Iris recognition based on 2D Gabor filter

Iris recognition is a type of biometrics technology that is based on physiological features of the human body. The objective of this research is to recognize and identify iris among many irises that are stored in a visual database. This study employed a left and right iris biometric framework for inclusion decision processing by combining image processing and artificial bee colony. The proposed approach was evaluated on a visual database of 280 colored iris pictures. The database was then divided into 28 clusters. Images were preprocessed and texture features were extracted based Gabor filters to capture both local and global details within an iris. The technique begins by comparing the attributes of the online-obtained iris picture with those of the visual database. This technique either generates a reject or approve message. The consequences of the intended work reflect the output’s accuracy and integrity. This is due to the careful selection of attributes, as well as the deployment of an artificial bee colony and data clustering, which decreased complexity and eventually increased identification rate to 100%. We demonstrate that the proposed method achieves state-of-the-art performance and that our recommended procedures outperform existing iris recognition systems

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

Object Detection Based on Template Matching through Use of Best-So-Far ABC

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution

Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks

A VANET faces many problems due to dynamic changing of networks with certain requirements such as low delay, high (PDR) packet delivery ratio, low routing overhead and throughput. However, numerous routing protocols have been suggested to meet the demands of Quality of Service (QoS), but none of them can consistently maintain the highest level of QoS simultaneously. The proposed method Ant with Artificial Bee Colony Techniques provides better performance when compared to the existing techniques. This work is compared with latest developed Techniques in VANET to find the best path and different performance metrics are used to check the performance. This work premeditated the comparative analysis of Quality of services made by the performance of latest emerging techniques in VANET and will provide the best solution for the recognition problem in finding the best path based on the evaluation of performance of Quality of Service. Simulation results imply the benefits of the proposed Ant with Artificial Bee Colony Techniques (AABC) produces better result when compare to the other conventional method and Ant Colony Techniques(ACT)in terms of high packet delivery ratio, less end-to-end delay and less energy consumption level. The performance is evaluated by using Ns2 simulator and results shows that the AABC successfully achieve the optimal routes

Brightness and Contrast Modification in Ultrasonography Images Using Edge Detection Results

Currently, ultrasonography device become an important equipment for supporting diagnosis in diesases. Unfortunetaly, a lot of ultrasonography images do not provide enough information for supporting diagnosis especially images produced by low-resolution ultrasonography. It is caused by image quality that has been produced is inadequate because of noise. This research aims to improve image quality by modifying brightness and contrast to the edge detection algorithms. By modifying the brightness and contrast will cause the value of standard deviation of the ultrasonography image is lowered. Raising setting values will cause deviation standard value becomes smaller, and also the result of standard deviation is inversely proportional to the value of RMSE.  The results show that this modification can improve image quality by reducing noise significantly

A contribution for single and multiple faces recognition using feature-based approaches

Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento

Segmentation Method Based on Artificial Bee Colony for Recognizing Leaf Lesion

Many studies on object detection have been initiated but these methods have some limitation. A segmentation method was proposed to recognize a leaf lesion in leaf images and overcome the limitation of existing object detection method in terms of accuracy and processing time. The method includes steps based on Artificial Bee Colony, Otsu, and geometry. The method was conducted in three phases, image preparation, lesion recognition and measurement, and evaluation. The paper shows results of the evaluation phase. The results show that the proposed segmentation method achieved better percentage of accuracy and produced a shorter processing time