Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithm

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

Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning

Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and stateof-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.peer-reviewe

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure