Over the years, unmanned aerial vehicles (UAVs) have been regarded as a potential solution to surveil public spaces, providing a cheap way for data collection, while covering large and difficult-to-reach areas. This kind of solutions can be particularly useful to detect, track and identify subjects of interest in crowds, for security/safety purposes. In this context, various datasets are publicly available, yet most of them are only suitable for evaluating detection, tracking and short-term re-identification techniques. This paper announces the free availability of the P-DESTRE dataset, the first of its kind to provide video/UAV-based data for pedestrian long-term re-identification research, with ID annotations consistent across data collected in different days. As a secondary contribution, we provide the results attained by the state-of-the-art pedestrian detection, tracking, short/long term re-identification techniques in well-known surveillance datasets, used as baselines for the corresponding effectiveness observed in the P-DESTRE data. This comparison highlights the discriminating characteristics of P-DESTRE with respect to similar sets. Finally, we identify the most problematic data degradation factors and co-variates for UAV-based automated data analysis, which should be considered in subsequent technologic/conceptual advances in this field. The dataset and the full specification of the empirical evaluation carried out are freely available at http://p-destre.di.ubi.pt/.

This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82×2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and peri-ocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject.

This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C -means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

This paper presents a Picture Fuzzy Clustering (PFC) method for MRI brain image segmentation. The PFC is based on the Picture fuzzy set, which is the generalization of the traditional fuzzy set and intuitionistic fuzzy set. In traditional fuzzy set, the problem of uncertainty arises in defining the membership function. Intuitionistic fuzzy set handles this uncertainty by considering hesitation degree. However, intuitionistic fuzzy set fails to solve real time problems which require answers like yes, abstain, no and refusal. The picture fuzzy set solves these problems by considering refusal degree along with membership, neutral and nonmembership degree. Thus, the cluster centers in the PFC may converge to a desirable location than the cluster centers obtained using traditional Fuzzy C-Means (FCM) and Intuitionistic Fuzzy Clustering (IFC). Experimentation is carried out on the standard MRI brain image dataset. To assess the performance, the proposed method is compared with the existing FCM and IFC methods. Results show that the proposed method gives the better result.

A number of vehicles may be controlled and supervised by traffic security and its management. The License Plate Recognition is broadly employed in traffic management to recognize a vehicle whose owner has despoiled traffic laws or to find stolen vehicles. Vehicle License Plate Detection and Recognition is a key technique in most of the traffic related applications such as searching of stolen vehicles, road traffic monitoring, airport gate monitoring, speed monitoring and automatic parking lots access control. It is simply the ability of automatically extract and recognition of the vehicle license number plate's character from a captured image. Number Plate Recognition method suffered from problem of feature selection process. The current method of number plate recognition system only focus on local, global and Neural Network process of Feature Extraction and process for detection. The Optimized Feature Selection process improves the detection ratio of number plate recognition. In this paper, it is proposed a new methodology for `License Plate Recognition' based on wavelet transform function. This proposed methodology compare with Correlation based method for detection of number plate. Empirical result shows that better performance in comparison of correlation based technique for number plate recognition. Here, it is modified the Matching Technique for numberplate recognition by using Multi-Class RBF Neural Network Optimization.

Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most commonly used unsupervised methods are K-Means, Expectation-Maximization, and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images that are complex, largely uncertain, and imprecise. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulting from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulting from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure, and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the art. The performance of the proposed method is also presented using a real-world Autism Spectrum Disorder Detection problem with better accuracy compared to other existing methods.