Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.

Calculation Methodology for Flexible Arithmetic Processing

Paper submitted to the IFIP International Conference on Very Large Scale Integration (VLSI-SOC), Darmstadt, Germany, 2003.

Targetless image-based method for measuring displacements and strains on concrete surfaces with a consumer camera

Measurement of concrete strain through non-invasive methods is of great importance in civil engineering and structural analysis. Traditional methods use laser speckle and high quality cameras that may result too expensive for many applications. Here we present a method for measuring concrete deformations with a standard reflex camera and image processing for tracking objects in the concretes surface. Two different approaches are presented here. In the first one, on-purpose objects are drawn on the surface, while on the second one we track small defects on the surface due to air bubbles in the hardening process. The method has been tested on a concrete sample under several loading/unloading cycles. A stop-motion sequence of the process has been captured and analyzed. Results have been successfully compared with the values given by a strain gauge. Accuracy of our methods in tracking objects is below 8 μm, in the order of more expensive commercial devices.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

On increasing the effective aperture of antennas by data processing /

Includes bibliographical references (p. 58-59)

Simulation using the Monte Carlo method

Photocopy. Springfield, Va., Distributed by Clearinghouse for Federal Scientific and Technical Information [1969]

Fruit processing, seed viability and dormancy mechanisms of Persoonia sericea A. Cunn. ex R. Br. and P-virgata R. Br. (Proteaceae)

The morphology of the fruit and difficulties with fruit processing impose major limitations to germination of Persoonia sericea and P. virgata. The mesocarp must be removed without harming the embryo. Fermentation of fruit or manual removal of the mesocarp was effective but digestion in 32% hydrochloric acid (HCl) completely inhibited germination. The endocarp is extremely hard and therefore very difficult and time consuming to remove without damaging the seeds. The most efficient method was cracking the endocarp with pliers, followed by manual removal of seeds. Germination was completely inhibited unless at least half of the endocarp was removed. Microbial contamination of the fruit and seeds was controlled by disinfestation and germination of the seed under aseptic conditions. The results suggest that dormancy in these species is primarily due to physical restriction of the embryo by the hard endocarp.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Obsessive-compulsive disorder across developmental trajectory: Cognitive processing of threat in children, adolescents and adults

Background. While the cognitive theory of obsessive-compulsive disorder (OCD) is one of the most widely accepted accounts of the maintenance of the disorder in adults, no study to date has systematically evaluated the theory across children, adolescence and adults with OCD. Method. This paper investigated developmental differences in the cognitive processing of threat in a sample of children, adolescents and adults with OCD. Using an idiographic assessment approach, as well as self-report questionnaires, this study evaluated cognitive appraisals of responsibility, probability, severity, thought-action fusion (TAF), thought-suppression, self-doubt and cognitive control. It was hypothesised that there would be age related differences in reported responsibility for harm, probability of harm, severity of harm, thought suppression, TAR self-doubt and cognitive control. Results. Results of this study demonstrated that children with OCD reported experiencing fewer intrusive thoughts, which were less distressing and less uncontrollable than those experienced by adolescents and adults with OCD. Furthermore, responsibility attitudes, probability biases and thought suppression strategies were higher in adolescents and adults with OCD. Cognitive processes of TAF, perceived severity of harm, self-doubt and cognitive control were found to be comparable across age groups. Conclusions. These results suggest that the current cognitive theory of OCD needs to address developmental differences in the cognitive processing of threat. Furthermore, for a developmentally sensitive theory of OCD, further investigation is warranted into other possible age related maintenance factors. Implications of this investigation and directions for future research are discussed.

The economics of inclusion body processing

Many recombinant proteins are often over-expressed in host cells, such as Escherichia coli, and are found as insoluble and inactive protein aggregates known as inclusion bodies (IBs). Recently, a novel process for IB extraction and solubilisation, based on chemical extraction, has been reported. While this method has the potential to radically intensify traditional IB processing, the process economics of the new technique have yet to be reported. This study focuses on the evaluation of process economics for several IB processing schemes based on chemical extraction and/or traditional techniques. Simulations and economic analysis were conducted at various processing conditions using granulocyte macrophage-colony stimulating factor, expressed as IBs in E. coli, as a model protein. In most cases, IB processing schemes based on chemical extraction having a shorter downstream cascade demonstrated a competitive economic edge over the conventional route, validating the new process as an economically more viable alternative for IB processing.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

A new indexing method for high dimensional dataset

Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.