Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

A key role for similarity in vicarious reward.

Humans appear to have an inherent prosocial tendency toward one another in that we often take pleasure in seeing others succeed. This fact is almost certainly exploited by game shows, yet why watching others win elicits a pleasurable vicarious rewarding feeling in the absence of personal economic gain is unclear. One explanation is that game shows use contestants who have similarities to the viewing population, thereby kindling kin-motivated responses (for example, prosocial behavior). Using a game show-inspired paradigm, we show that the interactions between the ventral striatum and anterior cingulate cortex subserve the modulation of vicarious reward by similarity, respectively. Our results support studies showing that similarity acts as a proximate neurobiological mechanism where prosocial behavior extends to unrelated strangers.

Combining interest points and edges for content-based image retrieval

This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features: Harris-Laplace interest points described by the SIFT descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for image retrieval. The feature detection in this work is an integrated process: edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the image retrieval system. Experimental results show this system has good performance. © 2005 IEEE.