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Nazeer Muhammad

Bio: Nazeer Muhammad is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Wavelet transform & Quantum efficiency. The author has an hindex of 21, co-authored 74 publications receiving 1271 citations. Previous affiliations of Nazeer Muhammad include Fatima Jinnah Women University & Hanyang University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: A novel human action recognition method is contributed by embedding the proposed frames fusion working on the principle of pixels similarity into the existing techniques for recognition rate and trueness.
Abstract: In video sequences, human action recognition is a challenging problem due to motion variation, in frame person difference, and setting of video recording in the field of computer vision. Since last few years, applications of human activity recognition have increased significantly. In the literature, many techniques are implemented for human action recognition, but still they face problem in contrast of foreground region, segmentation, feature extraction, and feature selection. This article contributes a novel human action recognition method by embedding the proposed frames fusion working on the principle of pixels similarity. An improved hybrid feature extraction increases the recognition rate and allows efficient classification in the complex environment. The design consists of four phases, (a) enhancement of video frames (b) threshold-based background subtraction and construction of saliency map (c) feature extraction and selection (d) neural network (NN) for human action classification. Results have been tested using five benchmark datasets including Weizmann, KTH, UIUC, Muhavi, and WVU and obtaining recognition rate 97.2, 99.8, 99.4, 99.9, and 99.9%, respectively. Contingency table and graphical curves support our claims. Comparison with existent techniques identifies the recognition rate and trueness of our proposed method.

81 citations

Journal ArticleDOI
TL;DR: A novel design of contrast stretching based classical features fusion process for localizing the of lungs cancer classification is proposed, which outperforms in comparison with several existing methods with greater accuracy.

81 citations

Journal ArticleDOI
TL;DR: A robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics is presented and it is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.
Abstract: Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

64 citations

Journal ArticleDOI
01 Apr 2017-Fractals
TL;DR: An overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases, and highlights the conditions of the image databases with regard to the recognition rate of each approach.
Abstract: Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

64 citations

Journal ArticleDOI
TL;DR: This work proposes a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator that tolerates an extensive variety of the pECToral muscle geometries with minimum risk of bias in breast profile than existing techniques.
Abstract: In digital mammography, finding accurate breast profile segmentation of women’s mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing. We propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images. To assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth. Our approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.

64 citations


Cited by
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Journal ArticleDOI
01 Jan 1977-Nature
TL;DR: Bergh and P.J.Dean as discussed by the authors proposed a light-emitting diode (LEDD) for light-aware Diodes, which was shown to have promising performance.
Abstract: Light-Emitting Diodes. (Monographs in Electrical and Electronic Engineering.) By A. A. Bergh and P. J. Dean. Pp. viii+591. (Clarendon: Oxford; Oxford University: London, 1976.) £22.

1,560 citations

Journal ArticleDOI
TL;DR: This survey provides a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors, and lists the traditional and new applications.
Abstract: Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.

749 citations

01 Mar 1997
TL;DR: In this article, first principles electronic structure calculations on wurtzite AlN, GaN, and InN reveal crystal field splitting parameters ΔCF of −217, 42, and 41 meV, respectively.
Abstract: First‐principles electronic structure calculations on wurtzite AlN, GaN, and InN reveal crystal‐field splitting parameters ΔCF of −217, 42, and 41 meV, respectively, and spin–orbit splitting parameters Δ0 of 19, 13, and 1 meV, respectively. In the zinc blende structure ΔCF≡0 and Δ0 are 19, 15, and 6 meV, respectively. The unstrained AlN/GaN, GaN/InN, and AlN/InN valence band offsets for the wurtzite (zinc blende) materials are 0.81 (0.84), 0.48 (0.26), and 1.25 (1.04) eV, respectively. The trends in these spectroscopic quantities are discussed and recent experimental findings are analyzed in light of these predictions.

274 citations

Journal ArticleDOI
TL;DR: This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category.

263 citations

Journal ArticleDOI
TL;DR: A survey on the different methods relevant to citrus plants leaves diseases detection and the classification reveals that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy and new tools are needed to fully automate the detection and Classification processes.

251 citations