Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking

Combining geometric edge detectors for feature detection

We propose a novel framework for the analysis and modeling of discrete edge filters, based on the notion of signed rays. This framework will allow us to easily deduce the geometric and localization properties of a family of first-order filters, and use this information to design custom filter banks for specific applications. As an example, a set of angle-selective corner detectors is constructed for the detection of buildings in video sequences. This clearly illustrates the merit of the theory for solving practical recognition problems

AUTOMATED REGISTRATION OF BUILDING SCAN WITH BIM THROUGH DETECTION OF CONGRUENT CORNER POINTS

Current methods of construction progress monitoring involve manual data collection and processing, which are time-consuming and labor-intensive, with a dominant human presence entailing several flaws such as missing or inaccurate information. Recent research efforts for automated progress monitoring have largely focused on model-based assessment methods that are dependent on a pre-requisite step known as registration which is still performed manually due to numerous challenges. This study proposes a novel automated coarse registration method that utilizes the BIM model as the as-planned model to align it with the corresponding as-built model using their geometrical features. First, it extracts the corner points in both models using their planar features and then identifies the conjugate corner points based on different geometric invariants. Later, the transformations are determined from those conjugate points and the most accurate transformation parameter is finalized in the end. The proposed method is validated on different datasets

An analysis of navigation algorithms for smartphones using J2ME

Embedded systems are considered one of the most potentialareas for future innovations. Two embedded fields that will mostcertainly take a primary role in future innovations are mobile roboticsand mobile computing. Mobile robots and smartphones are growing innumber and functionalities, becoming a presence in our daily life. In thispaper, we study the current feasibility of a smartphone to execute navigationalgorithms. As a test case, we use a smartphone to control anautonomous mobile robot. We tested three navigation problems: Mapping,Localization and Path Planning. For each of these problems, analgorithm has been chosen, developed in J2ME, and tested on the field.Results show the current mobile Java capacity for executing computationallydemanding algorithms and reveal the real possibility of usingsmartphones for autonomous navigation

Transcriptome Analysis of the Desert Locust Central Nervous System: Production and Annotation of a Schistocerca gregaria EST Database

) displays a fascinating type of phenotypic plasticity, designated as ‘phase polyphenism’. Depending on environmental conditions, one genome can be translated into two highly divergent phenotypes, termed the solitarious and gregarious (swarming) phase. Although many of the underlying molecular events remain elusive, the central nervous system (CNS) is expected to play a crucial role in the phase transition process. Locusts have also proven to be interesting model organisms in a physiological and neurobiological research context. However, molecular studies in locusts are hampered by the fact that genome/transcriptome sequence information available for this branch of insects is still limited. EST information is highly complementary to the existing orthopteran transcriptomic data. Since many novel transcripts encode neuronal signaling and signal transduction components, this paper includes an overview of these sequences. Furthermore, several transcripts being differentially represented in solitarious and gregarious locusts were retrieved from this EST database. The findings highlight the involvement of the CNS in the phase transition process and indicate that this novel annotated database may also add to the emerging knowledge of concomitant neuronal signaling and neuroplasticity events. EST data constitute an important new source of information that will be instrumental in further unraveling the molecular principles of phase polyphenism, in further establishing locusts as valuable research model organisms and in molecular evolutionary and comparative entomology