Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions

Collective Asynchronous Remote Invocation (CARI): A High-Level and Effcient Communication API for Irregular Applications

The Message Passing Interface (MPI) standard continues to dominate the landscape of parallel computing as the de facto API for writing large-scale scientific applications. But the critics argue that it is a low-level API and harder to practice than shared memory approaches. This paper addresses the issue of programming productivity by proposing a high-level, easy-to-use, and effcient programming API that hides and segregates complex low-level message passing code from the application specific code. Our proposed API is inspired by communication patterns found in Gadget-2, which is an MPI-based parallel production code for cosmological N-body and hydrodynamic simulations. In this paper—we analyze Gadget-2 with a view to understanding what high-level Single Program Multiple Data (SPMD) communication abstractions might be developed to replace the intricate use of MPI in such an irregular application—and do so without compromising the effciency. Our analysis revealed that the use of low-level MPI primitives—bundled with the computation code—makes Gadget-2 diffcult to understand and probably hard to maintain. In addition, we found out that the original Gadget-2 code contains a small handful of—complex and recurring—patterns of message passing. We also noted that these complex patterns can be reorganized into a higherlevel communication library with some modifications to the Gadget-2 code. We present the implementation and evaluation of one such message passing pattern (or schedule) that we term Collective Asynchronous Remote Invocation (CARI). As the name suggests, CARI is a collective variant of Remote Method Invocation (RMI), which is an attractive, high-level, and established paradigm in distributed systems programming. The CARI API might be implemented in several ways—we develop and evaluate two versions of this API on a compute cluster. The performance evaluation reveals that CARI versions of the Gadget-2 code perform as well as the original Gadget-2 code but the level of abstraction is raised considerably

Interference and Deployment Issues for Cognitive Radio Systems in Shadowing Environments

In this paper we describe a model for calculating the aggregate interference encountered by primary receivers in the presence of randomly placed cognitive radios (CRs). We show that incorporating the impact of distance attenuation and lognormal fading on each constituent interferer in the aggregate, leads to a composite interference that cannot be satisfactorily modeled by a lognormal. Using the interference statistics we determine a number of key parameters needed for the deployment of CRs. Examples of these are the exclusion zone radius, needed to protect the primary receiver under different types of fading environments and acceptable interference levels, and the numbers of CRs that can be deployed. We further show that if the CRs have apriori knowledge of the radio environment map (REM), then a much larger number of CRs can be deployed especially in a high density environment. Given REM information, we also look at the CR numbers achieved by two different types of techniques to process the scheduling information.Comment: to be presented at IEEE ICC 2009. This posting is the same as the original one. Only author's list is updated that was unfortunately not correctly mentioned in first versio

A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction

This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. The proposed model of hybrid MLRC with SVM have found two significant clusters whereby, each clusters contained 15 and three significant variables for cluster 1 and 2, respectively. The experiments found that the proposed model tend to be the best model with least value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). This finding has shed light to health practitioner in determining the factors that contribute to colorectal cancer

Integrated Fungicidal Management for Downy Mildew of Pumpkin (Pseudoperonosporacubensis)

Downy mildew of pumpkin is caused by the fungus (Pseudoperonosporacubensis), which is responsible for considerable damage to the cucurbits. This pathogen plays a major role for yield losses in pumpkin crop. Current study was intended to verify the effectiveness of different fungicides alone and in combination against downy mildew of pumpkin. Diseased samples were collected for inoculation from different farms of Okara district. A pumpkin variety (Mahadeev) was inoculated by spraying method grown at experimental area of the Department of Plant Pathology, University of Agriculture, Faisalabad under randomized complete block design (RCBD). Selected chemotherapeutic mixtures were sprayed for the control of (Pseudoperonasporacubensis) under field condition. The data were recorded and analyzed statistically. Ipovalicarb (s) + Propanib (P) revealed maximum efficacy against disease (60%) followed by Tebuconazole (s) + Metiram (p) (58%) and Matlaxyal (s) + Mancozeb (p) (55%), Cymoxinal (s) + Mancozeb (p) (52%), Difenconazole (s) +Mancozeb (p) (46%) and Chlorothalonil (p) + Fosytyle Al (39%) respectively. Thus, Ipovalicarb (s) + Propanib (P) can be used to manage the disease under field conditions

Detection of beet necrotic yellow vein virus in Pakistan using bait-plant bioassay, ELISA and RT-PCR

The Northwestern plains of Pakistan are the major sugar beet producing region in the country, providing an important alternative to sugar cane for sugar production when sugar cane is absent in the fields. We surveyed this region for four consecutive years and found that Beet necrotic yellow vein virus (BNYVV) is prevalent in at least five of these districts (Peshawar, Charsadda, Nowshera, Mardan and Swabi). An increase in virus incidence was observed in 2012 as compared to previous years (2009 to 2011) in all the sugar beet growing districts surveyed. The identity of the virus was confirmed using bait bioassay, enzyme-linked immunosorbent assay (ELISA), reverse transcription-polymerase chain reaction (RTPCR) and infectivity assay in roots and leaves of bait plants and sugar beet commercial cultivars. The results indicate that the virus was detected in at least 17 (out of 20) locations and all the four sugar beet cultivars commercially grown in the region were found susceptible to the virus. Our results indicate that bait plant bioassay, ELISA, RT-PCR and infectivity assay can efficiently detect BNYVV in roots and leaves of baited plants, field samples and sugar beet cultivars commercially grown in the region. This is the first report of BNYVV in Pakistan using both conventional and molecular techniques.Keywords: Detection, BNYVV, plant bioassay, ELISA, RT-PCR, sugar beet, Pakista

Big-Four Auditors and Financial Reporting Quality: Evidence from Pakistan

Purpose of Study: The purpose of this paper is to investigate whether firms audited by big four auditors have better financial reporting quality as compared to firms audited by non-big four auditors in Pakistan. Methodology: This study examine whether firms are more engaged in real earnings’ management when their ability to manage accruals is constrained by big four auditors. In current study, we find that big four auditors’ have curtailed accrual- based earnings management activities in firms. Results: However, firms audited by big four auditors are more engaged in costly real earnings’ management activities. The study used a sample of non-financial listed firms in Pakistan over the period of 2009–2016. This study contributes to the field of corporate governance, where it provides deep insight to policy-makers who are interested in improving financial reporting quality in transnational economies

A hybrid method for eyes detection in facial images

This paper proposes a hybrid method for eyes localization in facial images. The novelty is in combining techniques that utilise colour, edge and illumination cues to improve accuracy. The method is based on the observation that eye regions have dark colour, high density of edges and low illumination as compared to other parts of face. The first step in the method is to extract connected regions from facial images using colour, edge density and illumination cues separately. Some of the regions are then removed by applying rules that are based on the general geometry and shape of eyes. The remaining connected regions obtained through these three cues are then combined in a systematic way to enhance the identification of the candidate regions for the eyes. The geometry and shape based rules are then applied again to further remove the false eye regions. The proposed method was tested using images from the PICS facial images database. The proposed method has 93.7% and 87% accuracies for initial blobs extraction and final eye detection respectively