The roles of static depth information and object-image relative motion in perception of heading

In a series of 6 experiments, two hypotheses were tested: that nominal heading perception is determined by the relative motion of images of objects positioned at different depths (R. F. Wang & J. E. Cutting 1999) and that static depth information contributes to this determination. By manipulating static depth information while holding retinal-image motion constant during  simulated self-movement, the authors found that static depth information played a role in determining perceived heading. Some support was also found for the involvement of R. F. Wang and J. E. Cutting’s (1999) categories of object-image relative motion in determining perceived heading. However, results suggested an unexpected functional dominance of information about heading relative to apparently near objects.

Depth first rule heneration for text categorization

Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.

Child abuse investigation: an in depth analysis of how police officers perceive and cope with daily work challenges

Purpose: The purpose of the current study was two-fold: to explore police officers' perceptions of the daily challenges involved in child abuse investigation and how those challenges affect their ability to undertake child abuse investigations, and to explore how these challenges are managed on a daily basis. Design/methodology/approach: This study employed a qualitative research design. In-depth interviews were conducted with a diverse sample of 25 police officers working in child abuse units across three Australian states. Findings: Inductive thematic analysis revealed that heavy caseload and collaboration with other professional groups are two key sources of negative work stress frequently associated with child abuse investigation. Further, despite the provision of organisational strategies aimed at reducing work stress, the officers tended to rely predominantly on informal coping mechanisms.  Research limitations/implications: This study has raised many questions for further research aimed at developing interventions to assist police organisations in managing work stress. Originality/value: This paper provides an in-depth analysis of the key challenges associated with child abuse investigation and the coping mechanisms employed for overcoming these challenges from the unique perspective of police officers authorised to investigate child abuse.

Depth estimation using multi-wavelet analysis based stereo vision approach

The problem of dimensional defects in aluminum die- casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo cameras pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

Corporate ethics and trust in intra-corporate relationships, an in-depth and longitudinal case description

Theoretically, a contribution of this article is the pinpointed connection between corporate ethics and trust in intra-corporate relationships. Furthermore, it contributes to a conceptual framework that distinguishes between the constructs of business ethics and corporate ethics. The authors also provide a grounded conceptual framework of corporate ethics and trust. The principal dyadic determinants of corporate ethics in intra-corporate relationships are interpreted to be management behaviour versus employee perception of that behaviour. Empirically, the contribution is an in-depth and longitudinal case description that underpins the topic and the discussion provided in the article.

Depth-profile analysis of elements by glow discharge optical emmission specrometry

Glow-discharge optical emission spectrometry (GD-OES) is a powerful tool for the rapid analysis of elements in the surface of solids. One may employ GD-OES to determine quantitatively the bulk concentration of elements in a sample. With further calibration, one may also obtain elemental concentrations as a function of depth into the sample. This allows depth profiling on a host of advanced materials: treated metals, coated metals and other materials, multi-layers, painted surfaces, hard samples coated with polymers, thin films, and many others.

A consortium of institutions in Victoria, led by Deakin University, has purchased a new glow-discharge optical emission spectrometer. This instrument has the ability to perform elemental depth profiling on a wide range of materials. This technique, the first of its kind in Australia, is of particular interest to those working on metals, ceramics, glasses, coatings, semi-conductors, and multi-layers. We present here an overview of depth profiling by GD-OES and some examples of its use.

Depth estimation of metallic objects using multiwavelets scale-space representation

The problem of dimensional defects in aluminum die-castings is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic with highly reflective nature, it is extremely hard to estimate true dimensionality of these metallic parts, autonomously. Some existing vision systems are capable of estimating depth to high accuracy, however are very much hardware dependent, involving the use of light and laser pattern projectors, integrated into vision systems or laser scanners. However, due to the reflective nature of these metallic parts and variable factory environments, the aforementioned vision systems tend to exhibit unpromising performance. Moreover, hardware dependency makes these systems cumbersome and costly. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a pair of stereo cameras and a defused fluorescent light. The proposed vision system is capable of estimating surface depths within the accuracy of 0.5mm. In addition, the system is invariant to illuminative variations as well as orientation and location of the objects on the input image space, making the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup. The proposed system is a major part of quality inspection system for the automotive manufacturing industry.

Depth estimation using multiwavelet analysis based stereo vision approach

The problem of dimensional defects in aluminum die-casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo camera pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.