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In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have...
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face...
This research work proposes an innovative processing scheme for the exploitation of eye movement dynamics on the field of biometrical identification. As the mechanisms that derive eye movements highly depend on each person's idiosyncrasies, cues that reflect at a certain extent individual characteristics may be captured and subsequently deployed for the implementation of a robust identification system...
Interactive training is a technique that allows humans to guide a learning algorithm. This technique is well suited to training first person shooter bots as it allows game designers to iterate a range of behaviors in real-time. This paper investigates an initial attempt at allowing users to interact with the learning process of a reinforcement learning algorithm to create first person shooter bot...
We investigate the use of human metrology for the prediction of certain soft biometrics, viz. gender and weight. In particular, we consider geometric measurements from the head, and those from the remaining parts of the human body, and analyze their potential in predicting gender and weight. For gender prediction, the proposed model results in a 0.7% misclassification rate using both body and head...
Detection and classification of significant human motions are important tasks when analyzing a video that records human activities. Among various human motions, we consider that repetitious motions are specially important since they are usually results of activities with clear intentions. In this paper, we propose and evaluate a method that detects video segments that contain repetitious motions,...
This paper proposes a new face detection method, combining AdaBoost algorithm and neural networks (NN). First, do a pretreatment of the human face image; then NN trains a series of weak classifiers, finally AdaBoost algorithm improves the accuracy of weak classifiers to achieve face detection. The experimental results show that AdaBoost-NN algorithm has better robustness and higher recognition rate,...
Due to the development of World Wide Web technologies, people are living in the place flooding trillions of web pages in every moment. The amount of web size has been increasing dramatically. For this reason, it is getting more difficult to find relevant web documents corresponding to what users want to read. Classifying documents into predefined categories is one of the most important tasks in Natural...
Several concepts from traditional research on Artificial Intelligence (AI) need to be trained before they can be used. For example, when applied to a computer game, its AI framework has to “learn” how the game should be played. However, such trainings may not be trivial due to the often complex game world environments. This paper presents a novel training approach for game AI frameworks where, instead...
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this...
Pedestrian detection is an important part of intelligent transportation systems. In the literature, Histogram of Oriented Gradients (HOG) detector for pedestrian detection is known for its good performance, but there are still some false detections appearing in the cases with flat area or clustered background. To deal with these problems, in this research work we develop a new feature which is based...
Pedestrian detection is a major difficulty in the field of object detection. In order to achieve a balance between speed and accuracy, we propose a new framework in pedestrian detection based on HOG-PCA and Gentle AdaBoost. Firstly, each block-based feature of the image is encoded using the histograms of oriented gradients (HOG), then Principal Components Analysis (PCA) is used to reduce the dimensions...
Text Summarization is the process of identifying and extracting the most vital information in a document. It has been seen as an effective method for dealing with increasing amount of information on the Internet nowadays. In this paper, we present an application of Genetic Programming to the problem of Automatic Text Summarization. Genetic Programming was used to evolve the function that ranks the...
Prior to 9/11, the intelligence process and tools used by our government were primarily directed at known threats with well understood functions and activities. However, with the rising importance of non-state actors and asymmetric threats, threat-focused processes and tools are now directed at known threats whose functions and activities are not well understood [1]. Today's intelligence environment...
Higher Education faces a revolutionary opportunity for change created by the confluence of pressures from economic and societal changes along with opportunities for new modes of education offered by the advancement and widespread availability of Information and Communication Technology. Advances in learning technologies are creating opportunities to improve education in 21st century competencies...
The objective of this research is to investigate the feasibility of using Conway's game of life to solve the problem on printed Lanna character recognition. Pattern recognition can be defined as the objects identification on the basis of information available about it. The problem on character recognition has been discussed to find out the best solution and various recognition methods have been implemented...
Active learning is a useful tool for in-situ learning and adaptive classification systems. While traditional active learning is focused mostly on the single-sample mode, the batch mode of active learning is more interactions efficient. This paper proposes a computationally efficient approach for maximizing the joint entropy of a batch of samples and thereby attaining the maximal information gain and...
Color signatures, histograms and bag of colors are basic and effective strategies for describing the color content of images, for retrieving images by their color appearance or providing color annotation. In some domains, colors assume a specific meaning for users and the color-based classification and retrieval should mirror the initial suggestions given by users in the training set. For instance...
In animal experiments, motor learning and synaptic plasticity in the cortex are enhanced by interventions that reduce the effectiveness of GABAergic intracortical inhibition. Previous work has shown that GABAergic inhibition in human motor cortex is reduced by fatigue of muscles innervated from the regions being tested. Interestingly, similar effects on GABA occur even after fatigue of distant muscles...
Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and...
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