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Nowadays semantic image annotation is becoming more than ever a very challenging issue since it helps improving image interpretation and retrieval. Currently, most semantic annotation methods represent images as lists of keywords or histogram of visual words, and do not consider the spatial distribution of regions, nor any prior knowledge concerning objects in a scene. This obviously leads to weak...
We describe an end-to-end generative approach for the segmentation and recognition of human activities. In this approach, a visual representation based on reduced Fisher Vectors is combined with a structured temporal model for recognition. We show that the statistical properties of Fisher Vectors make them an especially suitable front-end for generative models such as Gaussian mixtures. The system...
In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed...
We present ParallelSpaces, a novel method to explore bipartite datasets in both feature and data dimensions. This dyadic data is displayed as weighted bipartite graphs using scatterplots in two separated visual spaces, where each entity is positioned according to multi-dimensional properties of each entity or similarity in preferences. Selecting or navigating in one space is reflected in the other...
Both academia and organizations show great interest in streaming big data analytics - the process of extracting knowledge structures from continuous, high volume and high velocity continuous flow of data in a myriad of formats from a variety of real-time data sources. The challenge for organizations lies in being able to transform this deluge of data into instantaneous intelligence that can enable...
Social image search becomes an active research field in recent years due to the rapid development in big data processing technologies. In the retrieval systems, text description/tags play a key role to bridge the semantic gap between low-level features and higher-level concepts, and so guarantee the reliable search. However, in practice manual tags are usually noisy and incomplete, resulting in a...
In order to create or modify workflow model which is validated, it is good to support real-time trace and debug for data-intensive workflow model design tool. And, if it is possible to change variables of workflow instance, when workflow model trace or debug, it is very useful. So, we propose dynamic variables exchange method, and attempt to exchange variables on a data-intensive workflow modelling...
The current work proposes an approach for the recognition of plants from their digital leaf images using multiple visual features to handle heterogeneous plant types. Recognizing the fact that plant leaves can have a variety of recognizable features like color (green and non-green) and shape (simple and compound) and texture (vein structure patterns), a single set of features may not be efficient...
We propose a novel approach for predicting Web user click intention, using pupil dilation data generated by an eye-tracking device as input. Our goal is to determine if this variable is useful to differentiate choice and no-choice states, and if so, to generate a classification model for predicting choice understood as a click. For this, we performed an experiment with 25 healthy subjects in which...
von Hofsten proposed a model for explaining how the perception of egocentric distance is affected by vergence angle. According to his model, binocular distance estimation relies on the difference between vergence response and rest vergence, rather than on absolute vergence angle. We applied this model to the data obtained in a study on adaptation to telestereoscopic viewing. Such an optical distortion,...
Dyslexia is one of the most common Specific Learning Difficulties (SpLDs) in the world. Students with dyslexia have poor fluency in reading, writing, spelling, speech, short-term memory, and also other related disorders. In addition emotion is recognised as important as the cognitive difficulty that affects dyslexia learning. Students with dyslexia often suffer emotions like frustration and low self-esteem...
This paper describes a dynamic technique for identifying learners learning styles based on their behavior in the learning environment influenced by literature approach. The technique was suggested based on VAK Learning Styles model and the behavioral patterns. First we defined the learning material features (contents, case studies, examples, exercises and assessments) and the behavioral patterns (staying...
Developing neuro-inspired computing paradigms that mimic nervous system function is an emerging field of research that fosters our model understanding of the biological system and targets technical applications in artificial systems. The computational power of simulated brain circuits makes them a very promising tool for the development for brain-controlled robots. Early phases of robotic controllers...
Markov random field models of textures describe images by statistics of local image features and identify conditional dependencies between pixels. To account for higher-order inter-dependencies between multiple pixels, most common high-order modelling follows the well-known FRAME or Fields of Experts (FoE) frameworks in using marginals of the responses of various filters as descriptive statistics...
ABGP is a special cognitive model, which consists of awareness, beliefs, goals and plans. As most agent architectures, ABGP agents obtain knowledge from the natural scenes only through single preestablished rules as well, don't directly capture the natural scenes information like human visual. Inspired by the biological visual cortex (V1) and the higher brain areas perceiving visual features, we propose...
A good basis for invariant recognition is the shape or the contour of an object, which is usually stable and persistent. There are some challenges to be solved. The first is that the object has different shapes from different views. The second is to organize the information into a structured data, so that they can be manipulated easily. We represent the image as a set of lines by a detector, and all...
A person's routine incorporates the frequent and regular behaviour patterns over a time scale, e.g. daily routine. In this work we present a method for unsupervised discovery of a single person's daily routine within an indoor environment using a static depth sensor. Routine is modelled using top down and bottom up hierarchies, formed from location and silhouette spatio-temporal information. We employ...
VANET Simulation schemes require a combination of mobility and wireless network simulation packages, coupled with custom scripts, visualization tools and various scenarios. The results of simulation studies need to be supported by special tools or scripts to analyze or visualize them easily. Some additional difficulties arise at sharing the results, visually comparing simulation runs across different...
Classifying sequential data is an important problem in machine learning with applications in time series, sensor streams, and image analysis. The ordered structure of sequential data presents a difficulty for the standard classification models, which has motivated the task of generating features for vector-based discriminative models. Shapelet methods, which have been extensively studied in this topic,...
The visualization of data streams plays ail important role in diagnosing anomalies in the human body, particularly in Intensive Care Units (ICU). We propose an unconventional paradigm in computer science called Empirical Modelling that IS suitable for the combined visualization and exploration of biomedical data streams. Empirical Modelling for Dynamic Visualization (EMDV) provides a learning space...
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