Zaid Omar

Bio: Zaid Omar is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Image fusion & Feature extraction. The author has an hindex of 6, co-authored 41 publications receiving 335 citations. Previous affiliations of Zaid Omar include Imperial College London.

A review of hand gesture and sign language recognition techniques

Abstract: Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.

A mobile application of American sign language translation via image processing algorithms

Abstract: Due to the relative lack of pervasive sign language usage within our society, deaf and other verbally-challenged people tend to face difficulty in communicating on a daily basis. Our study thus aims to provide research into a sign language translator applied on the smartphone platform, due to its portability and ease of use. In this paper, a novel framework comprising established image processing techniques is proposed to recognise images of several sign language gestures. More specifically, we initially implement Canny edge detection and seeded region growing to segment the hand gesture from its background. Feature points are then extracted with Speeded Up Robust Features (SURF) algorithm, whose features are derived through Bag of Features (BoF). Support Vector Machine (SVM) is subsequently applied to classify our gesture image dataset; where the trained dataset is used to recognize future sign language gesture inputs. The proposed framework has been successfully implemented on smartphone platforms, and experimental results show that it is able to recognize and translate 16 different American Sign Language gestures with an overall accuracy of 97.13%.

Image Fusion: An Overview

Abstract: An extensive overview of the field of image fusion is presented in this paper. The study firstly delves into the problem of multiple modalities that form the motivation for fusion, and discusses the main advantages of image fusion. Further, it discusses in detail the history of fusion algorithms that comprise various transform-domain and data driven methods. A section on image fusion applications, ranging from geo-spatial, medical to security fields, is also presented. Overall the paper aims to bring to light the advances and state-of-the-art within the image fusion research area so as to benefit other fields.

Combination of EEMD and Morphological Filtering for Baseline Wander Correction in EMG Signals

Abstract: This paper aims at proposing an effective method for Baseline Wander removal from the EMG signals. Ensemble Empirical Mode Decomposition (EEMD) Algorithm is first applied to the baseline corrupted EMG signals to decompose them into Intrinsic Mode Functions (IMFs). After this step, morphological filtering employing octagon-shaped structuring element has been applied to filter out each IMF. Finally, the results of the proposed filtering methodology are compared with those of EMD- and EEMD-based filtering methods. Simulation results report that the methodology used in this study has eliminated the baseline wander from EMG signals with minimal distortions.

Region-based image fusion using a combinatory Chebyshev-ICA method

Abstract: The aim of this paper is to provide an algorithm for image fusion which combines the techniques of Chebyshev polynomial (CP) approximation and independent component analysis (ICA), based on the regional information of input images. We present a region-based method that combines the merits of both techniques. It utilises segmentation to identify edges, texture and other important features in the input image and subsequently apply the different fusion methods according to regions. The proposed method exhibits better perceptual performance than individual CP and ICA fusion approaches especially in noise corrupted images.

Automatic detection of microaneurysms in color fundus images for screening of diabetic retinopathy.

Abstract: This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease. The algorithm can be divided into four steps. The first step consists in image enhancement, shade correction and image normalization of the green channel. The second step aims at detecting candidates, i.e. all patterns possibly corresponding to MA, which is achieved by diameter closing and an automatic threshold scheme. Then, features are extracted, which are used in the last step to automatically classify candidates into real MA and other objects; the classification relies on kernel density estimation with variable bandwidth. A database of 21 annotated images has been used to train the algorithm. The algorithm was compared to manually obtained gradings of 94 images; sensitivity was 88.5% at an average number of 2.13 false positives per image.

A comparative study on shape retrieval using Fourier descriptiors with different shape signatures

Abstract: Shape is one of the most important features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exists. However, most of those methods either do not well represent shape or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FD) achieve both well representation and well normalization. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant different effect on the result of retrieval. In this paper, we build a Java retrieval framework to compare shape retrieval using FDs derived from different signatures. Common issues and techniques for shape representation and normalization are also analyzed in the paper. Data is given to show the retrieval result.