Embedded Vision Systems: A Review of the Literature

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems

The identification of vegetable matter using Fourier Transform Infrared Spectroscopy

A comparison of Fourier transform infrared methods for identifying vegetable matter is presented. Results from diffuse reflectance (DRIFTS) and FTIR microscopy on samples of cell wall material from 10 different species of fruits and vegetables are presented and compared with results from a KBr disc method. All three methods are able to discriminate between a test sample (apple) and non-apple samples. However, there are significant spectral variations from method to method which preclude the use of spectral libraries obtained by one method being used to identify spectra obtained by another method

Spectroscopic method for the authentication of vegetable matter

Infrared spectroscopy, in combination with classical discriminant analysis, was used to investigate the composition of the cell walls extracted from a number of plant species. It was shown that apple cell walls gave rise to a characteristic spectrum that allowed the discrimination of apple from a range of other plant species. The conclusion drawn is that infrared spectroscopy is potentially of value for the identification of plant cell walls, opening up the possibility that the technique may be useful for the authentication of fruit-containing products

Color and depth sensing sensor technologies for robotics and machine vision

Robust scanning technologies that offer 3D view of the world in real time are critical for situational awareness and safe operation of robotic and autonomous systems. Color and depth sensing technologies play an important role in localization and navigation in unstructured environments. Most often, sensor technology must be able to deal with factors such as objects that have low textures or objects that are dynamic, soft, and deformable. Adding intelligence to the imaging system has great potential in simplifying some of the problems. This chapter discusses the important role of scanning technologies in the development of trusted autonomous systems for robotic and machine vision with an outlook for areas that need further research and development. We start with a review of sensor technologies for specific environments including autonomous systems, mining, medical, social, aerial, and marine robotics. Special focus is on the selection of a particular scanning technology to deal with constrained or unconstrained environments. Fundamentals, advantages, and limitations of color and depth (RGB-D) technologies such as stereo vision, time of flight, structured light, and shape from shadow are discussed in detail. Strategies to deal with lighting, color constancy, occlusions, scattering, haze, and multiple reflections are discussed. This chapter also introduces the latest developments in this area by discussing the potential of emerging technologies, such as dynamic vision and focus-induced photoluminescence