Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016), University of Essex, UK, 6 page

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method proposed by Jianchao Yang et al for the efficient classification of the detected objects. Experimental results are presented for both detection and classification, showing the efficiency of the proposed design

Auditory and Visual based Intelligent Lighting Design for Music Concerts

Playing music is about conveying emotions and the lighting at a concert can help do that. However, without a dedicated light technician, many bands have to miss out on lighting that will help them to convey the emotions of what they play. In this paper we aim to develop an intelligent system that detects the intended emotions of the played music and in real-time adjusts the lighting accordingly. Through state-of-the-art research on music and emotion, a row of cues is defined. This includes amount, speed, fluency and regularity for the visual and level, tempo, articulation and timbre for the auditory. By assessing such cues, the system is able to detect the intended emotion. Specific lighting designs are then developed to support these specific emotions. The results suggest that the intelligent emotion-based lighting system has an advantage over a just beat synced lighting and it is concluded that there is reason to explore this idea further

Simulating Physiological Potentials of Daylight Variables in Lighting Design

A holistic approach to daylight dynamics in our built environment can have beneficial outcomes for both physiological and visual effects on humans. Simulations of how daylight variables affect light levels on the horizontal work plane are compared to their physiological effects, measured as melanopic EDI (Melanopic Equivalent Daylight Illuminance) on a vertical plane. The melanopic EDI levels were calculated in a simulated office space in ALFA software (Adaptive Lighting for Alertness) employing the daylight variables of orientation, time of day, season, sky conditions and spatial orientation. Results were analyzed for how daylight design can contribute to the physiological effects of dynamic light in office buildings. Daylight is shown to be a sufficient light source in the majority of cases to meet the recommended values of EDI and provide the suggested horizontal lx level according to the Danish Standards. A mapping of daylight conditions, focusing on the specific factors presented here, can provide guidelines in the design process and future smart building systems. The complex interrelationship between these parameters is important to acknowledge when working with daylight dynamics as a sustainable element in architecture and lighting design

Advances in 3DTV: Theory and Practice

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A genome-wide association study reveals novel SNP markers associated with resilience traits in two Mediterranean dairy sheep breeds

Genetic selection for higher productivity increased dairy sheep susceptibility to diseases and environmental stressors, challenging their health and welfare status and production efficiency. Improving resilience to such stressors can enhance their ability to face these challenges without compromising productivity. Our objective was to estimate genomic heritability and perform genome-wide association studies (GWAS) to detect SNPs and candidate genes associated with three proxy traits for resilience (milk somatic cell count—SCC, lactation persistency—LP, body condition score—BCS) of Chios and Frizarta dairy ewes. We used genome-wide genotypes of 317 Chios and 346 Frizarta ewes. Individual records of milk yield and BCS, and milk samples were collected monthly for two consecutive milking periods; samples were analyzed to determine SCC. The LP was calculated as the regression coefficient of daily milk yield on days from lambing. Within breed, variance components analyses and GWAS were performed using genomic relatedness matrices in single-trait animal linear mixed models. Genomic-based heritability estimates were relatively high (BCS: h2 = 0.54 and 0.55, SCC: h2 = 0.25 and 0.38, LP: h2 = 0.43 and 0.45, for Chios and Frizarta ewes, respectively), compared to previous pedigree-based studies. The GWAS revealed 7 novel SNPs associated with the studied traits; one genome-wide and two suggestive significant SNPs for SCC (Frizarta: rs403061409, rs424064526 and rs428540973, on chromosomes 9, 1 and 12, respectively), one suggestive significant SNP for BCS (Chios: rs424834097 on chromosome 4) and three suggestive significant SNPs for LP (Frizarta: rs193632931 and rs412648955 on chromosomes 1 and 6, Chios: rs428128299 on chromosome 3). Nineteen candidate genes were detected: two for BCS (Chios: POT1, TMEM229A), thirteen for SCC (Frizarta: NTAQ1, ZHX1, ZHX2, LOC101109545, HAS2, DERL1, FAM83A, ATAD2, RBP7, FSTL1, CD80, HCLS1, GSK3B) and four for LP (Frizarta: GRID2, FAIM, CEP70—Chios: GRIP1). Present results show that resilience in the studied dairy sheep breeds is heritable and advance existing knowledge on the genomic background of SCC, LP, and BCS. Future research will quantify effects of different alleles of significant SNPs on the studied traits and search for possible correlations among traits to facilitate their effective incorporation in breeding programs aiming to improve resilience.</p