Perspective distortion modeling for image measurements

A perspective distortion modelling for monocular view that is based on the fundamentals of perspective projection is presented in this work. Perspective projection is considered to be the most ideal and realistic model among others, which depicts image formation in monocular vision. There are many approaches trying to model and estimate the perspective effects in images. Some approaches try to learn and model the distortion parameters from a set of training data that work only for a predefined structure. None of the existing methods provide deep understanding of the nature of perspective problems. Perspective distortions, in fact, can be described by three different perspective effects. These effects are pose, distance and foreshortening. They are the cause of the aberrant appearance of object shapes in images. Understanding these phenomena have long been an interesting topic for artists, designers and scientists. In many cases, this problem has to be necessarily taken into consideration when dealing with image diagnostics, high and accurate image measurement, as well as accurate pose estimation from images. In this work, a perspective distortion model for every effect is developed while elaborating the nature of perspective effects. A distortion factor for every effect is derived, then followed by proposed methods, which allows extracting the true target pose and distance, and correcting image measurements

The design and mathematical model of a novel variable stiffness extensor-contractor pneumatic artificial muscle

This article presents the design of a novel Extensor-Contractor Pneumatic Artificial Muscle (ECPAM). This new actuator has numerous advantages over traditional pneumatic artificial muscles. These include the ability to both contract and extend relative to a nominal initial length, the ability to generate both contraction and extension forces and the ability to vary stiffness at any actuator length. A kinematic analysis of the ECPAM is presented in this article. A new output force mathematical model has been developed for the ECPAM based on its kinematic analysis and the theory of energy conservation. The correlation between experimental results and the new mathematical model has been investigated and show good correlation. Numerous stiffness experiments have been conducted to validate the variable stiffness ability of the actuator at a series of specific fixed lengths. This has proven that actuator stiffness can be adjusted independently of actuator length. Finally a stiffness-position controller has been developed to validate the effectiveness of the novel actuator

Novel design and position control strategy of a soft robot arm

This article presents a novel design of a continuum arm, which has the ability to extend and bend efficiently. Numerous designs and experiments have been done to different dimensions on both types of McKibben pneumatic muscle actuators (PMA) in order to study their performances. The contraction and extension behaviour have been illustrated with single contractor actuators and single extensor actuators, respectively. The tensile force for the contractor actuator and the compressive force for the extensor PMA are thoroughly explained and compared. Furthermore, the bending behaviour has been explained for a single extensor PMA, multi extensor actuators and multi contractor actuators. A two-section continuum arm has been implemented from both types of actuators to achieve multiple operations. Then, a novel construction is proposed to achieve efficient bending behaviour of a single contraction PMA. This novel design of a bending-actuator has been used to modify the presented continuum arm. Two different position control strategies are presented, arising from the results of the modified soft robot arm experiment. A cascaded position control is applied to control the position of the end effector of the soft arm at no load by efficiently controlling the pressure of all the actuators in the continuum arm. A new algorithm is then proposed by distributing the x, y and z-axis to the actuators and applying an effective closed-loop position control to the proposed arm at different load conditions

A comparative evaluation of interest point detectors and local descriptors for visual SLAM

Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriat

Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it

Metabonomic Investigation of Liver Profiles of Nonpolar Metabolites Obtained from Alcohol-Dosed Rats and Mice Using High Mass Accuracy MSn Analysis

Alcoholism is a complex disorder that, in man, appears to be genetically influenced, although the underlying genes and molecular pathways are not completely known. Here the intragastric alcohol feeding model in rodents was used together with high mass accuracy LC/MS(n) analysis to assess the metabonomic changes in nonpolar metabolite profiles for livers from control and alcohol treated rats and mice. Ion signals with a peak area variance of less than 30% (based on repeat analysis of a pooled quality control sample analysed throughout the batch) were submitted to multivariate statistical analysis (using principal components analysis, PCA). PCA revealed robust differences between profiles from control and alcohol-treated animals from both species. The major metabolites seen to differ between control and alcohol-treated animals were identified using high accuracy MS(n) data and verified using external search engines (http://www.lipidmaps.org; http://www.hmdb.ca; http://www.genome.jp/kegg/) and authentic standards. The main metabolite classes to show major changes in the alcoholic liver-derived samples were fatty acyls, fatty acid ethyl esters, glycerolipids and phosphatidylethanol homologues. Significant metabolites that were up-regulated by alcohol treatment in both rat and mouse livers included fatty acyls, metabolites such as octadecatrienoic acid and eicosapentaenoic acid, a number of fatty acid ethyl esters such as ethyl arachidonate, ethyl docosahexaenoic acid, ethyl linoleate and ethyl oleate and phosphatidylethanol (PEth) homologues (predominantly PEth 18:0/18:2 and PEth 16:0/18:2; PEth homologues are currently considered as potential biomarkers for harmful and prolonged alcohol consumption in man). A number of glycerophospholipids resulted in both up-regulation (m/z 903.7436 [M+H](+) corresponding to a triglyceride) and down-regulation (m/z 667.5296 [M+H](+) corresponding to a diglyceride). Metabolite profiles were broadly similar in both mouse and rat models. However, there were a number of significant differences in the alcohol-treated group particularly in the marked down-regulation of retinol and free cholesterol in the mouse compared to the rat. Unique markers for alcohol treatment included ethyl docosahexaenoic acid. Metabolites were identified with high confidence using predominantly negative ion MS(n) data for the fatty acyl components to match to www.lipidmaps.org MS and MS/MS databases; interpreting positive ion data needed to take into account possible adduct ions which may confound the identification of other lipid classes. The observed changes in lipid profiles were consistent with alcohol induced liver injury in humans

Area-throughput trade-offs for SHA-1 and SHA-256 hash functions’ pipelined designs

High-throughput designs of hash functions are strongly demanded due to the need for security in every transmitted packet of worldwide e-transactions. Thus, optimized and non-optimized pipelined architectures have been proposed raising, however, important questions. Which is the optimum number of the pipeline stages? Is it worth to develop optimized designs or could the same results be achieved by increasing only the pipeline stages of the non-optimized designs? The paper answers the above questions studying extensively many pipelined architectures of SHA-1 and SHA-256 hashes, implemented in FPGAs, in terms of throughput/area (T/A) factor. Also, guides for developing efficient security schemes designs are provided. Read More: https://www.worldscientific.com/doi/abs/10.1142/S021812661650032

A hybrid nonlinear-discriminant analysis feature projection technique

Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques. © 2008 Springer Berlin Heidelberg