Construction studies in higher education and the use of digital technology

The construction industry wants graduate employees skilled in relationship building and information technology and communications (ITC). Much of the relationship building at universities has evolved through technology. Government and the ITC industry fund lobby groups to influence both educational establishments and Government to incorporate more ITC in education _ and ultimately into the construction industry. This influencing ignores the technoskeptics’ concerns about student disengagement through excessive online distractions. Construction studies students (n=64) and lecturers (n=16) at a construction university were surveyed to discover the impact of the use and applications of ITC. Contrary to Government and industry technopositivism, construction students and lecturers preferred hard copy documents to online feedback for assignments and marking, more human interface and less technological substitution and to be on campus for lectures and face-to-face meetings rather than viewing on-screen. ITC also distracted users from tasks which, in the case of students, prevented the development of the concentration and deep thinking which a university education should deliver. The research findings are contrary to the promotions of Government, ITC industry and ITC departments and have implications for construction employers where a renewed focus on human communication should mean less stress, fewer delays and cost overruns.

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information.

Example-Based Face Shape Recovery Using the Zenith Angle of the Surface Normal

We present a method for recovering facial shape using an image of a face and a reference model. The zenith angle of the surface normal is recovered directly from the intensities of the image. The azimuth angle of the reference model is then combined with the calculated zenith angle in order to get a new field of surface normals. After integration of the needle map, the recovered surface has the effect of mapped facial features over the reference model. Experiments demonstrate that for the lambertian case, surface recovery is achieved with high accuracy. For non-Lambertian cases, experiments suggest potential for face recognition applications.