Color image segmentation by combining the convex active contour and the Chan Vese model

Pattern Analysis and Applications - In this paper, we present a robust and computationally efficient image segmentation technique based on a hybrid convex active contour and the Chan–Vese...     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.     

A passive technique for detecting copy-move forgeries by image feature matching

Multimedia Tools and Applications - Due to the recent evolutions in the technologies various digital devices and image processing tools are available in the market. Consequently, crime rates are...     

A robust and efficient convolutional deep learning framework for age-invariant face recognition

Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age-invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age-invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state-of-the-art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.     

Comparative study of maximum power point tracking techniques for hybrid renewable energy system

The power generation demand is increasing day-by-day throughout the world, therefore, the use of hybrid systems becomes a significant solution. The hybrid renewable energy system (HRES) is used for delivering power in various regions in order to overcome intermittence of wind and solar resources. Because of increasing environmental problems, for example, greenhouse gas emission and energy cost have interested novel research into substitute methods in favour of electrical power generation. Maximum Power Point Tracking (MPPT) control method is a vast deal of novel research used for enhancing the efficiency of HRES. The authors have revealed that the hybrid techniques i.e. Global MPPT, fuzzy-neuro systems, Adaptive Neuro-Fuzzy Inference System (ANFIS), Perturbed and Observe (P&O) + Adaptive Neural Network (ANN) etc. can provide best results as compared to other MPPT control methods. This paper offering a state of art review of MPPT control techniques for HRES.     

Quantum channel discrimination without entanglement

We address the problem of unambiguous discrimination of quantum channels (UDQC) without entanglement. As our main result, we show that even in the absence of entanglement, partial UDQC (PUDQC) can still be performed—depending on the unknown given channel. We provide a necessary and sufficient condition for PUDQC and put forth a method to perform the PUDQC once the said condition is met. We propose the performance metrics that capture the expected performance of the PUDQC independent of the specific channels to be distinguished. Finally, we perform PUDQC on several qubit channel pairs as concrete examples and derive the proposed performance metrics for these channel pairs.     

Self-embedding framework for tamper detection and restoration of color images

Multimedia Tools and Applications - The present era is paving huge expansion to the transmission of digital data in fields like health, military intelligence, scientific research, and publication...     

Underwater object detection: architectures and algorithms – a comprehensive review

Underwater object detection is an essential step in image processing and it plays a vital role in several applications such as the repair and maintenance of sub-aquatic structures and marine sciences. Many computer vision-based solutions have been proposed but an optimal solution for underwater object detection and species classification does not exist. This is mainly because of the challenges presented by the underwater environment which mainly include light scattering and light absorption. The advent of deep learning has enabled researchers to solve various problems like protection of the subaquatic ecological environment, emergency rescue, reducing chances of underwater disaster and its prevention, underwater target detection, spooring, and recognition. However, the advantages and shortcomings of these deep learning algorithms are still unclear. Thus, to give a clearer view of the underwater object detection algorithms and their pros and cons, we proffer a state-of-the-art review of different computer vision-based approaches that have been developed as yet. Besides, a comparison of various state-of-the-art schemes is made based on various objective indices and future research directions in the field of underwater object detection have also been proffered.

     

A novel offline handwritten text recognition technique to convert ruled-line text into digital text through deep neural networks

Multimedia Tools and Applications - Offline Handwritten Text Recognition (HTR) has been an active area of research due to its wide range of applications and challenges. Recently, many offline HTR...