Other affiliations: Warsaw University of Technology, AGH University of Science and Technology, Białystok Technical University
Bio: Khalid Saeed is an academic researcher from Bialystok University of Technology. The author has contributed to research in topics: Feature extraction & Biometrics. The author has an hindex of 16, co-authored 151 publications receiving 1281 citations. Previous affiliations of Khalid Saeed include Warsaw University of Technology & AGH University of Science and Technology.
Papers published on a yearly basis
TL;DR: A new thinning algorithm is proposed that presents interesting properties in terms of processing quality and algorithm clarity, enriched with examples, which makes it useful and versatile for a variety of applications.
Abstract: This paper aims at three aspects closely related to each other: first, it presents the state of the art in the area of thinning methodologies, by giving descriptions of general ideas of the most significant algorithms with a comparison between them. Secondly, it proposes a new thinning algorithm that presents interesting properties in terms of processing quality and algorithm clarity, enriched with examples. Thirdly, the work considers parallelization issues for intrinsically sequential algorithms of thinning. The main advantage of the suggested algorithm is its universality, which makes it useful and versatile for a variety of applications.
25 Jun 2014
TL;DR: This Springer Brief presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes that have applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition.
Abstract: This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.
TL;DR: A speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition is discussed, building a system to recognize both the uttered words and their speaker through an innovative graphical algorithm for feature extraction from the voice signal.
Abstract: This paper discusses a speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition. The speech signals of the Arabic digits from zero to ten are processed graphically (the signal is treated as an object image for further processing). The identifying and classifying methods are performed with Burg's estimation model and the algorithm of Toeplitz matrix minimal eigenvalues as the main tools for signal-image description and feature extraction. At the stage of classification, both conventional and neural-network-based methods are used. The success rate of the speaker-identifying system obtained in the presented experiments for individually uttered words is excellent and has reached about 98.8% in some cases. The miss rate of about 1.2% was almost only because of false acceptance (13 miss cases in 1100 tested voices). These results have promisingly led to the design of a security system for SAS identification. The average overall success rate was then 97.45% in recognizing one uttered word and identifying its speaker, and 92.5% in recognizing a three-digit password (three individual words), which is really a high success rate because, for compound cases, we should successfully test all the three uttered words consecutively in addition to and after identifying their speaker; hence, the probability of making an error is basically higher. The authors' major contribution to this task involves building a system to recognize both the uttered words and their speaker through an innovative graphical algorithm for feature extraction from the voice signal. This Toeplitz-based algorithm reduces the amount of computations from operations on an ntimesn matrix that contains n2 different elements to a matrix (of Toeplitz form) that contains only n elements that are different from each other
••26 Jun 2008
TL;DR: This work proposes an approach to identify the keyboard user while typing based on two extracted keystrokes features: 'flight' and 'dwell'.
Abstract: On-the-fly keyboard user authorization is certainly an interesting option for standard security procedures for high-security computer systems. Unauthorized access to logged-in workstation could threaten the security of data and systems. Conventional authorization methods (passwords, fingerprint scans) verify user identity only during logging process, leaving system vulnerable to user replacement afterwards. Procedures overcoming this peril are often invasive (constant visual monitoring) or uncomfortable (frequent identity verification with user cooperation). A possible solution for constant authorization without these drawbacks is to identify the keyboard user while typing. The approach we propose is based on two extracted keystrokes features: 'flight' and 'dwell'. We have tested the suggested solutions on individual group resembling a medium-sized company. The obtained results are promising.
01 Jan 2014
TL;DR: A detailed survey about the principles of image binarization techniques is introduced and a comprehensive review of a number of classical methodologies together with the recent works are considered.
Abstract: A detailed survey about the principles of image binarization techniques is introduced in this chapter. A comprehensive review is given. A number of classical methodologies together with the recent works are considered for comparison and study of the concept of binarization for both document and graphic images.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Feb 2009
TL;DR: This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale, and what might be coming next.
Abstract: Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?
01 Jan 2003