Cross-spectral local descriptors via quadruplet network

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data

An efficient multiscale scheme using local zernike moments for face recognition

In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.WOS:000437326800174Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2 - Q3ArticleUluslararası işbirliği ile yapılmayan - HAYIRMayıs2018YÖK - 2017-1

An Experimental and Numerical Investigation of Nitrogen Dioxide Emissions Characteristics of Compression Ignition Dual Fuel Engines

Detailed experimental research was conducted to explore the impact of the addition of gaseous fuels, including H2 and natural gas (NG), and engine load on the emissions of NO2, NO, and NOx from dual fuel engines. The addition of less than 2% of H2 or NG was shown to dramatically increase the emissions of NO2 until a maximum level of NO2 emissions was reached. The increased NO 2 emissions were due to the conversion of NO to NO2. The maximum NO2/NOx ratio obtained with the addition of H2 was 3.2 to 5.0 times that of diesel operation. The maximum NO 2/NOx ratio obtained with the addition of NG was 3.4 to 4.3 times that of diesel operation. Further increasing the amount of gaseous fuel beyond the point of maximum NO2 emissions resulted in a reduction of NO2 emissions. Detailed examination of factors having the potential to affect the formation of NOx and NO2 in compression ignition engines reported a firm correlation between the emissions of NO 2 and emissions of unburned H2 and methane (CH4), and their relative emissions. The presence of unburned gaseous fuels that survived the main combustion process appears to be one of the main factors contributing to the enhanced conversion of NO to NO2. This was supported by the experimental data reported in the literature. The presence of fumigation fuels outside the diesel spray plume might be the main factor contributing to the increased emissions of NO2 from dual fuel engines. The spontaneous combustion of fumigation fuels that are entrained into the diesel spray plume may not contribute to the increased emissions of NO 2. In comparison, the correlations between the increased emissions of NO2 and the variation in bulk mixture temperature and heat release process including maximum heat release rate, and combustion duration were weak.;A single zone, zero-dimensional, constant volume numerical model with detailed chemistry was used to simulate the oxidization process of the gaseous fuel, as well as its effect on the conversion of NO to NO2 after the post-combustion mixing of the gaseous fuel surviving the main combustion process with the NOx-containing combustion products. The gaseous fuel examined included CH4, H2, and carbon monoxide (CO). The simulation results revealed the significant effects of the fuel mixed, its initial concentration in the mixture, and the initial temperature on the oxidization of gaseous fuel, the conversion of NO to NO2, and the destruction of NO2 to NO after the completion of the oxidation process.;The single zone zero-dimensional model was further modified to a variable volume model with the volume of the combustion chamber calculated using the geometry of the 1999 Cummins engine and engine speed. The modified variable volume model with detailed chemistry was used to improve the simulation of the effect on the conversion of NO to NO2 of the post-combustion mixing of surviving gaseous fuel with NOx-containing combustion products. The spatial variation of the local bulk mixture temperature with the progress of the combustion process and the variation of cylinder volume during the expansion process was taken into account by a pseudo temperature at the top dead center (TDC) noted as Tpseudo TDC defined in this research. The simulation identified the importance of the phasing of postcombustion mixing on the oxidation of gaseous fuel and its effect on the conversion of NO to NO2.;A preliminary sensitivity analysis was also conducted to identify the reactions having significant effect on the conversion of NO to NO2 and its destruction to NO. Among the four reactions associated with the formation and destruction of NO2, R186 was identified as the main reaction to the formation of NO2 during the oxidation process of H 2 and CO. This was due to the high concentration of HO2 formed during the oxidation process of H2 and CO in the combustion product. The destruction of NO2 to NO occurred through R187 and R189. (Abstract shortened by UMI.)

Face Image Modality Recognition and Photo-Sketch Matching

Face is an important physical characteristic of human body, and is widely used in many crucial applications, such as video surveillance, criminal investigation, and security access system. Based on realistic demand, such as useful face images in dark environment and criminal profile, different modalities of face images appeared, e.g. three-dimensional (3D), near infrared (NIR), and thermal infrared (TIR) face images. Thus, researches with various face image modalities become a hot area. Most of them are set on knowing the modality of face images in advance, which contains a few limitations. In this thesis, we present approaches for face image modality recognition to extend the possibility of cross-modality researches as well as handle new modality-mixed face images. Furthermore, a large facial image database is assembled with five commonly used modalities such as 3D, NIR, TIR, sketch, and visible light spectrum (VIS). Based on the analysis of results, a feature descriptor based on convolutional neural network with linear kernel SVM did an optimal performance.;As we mentioned above, face images are widely used in crucial applications, and one of them is using the sketch of suspect\u27s face, which based on the witness\u27 description, to assist law enforcement. Since it is difficult to capture face photos of the suspect during a criminal activity, automatic retrieving photos based on the suspect\u27s facial sketch is used for locating potential suspects. In this thesis, we perform photo-sketch matching by synthesizing the corresponding pseudo sketch from a given photo. There are three methods applied in this thesis, which are respectively based on style transfer, DualGAN, and cycle-consistent adversarial networks. Among the results of these methods, style transfer based method did a poor performance in photo-sketch matching, since it is an unsupervised one which is not purposeful in photo to sketch synthesis problem while the others need to train pointed models in synthesis stage