A Navigation System for the Visually Impaired: A Fusion of Vision and Depth Sensor

For a number of years, scientists have been trying to develop aids that can make visually impaired people more independent and aware of their surroundings. Computer-based automatic navigation tools are one example of this, motivated by the increasing miniaturization of electronics and the improvement in processing power and sensing capabilities. This paper presents a complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor. The system is based around corners and depth values from Kinect’s infrared sensor. Obstacles are found in images from a camera using corner detection, while input from the depth sensor provides the corresponding distance. The combination is both efficient and robust. The system not only identifies hurdles but also suggests a safe path (if available) to the left or right side and tells the user to stop, move left, or move right. The system has been tested in real time by both blindfolded and blind people at different indoor and outdoor locations, demonstrating that it operates adequately.</jats:p

Multiagent Systems for 3D Reconstruction Applications

3D models of scenes are used in many areas ranging from cultural heritage to video games. In order to model a scene, there are several techniques. One of the well-known and well-used techniques is image-based reconstruction. An image-based reconstruction starts with data acquisition step and ends with 3D model of the scene. Data are collected from the scene using various ways. The chapter explains how data acquisition step can be handled using a multiagent system. The explanation is provided by literature reviews and a study whose purpose is reconstructing an area in 3D using a multiagent UAV system

Social Outcomes of Corporate Governance: Evidence from the Food Industry of Pakistan

Social and environmental problems are becoming strategic concerns for the managers in the current business scenario because it is challenging their sustainability. Here the need arises to respond to this changing phenomenon accordingly. In this regard social impact of corporate governance has not yet been explored where it can play a role of driver of excellence in terms of social performance and it is required to be studied. To check the existing situation, this study has been conducted where the social impact of corporate governance has been explored in the food Industry of Pakistan. Questionnaires have been filled from 176 managers working in six food producing firms listed in Pakistan Stock Exchange (PSX). Structural Equation Modeling based partial least square (PLS) has been used where Smart PLS has been used for model estimation. Results are supporting the stakeholder theory as Nestle Pakistan and Engro Foods are driving social excellence through corporate governance practices, where the corporations are showing strong positive relationships of corporate governance practices with stakeholders management, environmental integrity and protection, social cohesion and equity while insignificant relationship exists between strategic proactivity and corporate governance practices as people are resistant to change and innovation. The relationships can be explored in other industries like Oil and gas, Chemicals and Construction etc

Measuring the Coverage of Interest Point Detectors

Repeatability is widely used as an indicator of the performance of an image feature detector but, although useful, it does not convey all the information that is required to describe performance. This paper explores the spatial distribution of interest points as an alternative indicator of performance, presenting a metric that is shown to concur with visual assessments. This metric is then extended to provide a measure of complementarity for pairs of detectors. Several state-of-the-art detectors are assessed, both individually and in combination. It is found that Scale Invariant Feature Operator (SFOP) is dominant, both when used alone and in combination with other detectors

Analysis of interest point distribution in SURF octaves

Speeded-Up Robust Features (SURF) is a state-ofthe- art, scale- and rotation-invariant feature extraction technique with the potential for real-time execution. Although SURF has been extensively employed for multi-scale computer vision applications since its inception, there are still some areas of this computationally complex algorithm that have not been fully explored and require detailed analysis to enable algorithm-level optimization of SURF for real-time execution. In particular, the distribution of interest points in SURF octaves is a topic that requires thorough investigation. Contrary to the present perception, this paper demonstrates that there is a possibility of higher octaves being more significant than the lower octaves in terms of detected interest points for real-life images. The paper also shows that variation of blob response threshold has a significant effect on interest point distribution. The results presented highlight the need of developing a systematic approach to SURF octave selection

An algorithm for the contextual adaption of SURF octave selection with good matching performance: best octaves.

Speeded-Up Robust Features is a feature extraction algorithm designed for real-time execution, although this is rarely achievable on low-power hardware such as that in mobile robots. One way to reduce the computation is to discard some of the scale-space octaves, and previous research has simply discarded the higher octaves. This paper shows that this approach is not always the most sensible and presents an algorithm for choosing which octaves to discard based on the properties of the imagery. Results obtained with this best octaves algorithm show that it is able to achieve a significant reduction in computation without compromising matching performance

FAB: Fast Angular Binary Descriptor for Matching Corner Points in Video Imagery

Image matching is a fundamental step in several computer vision applications where the requirement is fast, accurate, and robust matching of images in the presence of different transformations. Detection and more importantly description of low-level image features proved to be a more appropriate choice for this purpose, such as edges, corners, or blobs. Modern descriptors use binary values to store neighbourhood information of feature points for matching because binary descriptors are fast to compute and match. This paper proposes a descriptor called Fast Angular Binary (FAB) descriptor that illustrates the neighbourhood of a corner point using a binary vector. It is different from conventional descriptors because of selecting only the useful neighbourhood of corner point instead of the whole circular area of specific radius. The descriptor uses the angle of corner points to reduce the search space and increase the probability of finding an accurate match using binary descriptor. Experiments show that FAB descriptor’s performance is good, but the calculation and matching time is significantly less than BRIEF, the best known binary descriptor, and AMIE, a descriptor that uses entropy and average intensities of informative part of a corner point for the description