A feasibility study on the use of agent-based image recognition on a desktop computer for the purpose of quality control in a production environment

Thesis (M. Tech.) - Central University of Technology, Free State, 2006A multi-threaded, multi-agent image recognition software application called RecMaster has been developed specifically for the purpose of quality control in a production environment. This entails using the system as a monitor to identify invalid objects moving on a conveyor belt and to pass on the relevant information to an attached device, such as a robotic arm, which will remove the invalid object. The main purpose of developing this system was to prove that a desktop computer could run an image recognition system efficiently, without the need for high-end, high-cost, specialised computer hardware. The programme operates by assigning each agent a task in the recognition process and then waiting for resources to become available. Tasks related to edge detection, colour inversion, image binarisation and perimeter determination were assigned to individual agents. Each agent is loaded onto its own processing thread, with some of the agents delegating their subtasks to other processing threads. This enables the application to utilise the available system resources more efficiently. The application is very limited in its scope, as it requires a uniform image background as well as little to no variance in camera zoom levels and object to lens distance. This study focused solely on the development of the application software, and not on the setting up of the actual imaging hardware. The imaging device, on which the system was tested, was a web cam capable of a 640 x 480 resolution. As such, all image capture and processing was done on images with a horizontal resolution of 640 pixels and a vertical resolution of 480 pixels, so as not to distort image quality. The application locates objects on an image feed - which can be in the format of a still image, a video file or a camera feed - and compares these objects to a model of the object that was created previously. The coordinates of the object are calculated and translated into coordinates on the conveyor system. These coordinates are then passed on to an external recipient, such as a robotic arm, via a serial link. The system has been applied to the model of a DVD, and tested against a variety of similar and dissimilar objects to determine its accuracy. The tests were run on both an AMD- and Intel-based desktop computer system, with the results indicating that both systems are capable of efficiently running the application. On average, the AMD-based system tended to be 81% faster at matching objects in still images, and 100% faster at matching objects in moving images. The system made matches within an average time frame of 250 ms, making the process fast enough to be used on an actual conveyor system. On still images, the results showed an 87% success rate for the AMD-based system, and 73% for Intel. For moving images, however, both systems showed a 100% success rate

Object recognition by symmetrised graph matching using relaxation labelling with an inhibitory mechanism

Object recognition using graph-matching techniques can be viewed as a two-stage process: extracting suitable object primitives from an image and corresponding models, and matching graphs constructed from these two sets of object primitives. In this paper we concentrate mainly on the latter issue of graph matching, for which we derive a technique based on probabilistic relaxation graph labelling. The new method was evaluated on two standard data sets, SOIL47 and COIL100, in both of which objects must be recognised from a variety of different views. The results indicated that our method is comparable with the best of other current object recognition techniques. The potential of the method was also demonstrated on challenging examples of object recognition in cluttered scenes

Object recognition by symmetrised graph matching using relaxation labelling with an inhibitory mechanism

Object recognition using graph-matching techniques can be viewed as a two-stage process: extracting suitable object primitives from an image and corresponding models, and matching graphs constructed from these two sets of object primitives. In this paper we concentrate mainly on the latter issue of graph matching, for which we derive a technique based on probabilistic relaxation graph labelling. The new method was evaluated on two standard data sets, SOIL47 and COIL100, in both of which objects must be recognised from a variety of different views. The results indicated that our method is comparable with the best of other current object recognition techniques. The potential of the method was also demonstrated on challenging examples of object recognition in cluttered scenes

Structure-function relationships in stressosome complexes from Listeria monocytogenes and Bacillus subtilis

PhD ThesisListeria monocytogenes is a foodborne bacterial pathogen that can resist and overcome extreme environmental conditions, such as the extremes of temperature, salinity and pH that are encountered during food processing. Stress resistance is regulated by a supramolecular protein complex called the stressosome, which detects and integrates environmental stress signals that induce a partner-switching and phosphorylation cascade leading to the activation of an alternative RNA polymerase sigma factor, σB, which controls a regulon of ~200 genes involved in the general stress response. The stressosome comprises three main proteins, RsbR (which has four paralogues), RsbS and RsbT. The N-terminal domains of RsbR proteins have been proposed to act as stress sensors and they project from the core of the stressosome as ‘turrets’. However, the mechanism by which signals are perceived and transmitted is still unknown. Structural studies of the stressosome’s sensory domains resulted in the successful determination of the crystal structures of N-RsbR1, N-RsbR2, and N-RsbR3. Ligand binding pockets were identified in N-RsbR3 that yield insight into signal perception and transduction mechanisms. The interaction of the Prli42 miniprotein with N-RsbR proteins was also assessed and shown not to occur at biologically-relevant concentrations. Common ligands and drug-like fragments were screened against binding to the putative ligand binding pocket and candidate interacting molecules were identified. Native stressosomes pulled-down from B. subtilis cell lysates by affinity purification contained all four RsbR paralogues, along with RsbS and RsbT. Initial electron microscopy of the purified native stressosomes were consistent with the formation of a highly symmetric structure. Purification and EM studies of stressosome variants revealed that stressosomes can be formed by any of the RsbR paralogues. Stressosome variants were analysed by cryo-EM single particle analysis, which showed that the RsbR-RsbS complex displays similar features to the known stressosome complex of B. subtilis, albeit with markedly different stoichiometries

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes