Automatic boundary extraction and rectification of bony tissue in CT images using artificial intelligence techniques

A novel approach is presented for fully automated boundary extraction and rectification of bony tissue from planar CT data. The approach extracts and rectifies feature boundary in a hierarchical fashion. It consists of a fuzzy multilevel thresholding operation, followed by a small void cleanup procedure. Then a binary morphological boundary detector is applied to extract the boundary. However, defective boundaries and undesirable artifacts may still be present. Thus two innovative anatomical knowledge based algorithms are used to remove the undesired structures and refine the erroneous boundary. Results of applying the approach on lumbar CT images are presented, with a discussion of the potential for clinical application of the approach.published_or_final_versio

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Fuzzy logic based approach for object feature tracking

This thesis introduces a novel technique for feature tracking in sequences of greyscale images based on fuzzy logic. A versatile and modular methodology for feature tracking using fuzzy sets and inference engines is presented. Moreover, an extension of this methodology to perform the correct tracking of multiple features is also presented. To perform feature tracking three membership functions are initially defined. A membership function related to the distinctive property of the feature to be tracked. A membership function is related to the fact of considering that the feature has smooth movement between each image sequence and a membership function concerns its expected future location. Applying these functions to the image pixels, the corresponding fuzzy sets are obtained and then mathematically manipulated to serve as input to an inference engine. Situations such as occlusion or detection failure of features are overcome using estimated positions calculated using a motion model and a state vector of the feature. This methodology was previously applied to track a single feature identified by the user. Several performance tests were conducted on sequences of both synthetic and real images. Experimental results are presented, analysed and discussed. Although this methodology could be applied directly to multiple feature tracking, an extension of this methodology has been developed within that purpose. In this new method, the processing sequence of each feature is dynamic and hierarchical. Dynamic because this sequence can change over time and hierarchical because features with higher priority will be processed first. Thus, the process gives preference to features whose location are easier to predict compared with features whose knowledge of their behavior is less predictable. When this priority value becomes too low, the feature will no longer tracked by the algorithm. To access the performance of this new approach, sequences of images where several features specified by the user are to be tracked were used. In the final part of this work, conclusions drawn from this work as well as the definition of some guidelines for future research are presented.Nesta tese é introduzida uma nova técnica de seguimento de pontos característicos de objectos em sequências de imagens em escala de cinzentos baseada em lógica difusa. É apresentada uma metodologia versátil e modular para o seguimento de objectos utilizando conjuntos difusos e motores de inferência. É também apresentada uma extensão desta metodologia para o correcto seguimento de múltiplos pontos característicos. Para se realizar o seguimento são definidas inicialmente três funções de pertença. Uma função de pertença está relacionada com a propriedade distintiva do objecto que desejamos seguir, outra está relacionada com o facto de se considerar que o objecto tem uma movimentação suave entre cada imagem da sequência e outra função de pertença referente à sua previsível localização futura. Aplicando estas funções de pertença aos píxeis da imagem, obtêm-se os correspondentes conjuntos difusos, que serão manipulados matematicamente e servirão como entrada num motor de inferência. Situações como a oclusão ou falha na detecção dos pontos característicos são ultrapassadas utilizando posições estimadas calculadas a partir do modelo de movimento e a um vector de estados do objecto. Esta metodologia foi inicialmente aplicada no seguimento de um objecto assinalado pelo utilizador. Foram realizados vários testes de desempenho em sequências de imagens sintéticas e também reais. Os resultados experimentais obtidos são apresentados, analisados e discutidos. Embora esta metodologia pudesse ser aplicada directamente ao seguimento de múltiplos pontos característicos, foi desenvolvida uma extensão desta metodologia para esse fim. Nesta nova metodologia a sequência de processamento de cada ponto característico é dinâmica e hierárquica. Dinâmica por ser variável ao longo do tempo e hierárquica por existir uma hierarquia de prioridades relativamente aos pontos característicos a serem seguidos e que determina a ordem pela qual esses pontos são processados. Desta forma, o processo dá preferência a pontos característicos cuja localização é mais fácil de prever comparativamente a pontos característicos cujo conhecimento do seu comportamento seja menos previsível. Quando esse valor de prioridade se torna demasiado baixo, esse ponto característico deixa de ser seguido pelo algoritmo. Para se observar o desempenho desta nova abordagem foram utilizadas sequências de imagens onde várias características indicadas pelo utilizador são seguidas. Na parte final deste trabalho são apresentadas as conclusões resultantes a partir do desenvolvimento deste trabalho, bem como a definição de algumas linhas de investigação futura

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction