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Finding an effective way to represent human actions is yet an open problem because it usually requires taking evidences extracted from various temporal resolutions into account. A conventional way of representing an action employs temporally ordered fine-grained movements, e.g., key poses or subtle motions. Many existing approaches model actions by directly learning the transitional relationships...
We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the problem of face alignment “in-the-wild”. Recently, Deep Bottleneck Features (DBF) has been proposed as an effective channel to represent input with compact, low-dimensional descriptors. The locations of fiducial landmarks of human faces could be effectively represented using low dimensional features due to...
As high-resolution fingerprint images are becoming more common, the pores have been found to be one of the promising candidates in improving the performance of automated fingerprint identification systems (AFIS). This paper proposes a deep learning approach towards pore extraction. It exploits the feature learning and classification capability of convolutional neural networks (CNNs) to detect pores...
Accurate pedestrian detection with high speed is always of great interests especially for practical application. Detectors usually follow the feature selection paradigm, and need to first construct rich and diverse features. In particular, current state-of-the-arts generate more channels of feature by convolving the basic feature channels with filter banks, which significantly improves accuracy. In...
Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing...
Combinatorial Testing is a test design methodology that aims to detect the interaction failures existing in the software under test. The combinatorial input space model comprises of the parameters and the values it can take. Building this input space model is a domain knowledge and experience intensive task. The objective of the paper is to assist test designer in building this test model. A rule...
Structural learning, a method to estimate the parameters for discrete energy minimization, has been proven to be effective in solving computer vision problems, especially in 3D scene parsing. As the complexity of the models increases, structural learning algorithms turn to approximate inference to retain tractability. Unfortunately, such methods often fail because the approximation can be arbitrarily...
This paper focuses on the content of test cases, and categorizes test cases into clusters using the similarity between test cases, their degree of similarity is obtained through a morphological analysis. If there are two similar test cases, they would test the same or similar functionalities in similar but different conditions. Thus, when one of them is run for a regression testing, the remaining...
To ensure safety in the construction of important metallic components for roadworthiness, it is necessary to check every component thoroughly using non-destructive testing. In last decades, X-ray testing has been adopted as the principal non-destructive testing method to identify defects within a component which are undetectable to the naked eye. Nowadays, modern computer vision techniques, such as...
Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed. We propose a new random forest (RF)-based online action detection framework that addresses these challenges. Our...
Research on automated image enhancement has gained momentum in recent years, partially due to the need for easy-to-use tools for enhancing pictures captured by ubiquitous cameras on mobile devices. Many of the existing leading methods employ machine-learning-based techniques, by which some enhancement parameters for a given image are found by relating the image to the training images with known enhancement...
Dialect can be defined as a variety of a language that is distinguished from other varieties of the same language by pronunciation, grammar and vocabulary. The process of recognizing such dialects is called Dialect Identification. Kamrupi, although a dialect of the Assamese language, is spoken both in Assam (Kamrup district) and North Bengal. In this paper, we describe a method to identify not just...
Optical Character Recognition can be defined as the process of detecting and identifying text from a scanned image. There are a number of techniques by which recognition is carried out in several languages. The main steps of optical character recognition are Line segmentation, Word segmentation, Character segmentation and Character recognition. Character recognition has two phases: Feature extraction...
This paper presents a novel technique of image classification using BOVW model. The entire process first involves feature detection of images using FAST, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which FREAK, a binary feature descriptor is employed. K-means clustering is then applied in order to make the bag of visual words. Every...
Keystroke dynamics is an effective behavioral biometric for user authentication at a computer terminal. While many distinctive features have been used for the analysis of acquired user patterns and verification of users transparently, a group of features such as Shift and Comma has always been overlooked and treated as noise. In this paper, we define these normally ignored features as secondary features...
This paper introduces to diagnosis of Dyslexia using computing system, considered people difficulties in reading, spelling, writing and speaking. Consequently, a computational analysis classifier will be achieved using dyslexia metrics techniques. Accordingly, Gibson test of brain skills will be used with effect of working memory, auditing (hearing and speech) and visual memory and cognition, visual...
Face recognition system is used for the identification and verification of a face from a video or digital image. In the first phase, Viola Jones algorithm is used to detect and crop face region automatically from image/video frame. The second phase is to recognize the face of a person, in our proposed method Bag of Word technique used to extract features from an image which uses SURF for interest...
Security of communication network is essential for the smooth functioning of smart grid. In this paper, an intrusion detection system is proposed for early detection of threats in advanced metering infrastructure of smart grid. The proposed intrusion detection system has a multi-support vector machine classifier with mutual information based feature selection technique to detect attacks in Neighborhood...
Millions of file uploads and downloads happen every minute resulting in big data creation and manual text categorization is not possible. Hence, there is a need for automatic categorization of documents that makes storage and retrieval more efficient. This research paper proposes a hybrid text categorization model that combines both Rocchio algorithm and Random Forest algorithm to perform Multi-label...
In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections...
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