Hybrid features for object detection in RGB-D scenes

<div>Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes.</div>

Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.

Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

Local scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavy losses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task due to the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula for the prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling technique known as gene expression programming (GEP), with data obtained from numerical simulations used to compare GEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of the local scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numerical simulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shape factor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula from the GEP model provides better performance for predicting the local depth of scouring as compared with conventional regression with the NLR model, with R2 = 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results further suggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as compared to the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring, and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allow calculation of such depth of scouring.

Artificial Immune System with Negative Selection Applied to Facial Biometry Based on Binary Pattern Characteristics

This work aims to explore resources and alternatives for 3D facial biometry using Binary Patterns. A 3D facial geometry image is converted into two 2D representations, appointed as descriptors: A Depth Map and an Azimuthal Projection Distance Image. The first is known as traditional facial geometry, and the second is normal facial geometry that is able to capture the information of different geometries. The characteristics of Local Binary Patterns, Local Phase Quantisers and Gabor Binary Patterns were used with the Depth Map and Azimuthal Projection Distance Image to produce six new facial descriptors: 3D Local Binary Patterns, Local Azimuthal Binary Patterns, Local Depth Phase Quantisers, Local Azimuthal Phase Patterns, and Local Depth Gabor Binary Pattern Magnitudes and Phases. Then, this work uses the immune concept to propose a new approach to realize facial biometry, where the eight new facial descriptors were applied to an Artificial Intelligence algorithm named Artificial Immune Systems of Negative Selection. The analysis of the results shows the efficiency, robustness, precision and reliability of this approach, encouraging further research in this area.