandemic

We use a novel panel with detailed transaction records of more than one million de-identified individuals to study the effect of a large-scale Chinese government-issued digital coupon program on consumer spending. At the core of this stimulus program are a set of salient features that drive the marginal propensity to consume (MPC) to the range of 3.4 to 5.8, an order of magnitude larger than those in previous studies. Testing between different models of consumer behaviors, we find that a behavioral model with mental accounting and loss framing can match the empirical evidence from the field. Our analysis, by illustrating the importance of embedding behavioral factors into the design and implementation of public policy, inform the current debate about cost effective policy tools to recover the economy.

Activity recognition with cooperative radar systems at C and K band

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

A Super Descriptor Tensor Decomposition for Dynamic Scene Recognition

This paper presents a new approach for dynamic scene recognition based on a super descriptor tensor decomposition. Recently, local feature extraction based on dense trajectories has been used for modeling motion. However, dense trajectories usually include a large number of unnecessary trajectories, which increase noise, add complexity, and limit the recognition accuracy. Another problem is that the traditional bag-of-words techniques encode and concatenate the local features extracted from multiple descriptors to form a single large vector for classification. This concatenation not only destroys the spatio-temporal structure among the features but also yields high dimensionality. To address these problems, first, we propose to refine the dense trajectories by selecting only salient trajectories in a region of interest containing motion. Visual descriptors consisting of oriented gradient and motion boundary histograms are then computed along the refined dense trajectories. In case of camera motion, a short-window video stabilization is integrated to compensate for global motion. Second, the extracted features from multiple descriptors are encoded using a super descriptor tensor model. To this end, the TUCKER-3 tensor decomposition is employed to obtain a compact set of salient features, followed by feature selection via Fisher ranking. Experiments are conducted using two benchmark dynamic scene recognition datasets: Maryland “in-the-wild” and YUPPEN dynamic scenes. Experimental results show that the proposed approach outperforms several existing methods in terms of recognition accuracy and achieves a performance comparable with the state-of-the-art deep learning methods. The proposed approach achieves classification rates of 89.2% for Maryland and 98.1% for YUPPEN datasets.

Fueling Prediction of Player Decisions: Foundations of Feature Engineering for Optimized Behavior Modeling in Serious Games

As a digital learning medium, serious games can be powerful, immersive educational vehicles and provide large data streams for understanding player behavior. Educational data mining and learning analytics can effectively leverage big data in this context to heighten insight into student trajectories and behavior profiles. In application of these methods, distilling event-stream data down to a set of salient features for analysis (i.e. feature engineering) is a vital element of robust modeling. This paper presents a process for systematic game-based feature engineering to optimize insight into player behavior: the IDEFA framework (Integrated Design of Event-stream Features for Analysis). IDEFA aligns game design and data collection for high-resolution feature engineering, honed through critical, iterative interplay with analysis. Building on recent research in game-based data mining, we empirically investigate IDEFA application in serious games. Results show that behavioral models which used a full feature set produced more meaningful results than those with no feature engineering, with greater insight into impactful learning interactions, and play trajectories characterizing groups of players. This discovery of emergent player behavior is fueled by the data framework, resultant base data stream, and rigorous feature creation process put forward in IDEFA—integrating iterative design, feature engineering, and analysis for optimal insight into serious play.

MR Image Analytics to Characterize the Upper Airway Structure in Obese Children with Obstructive Sleep Apnea Syndrome.

PurposeQuantitative image analysis in previous research in obstructive sleep apnea syndrome (OSAS) has focused on the upper airway or several objects in its immediate vicinity and measures of object size. In this paper, we take a more general approach of considering all major objects in the upper airway region and measures pertaining to their individual morphological properties, their tissue characteristics revealed by image intensities, and the 3D architecture of the object assembly. We propose a novel methodology to select a small set of salient features from this large collection of measures and demonstrate the ability of these features to discriminate with very high prediction accuracy between obese OSAS and obese non-OSAS groups.Materials and MethodsThirty children were involved in this study with 15 in the obese OSAS group with an apnea-hypopnea index (AHI) = 14.4 ± 10.7) and 15 in the obese non-OSAS group with an AHI = 1.0 ± 1.0 (p<0.001). Subjects were between 8–17 years and underwent T1- and T2-weighted magnetic resonance imaging (MRI) of the upper airway during wakefulness. Fourteen objects in the vicinity of the upper airways were segmented in these images and a total of 159 measurements were derived from each subject image which included object size, surface area, volume, sphericity, standardized T2-weighted image intensity value, and inter-object distances. A small set of discriminating features was identified from this set in several steps. First, a subset of measures that have a low level of correlation among the measures was determined. A heat map visualization technique that allows grouping of parameters based on correlations among them was used for this purpose. Then, through T-tests, another subset of measures which are capable of separating the two groups was identified. The intersection of these subsets yielded the final feature set. The accuracy of these features to perform classification of unseen images into the two patient groups was tested by using logistic regression and multi-fold cross validation.ResultsA set of 16 features identified with low inter-feature correlation (< 0.36) yielded a high classification accuracy of 96% with sensitivity and specificity of 97.8% and 94.4%, respectively. In addition to the previously observed increase in linear size, surface area, and volume of adenoid, tonsils, and fat pad in OSAS, the following new markers have been found. Standardized T2-weighted image intensities differed between the two groups for the entire neck body region, pharynx, and nasopharynx, possibly indicating changes in object tissue characteristics. Fat pad and oropharynx become less round or more complex in shape in OSAS. Fat pad and tongue move closer in OSAS, and so also oropharynx and tonsils and fat pad and tonsils. In contrast, fat pad and oropharynx move farther apart from the skin object.ConclusionsThe study has found several new anatomic bio-markers of OSAS. Changes in standardized T2-weighted image intensities in objects may imply that intrinsic tissue composition undergoes changes in OSAS. The results on inter-object distances imply that treatment methods should respect the relationships that exist among objects and not just their size. The proposed method of analysis may lead to an improved understanding of the mechanisms underlying OSAS.

Organizations and Innovation within the Biotechnology Industry in China: An Interdisciplinary Perspective

This article explores the research patterns and organizational features within R&amp;D sector in China's biotechnology industry, delineating the innovation in knowledge production and industrial development. The more recent development of China's biotechnology industry is briefly overviewed from an interdisciplinary perspective, whilst a set of salient features embodied by social actors are envisaged as have so far strongly shaped the market-based, commercially driven mode of scientific knowledge production in the R&amp;D activities. Furthermore, this mode serves as a premise to the innovation of the interaction-network. The implications derived from this analytical work shed a new light upon policy-making both at the level of S&amp;T governance and in the management practice in China's biotechnology industry.

Signal-to-noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal-to-noise ratio screening method. The application results of the signal-to-noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non-salient input features.

Measuring Saliency of Features Using Signal-to-noise Ratios for Detection of Electrocardiographic Changes in Partial Epileptic Patients

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of two types of electrocardiogram (ECG) beats (normal and partial epilepsy). In order to extract features representing the ECG signals, discrete wavelet transform was used. The PNNs used in the ECG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.

Measuring saliency of features representing EEG signals using signal-to-noise ratios

Various methodologies of automated diagnosis have been adopted, however the entire process can generally be subdivided into a number of disjoint processing modules: pre-processing, feature extraction/selection, and classification. Features are used to represent patterns with minimal loss of important information. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of electroencephalogram (EEG) signals. In order to extract features representing the EEG signals, eigenvector methods were used. The PNNs used in the EEG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the EEG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.

Measuring saliency of features extracted by model-based methods from internal carotid arterial Doppler signals using signal-to-noise ratios

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. The noise in a classification model can be reduced by identifying a set of salient features and then more accurate classification can be obtained. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of internal carotid arterial Doppler signals (ICADS). In order to extract features representing the ICADS, model-based methods were used. The PNNs used in the ICADS classification were trained for the SNR screening method. The application results of the SNR screening method to the ICADS demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and nonsalient input features.

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for Doppler ultrasound signals

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of Doppler signals. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. Ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in classification of the ophthalmic and internal carotid arterial Doppler signals were trained for the SNR screening method. The application results of the SNR screening method to the ophthalmic and internal carotid arterial Doppler signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time-frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.

VideoPlus: a method for capturing the structure and appearance of immersive environments

This paper presents a simple approach to capturing the appearance and structure of immersive scenes based on the imagery acquired with an omnidirectional video camera. The scheme proceeds by combining techniques from structure-from-motion with ideas from image-based rendering. An interactive photogrammetric modeling scheme is used to recover the locations of a set of salient features in the scene (points and lines) from image measurements in a small set of keyframe images. The estimates obtained from this process are then used as a basis for estimating the position and orientation of the camera at every frame in the video clip. By augmenting the video sequence with pose information, we provide the end-user with the ability to index the video sequence spatially as opposed to temporally. This allows the user to explore the immersive scene by interactively selecting the desired viewpoint and viewing direction.

Feature screening using signal-to-noise ratios

We present an approach for identifying important input features in multi-layer perceptron feedforward artificial neural networks (ANN) trained via backpropagation. Specifically, we propose a new saliency measure and demonstrate its use in a methodology that holds the promise of potentially identifying and removing noisy input features in a single training run. First, we propose a signal-to-noise ratio (SNR) saliency measure, which determines the saliency of a feature by comparing it to that of an injected noise feature. Next, we propose a feature screening method that utilizes the SNR saliency measure to select a parsimonious set of salient features. The SNR screening method is demonstrated using Fisher's Iris problem. Confidence in the SNR saliency measure and screening method are bolstered by comparisons to Setiono and Liu's neural network feature selector and a principal component analysis (PCA) approach on three real-world applications: the University of Wisconsin Breast Cancer Diagnosis problem, the US Congressional voting records problem, and the Pima Indians Diabetes problem.

Application of recursive least squares algorithm for learning and mathematical reasoning

In our recent publications, we have illustrated how pattern recognition and adaptive signal-processing techniques can be used to construct a machine learning system (MLS). The MLS is based on the manipulation of raw data to extract a set of salient features which have strong significance in the behaviour of the system or device under observation. This paper illustrates the application of the recursive least squares (RLS) algorithm, which has been used as a learning strategy in the MLS, for feature extraction and mathematical reasoning. Here a data-analysis program is developed, which can be used to explain the relationships between the inputs and the outputs of a system.

Intelligent alignment of waveguide filters using a machine learning approach

The authors investigate the application of a machine learning system to the tuning of waveguide filters. This system uses techniques from pattern recognition and adaptive signal processing. The manual tuning of the waveguide filters is very time consuming and expensive and a skilled operator is required. Here, the machine learning system is adapted in such a way that it can assist an unskilled operator to perform fast and accurate tuning of these filters. The machine learning approach is based on the manipulation of some raw data to extract a set of salient features that have strong significance in the behavior of the filters. These features are derived visually, by comparing the characteristics of a tuned filter to those of a faulty filter with known levels of maladjustments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>