Capacity limits for the detection of changing visual features

Capacity limits in visual attention have traditionally been studied using static arrays of elements from which an observer must detect a target defined by a certain visual feature or combination of features. In the current study we use this visual search paradigm, with accuracy as the dependent variable, to examine attentional capacity limits for different visual features undergoing change over time. In Experiment 1, detectability of a single changing target was measured under conditions where the type of change (size, speed, colour), the magnitude of change, the set size and homogeneity of the unchanging distractors were all systematically varied. Psychometric function slopes were calculated for different experimental conditions and ‘change thresholds’extracted from these slopes were used in Experiment 2, in which multiple supra-threshold changes were made, simultaneously, either to a single or to two or three different stimulus elements. These experiments give an objective psychometric paradigm for measuring changes in visual features over time. Results favour object-based accounts of visual attention, and show consistent differences in the allocation of attentional capacity to different perceptual dimensions.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.

A corpus-based analysis of transfer effects and connected speech processes in Vietnamese English

This paper presents a corpus-based descriptive analysis of the most prevalent transfer effects and connected speech processes observed in a comparison of 11 Vietnamese English speakers (6 females, 5 males) and 12 Australian English speakers (6 males, 6 females) over 24 grammatical paraphrase items. The phonetic processes are segmentally labelled in terms of IPA diacritic features using the EMU speech database system with the aim of labelling departures from native-speaker pronunciation. An analysis of prosodic features was made using ToBI framework. The results show many phonetic and prosodic processes which make non-native speakers’ speech distinct from native ones. The corpusbased methodology of analysing foreign accent may have implications for the evaluation of non-native accent, accented speech recognition and computer assisted pronunciation- learning.

Indexing text and visual features for WWW images

In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index imagersquos multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partitionrsquos center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images haves similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the ldquodimensionality curserdquo existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms imagersquos text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partitionrsquos center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude.

Heart rate variability characterization using a time-frequency based instantaneous frequency estimation technique

In this paper, a new method for characterizing the newborn heart rate variability (HRV) is proposed. The central of the method is the newly proposed technique for instantaneous frequency (IF) estimation specifically designed for nonstationary multicomponen signals such as HRV. The new method attempts to characterize the newborn HRV using features extracted from the time–frequency (TF) domain of the signal. These features comprise the IF, the instantaneous bandwidth (IB) and instantaneous energy (IE) of the different TF components of the HRV. Applied to the HRV of both normal and seizure suffering newborns, this method clearly reveals the locations of the spectral peaks and their time-varying nature. The total energy of HRV components, ET and ratio of energy concentrated in the low-frequency (LF) to that in high frequency (HF) components have been shown to be significant features in identifying the HRV of newborn with seizures.

Exploring composite acoustic features for efficient music similarity query

Music similarity query based on acoustic content is becoming important with the ever-increasing growth of the music information from emerging applications such as digital libraries and WWW. However, relative techniques are still in their infancy and much less than satisfactory. In this paper, we present a novel index structure, called Composite Feature tree, CF-tree, to facilitate efficient content-based music search adopting multiple musical features. Before constructing the tree structure, we use PCA to transform the extracted features into a new space sorted by the importance of acoustic features. The CF-tree is a balanced multi-way tree structure where each level represents the data space at different dimensionalities. The PCA transformed data and reduced dimensions in the upper levels can alleviate suffering from dimensionality curse. To accurately mimic human perception, an extension, named CF+-tree, is proposed, which further applies multivariable regression to determine the weight of each individual feature. We conduct extensive experiments to evaluate the proposed structures against state-of-art techniques. The experimental results demonstrate superiority of our technique.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

A neural network based technique for automatic classification of road cracks

This paper presents a neural network based technique for the classification of segments of road images into cracks and normal images. The density and histogram features are extracted. The features are passed to a neural network for the classification of images into images with and without cracks. Once images are classified into cracks and non-cracks, they are passed to another neural network for the classification of a crack type after segmentation. Some experiments were conducted and promising results were obtained. The selected results and a comparative analysis are included in this paper.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

Fuzzy modeling based recognition of multi-font numerals

In this paper, we present a new scheme for off-line recognition of multi-font numerals using the Takagi-Sugeno (TS) model. In this scheme, the binary image of a character is partitioned into a fixed number of sub-images called boxes. The features consist of normalized vector distances (gamma) from each box. Each feature extracted from different fonts gives rise to a fuzzy set. However, when we have a small number of fonts as in the case of multi-font numerals, the choice of a proper fuzzification function is crucial. Hence, we have devised a new fuzzification function involving parameters, which take account of the variations in the fuzzy sets. The new fuzzification function is employed in the TS model for the recognition of multi-font numerals.

An assessment of the learning benefits of using a web-based learning environment when teaching accounting

The impact and use of information and communication technology on learning outcomes for accounting students is not well understood. This study investigates the impact of design features of Blackboard 1 used as aWeb-based Learning Environment (WBLE) in teaching undergraduate accounting students. Specifically, this investigation reports on a number of Blackboard design features (e.g. delivery of lecture notes, announcements, online assessment and model answers) used to deliver learning materials regarded as necessary to enhance learning outcomes. Responses from 369 on-campus students provided data to develop a regression model that seeks to explain enhanced participation and mental effort. The final regression shows that student satisfaction with the use of a WBLE is associated with five design features or variables. These include usefulness and availability of lecture notes, online assessment, model answers, and online chat.

Edges and bars: where do people see features in 1-D images?

There have been two main approaches to feature detection in human and computer vision - based either on the luminance distribution and its spatial derivatives, or on the spatial distribution of local contrast energy. Thus, bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of features in images? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square-wave and all Fourier components have a common phase. Observers used a cursor to mark where bars and edges were seen for different test phases (Experiment 1) or judged the spatial alignment of contours that had different phases (e.g. 0 degrees and 45 degrees ; Experiment 2). The feature positions defined by both tasks shifted systematically to the left or right according to the sign of the phase offset, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks (bars) and gradient peaks (edges), but not by energy peaks which (by design) predicted no shift at all. These results encourage models based on a Gaussian-derivative framework, but do not support the idea that human vision uses points of phase alignment to find local, first-order features. Nevertheless, we argue that both approaches are presently incomplete and a better understanding of early vision may combine insights from both. (C)2004 Elsevier Ltd. All rights reserved.

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.