A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images

Biologically inspired intensity and depth image edge extraction

In recent years artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks we emulate functional computational aspects of biological visual systems. Results demonstrate that the proposed bio-inspired artificial vision system has increased performance over existing computer vision feature extraction approaches

A novel approach to robot vision using a hexagonal grid and spiking neural networks

Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields

Material recognition using tactile sensing

Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems

CDK-dependent nuclear localization of B-Cyclin Clb1 promotes FEAR activation during meiosis I in budding yeast

Cyclin-dependent kinases (CDK) are master regulators of the cell cycle in eukaryotes. CDK activity is regulated by the presence, post-translational modification and spatial localization of its regulatory subunit cyclin. In budding yeast, the B-cyclin Clb1 is phosphorylated and localizes to the nucleus during meiosis I. However the functional significance of Clb1's phosphorylation and nuclear localization and their mutual dependency is unknown. In this paper, we demonstrate that meiosis-specific phosphorylation of Clb1 requires its import to the nucleus but not vice versa. While Clb1 phosphorylation is dependent on activity of both CDK and polo-like kinase Cdc5, its nuclear localization requires CDK but not Cdc5 activity. Furthermore we show that increased nuclear localization of Clb1 during meiosis enhances activation of FEAR (Cdc Fourteen Early Anaphase Release) pathway. We discuss the significance of our results in relation to regulation of exit from meiosis I

An exploratory study looking at the relationship marketing techniques used in the music festival industry

There are current issues and trends in the music festival market, which may affect the success of an event, and market saturation is at the forefront of these issues. Previous literature, maintaining the need for a marketing approach to festivals, identifi es the need for maintaining strong stakeholder relationships in order to succeed in a business environment; attention has been focused to the theory of relationship marketing (RM) because of the recognition that this practice is complementary to the marketing of festivals. The very nature of the music festival as an annual, usually, 4-day event means that effective marketing is needed to keep connections with the consumer throughout the year. This article focuses on the RM techniques utilised within the music festival industry from the viewpoint of the festival organiser in an attempt to establish how festival organisations value and monitor organisational relationships. This article explores the extent to which these relationships are valued and managed; furthermore, the variations between these intricate relationships are considered by focusing on those held with the organisation ’ s consumers and sponsors, the results of which have provided the ability to establish the importance and relevance of RM to the industry and further identify the marketing communication methods employed to establish and maintain such relationships. In-depth, convergent interviews have been conducted with a segment of music festival organisers from a range of events. The results have been integrated with the study of current literature to best exemplify these issues. It has been established that RM has a strong role in today ’ s commercial and independent music festival industry; technological advances are enabling the organiser to support online relationships further and increase consumer loyalty. There is a need to expand the research further because of the complexity of organisational relationships and the varying categories of festivals