Mohammed Hazim Alkawaz

Content-based image retrieval using PSO and k-means clustering algorithm

In various application domains such as website, education, crime prevention, commerce, and biomedicine, the volume of digital data is increasing rapidly. The trouble appears when retrieving the data from the storage media because some of the existing methods compare the query image with all images in the database; as a result, the search space and computational complexity will increase, respectively. The content-based image retrieval (CBIR) methods aim to retrieve images accurately from large image databases similar to the query image based on the similarity between image features. In this study, a new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms. It is presented as a proposed CBIR method that uses the color and texture images as visual features to represent the images. The proposed method is based on four feature extractions for measuring the similarity, which are color histogram, color moment, co-occurrence matrices, and wavelet moment. The experimental results have indicated that the proposed system has a superior performance compared to the other system in terms of accuracy.

The effect of emotional colour on creating realistic expression of avatar

The research on facial animation has grown very fast and become more realistic in term of 3D facial data since the laser scan and advance 3D tools can support creating complex facial model. However, that approaches still lacking in term of facial expression based on emotional condition. Facial skin color is one parameter that is contributing on augmenting the realism of facial expression, because its closely related to the emotion which happens inside the human. This paper provides a new technique for facial animation to change the colour of facial skin based on linear interpolation.

Automated nuclei segmentation of malignant using level sets

Segmentation of objects from a noisy and complex image is still a challenging task that needs to be addressed. This article proposed a new method to detect and segment nuclei to determine whether they are malignant or not (determination of the region of interest, noise removal, enhance the image, candidate detection is employed on the centroid transform to evaluate the centroid of each object, the level set [LS] is applied to segment the nuclei). The proposed method consists of three main stages: preprocessing, seed detection, and segmentation. Preprocessing stage involves the preparation of the image conditions to ensure that they meet the segmentation requirements. Seed detection detects the seed point to be used in the segmentation stage, which refers to the process of segmenting the nuclei using the LS method. In this research work, 58 H&E breast cancer images from the UCSB Bio‐Segmentation Benchmark dataset are evaluated. The proposed method reveals the high performance and accuracy in comparison to the techniques reported in literature. The experimental results are also harmonized with the ground truth images.

Data Hiding Based on Improved Exploiting Modification Direction Method and Huffman Coding

Abstract The rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction (EMD) method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k1 and k2, which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a (2ki + 1)-ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the χ2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes.

Automated kinship verification and identification through human facial images: a survey

Face is the most considerable constituent that people use to recognize one another. Humans can quickly and easily identify each other by their faces and since facial features are unobtrusive to lighting condition and pose, face remains as a dynamic recognition approach to human. Kinship recognition refers to the task of training a machine to recognize the blood relation between a pair of kin and non-kin faces (verification) based on features extracted from facial images, and to determine the exact type or degree of that relation (identification). Automatic kinship verification and identification is an interesting areas for investigation, and it has a significant impact in many real world applications, for instance, forensic, finding missing family members, and historical and genealogical research. However, kinship recognition is still not largely explored due to insufficient database availability. In this paper we present a survey on issues and challenges in kinship verification and identification, related previous works, current trends and advancements in kinship recognition, and potential applications and research direction for the future. We also found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.

Multifocus watermarking approach based on discrete cosine transform

Image fusion process consolidates data and information from various images of same sight into a solitary image. Each of the source images might speak to a fractional perspective of the scene, and contains both “pertinent” and “immaterial” information. In this study, a new image fusion method is proposed utilizing the Discrete Cosine Transform (DCT) to join the source image into a solitary minimized image containing more exact depiction of the sight than any of the individual source images. In addition, the fused image comes out with most ideal quality image without bending appearance or loss of data. DCT algorithm is considered efficient in image fusion. The proposed scheme is performed in five steps: (1) RGB colour image (input image) is split into three channels R, G, and B for source images. (2) DCT algorithm is applied to each channel (R, G, and B). (3) The variance values are computed for the corresponding 8 × 8 blocks of each channel. (4) Each block of R of source images is compared with each other based on the variance value and then the block with maximum variance value is selected to be the block in the new image. This process is repeated for all channels of source images. (5) Inverse discrete cosine transform is applied on each fused channel to convert coefficient values to pixel values, and then combined all the channels to generate the fused image. The proposed technique can potentially solve the problem of unwanted side effects such as blurring or blocking artifacts by reducing the quality of the subsequent image in image fusion process. The proposed approach is evaluated using three measurement units: the average of Qabf, standard deviation, and peak Signal Noise Rate. The experimental results of this proposed technique have shown good results as compared with older techniques. Microsc. Res. Tech. 79:431–437, 2016.

An overview of interactive wet cloth simulation in virtual reality and serious games

Abstract Cloth simulation is an area of research in domain of computer graphics, that assists actors/actress to wear such clothes that seem real using animated techniques. In this paper, we discuss a panoramic overview on computer graphics cloth simulation and modelling techniques such as firstly geometrical methods which utilise geometrical equations to generate cloth-like visualisations without taking into account the cloth apparent characteristics, secondly these approaches in dynamic cloth simulation are connected to fabric engineering field including three types which are the mass-spring model, particle-based system and elastically deformable model and thirdly hybrid methods that combine physical and geometrical methods essentially to arrive at a more complex simulation model are highlighted. The advance cloth simulation which stated that the cloth simulation is a mixture of a many methods and strategies, which involves mechanical simulation, statistical integration, collision recognition, constraints, and rendering approaches for creating clothes is discussed. Physical modelling phase and collision handling phase are critically inspected. The interaction between fluids and rigid and elastic bodies that boosts the complexity of fluids’ motion is discussed..

Realistic Facial Expression of Virtual Human Based on Color, Sweat, and Tears Effects

Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry) and blushing (anger and happiness) is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs) of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH) methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The result shows that virtual human facial expression is enhanced by mimicking actual sweating and tears simulations for all extreme expressions. The proposed method has contribution towards the development of facial animation industry and game as well as computer graphics.

Retinal imaging analysis based on vessel detection: JAMAL et al.

With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patients retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.

Concise analysis of current text automation and watermarking approaches

With ceaseless utilization of web and other online advances, it has turned out to be amazingly simple to imitate, discuss, and convey digital material. Subsequently, confirmation and copyright assurance issues have been emerged. Text is the most widely used media of communication on the web as compared with images and videos. The significant part of books, daily papers, websites, commercial, research papers, reports, and numerous different archives are just the plain text. Therefore, copyrights protection of plain text is a critical issue that could not be accepted at all. This paper presents a concise analysis on information hiding techniques and their consequences. Various watermarking systems properties are highlighted along with different types of attacks and their possible defenses. Additionally, the current applications for text document watermarking and the reasons for the problems of text document watermarking are described. Finally, attacks characteristics are discussed including types of attacks, volumes, and nature on text. Current watermarking approaches are reviewed in depth, and consequently, their advantages and weaknesses are highlighted. Copyright