Agente de visão semântica para robótica

Mestrado em Engenharia de Computadores e TelemáticaVisão semântica é uma importante linha de investigação na área de visão por computador. A palavra-chave “semântica” implica a extracção de características não apenas visuais (cor, forma, textura), mas também qualquer tipo de informação de “alto-nível”. Em particular, a visão semântica procura compreender ou interpretar imagens de cenas em termos dos objectos presentes e eventualmente das relações entre eles. Uma das principais áreas de aplicação actual é a robótica. Sendo o mundo que nos rodeia extremamente visual, a interacção entre um utilizador humano não especializado e um robô requer que o robô seja capaz de detectar, reconhecer e compreender qualquer tipo de referências visuais fornecidas no âmbito da comunicação entre o utilizador e o robô. Para que tal seja possível, é necessária uma fase de aprendizagem, através da qual várias categorias de objectos são aprendidas pelo robô. Depois deste processo, o robô será capaz de reconhecer novas instâncias das categorias anteriormente aprendidas. Foi desenvolvido um novo agente de visão semântica que recorre a serviços de pesquisa de imagens na Web para aprender um conjunto de categorias gerais a partir apenas dos seus respectivos nomes. O trabalho teve como ponto de partida o agente UA@SRVC, anteriormente desenvolvido na Universidade de Aveiro para participação no Semantic Robot Vision Challenge. O trabalho começou pelo desenvolvimento de uma nova técnica de segmentação de objectos baseada nas suas arestas e na diversidade de cor. De seguida, a técnica de pesquisa semântica e selecção de imagens de treino do agente UA@SRVC foi revista e reimplementada utilizando, entre outros componentes, o novo módulo de segmentação. Por fim foram desenvolvidos novos classificadores para o reconhecimento de objectos. Apreendemos que, mesmo com pouca informação prévia sobre um objecto, é possível segmentá-lo correctamente utilizando para isso uma heurística simples que combina a diversidade da cor e a distância entre segmentos. Recorrendo a uma técnica de agrupamento conceptual, é possível criar um sistema de votos que permite efectuar uma boa selecção de instâncias para o treino de categorias. Conclui-se também que diferentes classificadores são mais eficientes quando a fase de aprendizagem é supervisionada ou automatizada.Semantic vision is an important line of research in computer vision. The keyword “semantic” means the extraction of features, not only visual (color, shape, texture), but also any “higher level” information. In particular, semantic vision seeks to understand or interpret images of scenes in terms of present objects and possible relations between them. One of the main areas of current application is robotics. As the world around us is extremely visual, interaction between a non specialized human user and a robot requires the robot to be able to detect, recognize and understand any kind of visual cues provided in the communication between user and robot. To make this possible, a learning phase is needed, in which various categories of objects are learned by the robot. After this process, the robot will be able to recognize new instances of the categories previously learned. We developed a new semantic vision agent that uses image search web services to learn a set of general categories based only on their respective names. The work had as starting point the agent UA@SRVC, previously developed at the University of Aveiro for participation in the Semantic Robot Vision Challenge. This work began by developing a new technique for segmentation of objects based on their edges and diversity of color. Then, the technique of semantic search and selection of images from the agent UA@SRVC was revised and reimplemented using, among other components, the new object extracting module. Finally new classifiers were developed for the recognition of objects. We learned that, even with little prior information about an object, it is possible to segment it correctly using a simple heuristic that combines colour disparity and distance between segments. Drawing on a conceptual clustering technique, we can create a voting system that allows a good selection of instances for training the categories. We also conclude that various classifiers are most effective when the learning phase is supervised or automated

Small and Dim Target Detection in IR Imagery: A Review

While there has been significant progress in object detection using conventional image processing and machine learning algorithms, exploring small and dim target detection in the IR domain is a relatively new area of study. The majority of small and dim target detection methods are derived from conventional object detection algorithms, albeit with some alterations. The task of detecting small and dim targets in IR imagery is complex. This is because these targets often need distinct features, the background is cluttered with unclear details, and the IR signatures of the scene can change over time due to fluctuations in thermodynamics. The primary objective of this review is to highlight the progress made in this field. This is the first review in the field of small and dim target detection in infrared imagery, encompassing various methodologies ranging from conventional image processing to cutting-edge deep learning-based approaches. The authors have also introduced a taxonomy of such approaches. There are two main types of approaches: methodologies using several frames for detection, and single-frame-based detection techniques. Single frame-based detection techniques encompass a diverse range of methods, spanning from traditional image processing-based approaches to more advanced deep learning methodologies. Our findings indicate that deep learning approaches perform better than traditional image processing-based approaches. In addition, a comprehensive compilation of various available datasets has also been provided. Furthermore, this review identifies the gaps and limitations in existing techniques, paving the way for future research and development in this area.Comment: Under Revie

Automatic aircraft recognition and identification

Aircraft recognition remains a challenging problem despite a great deal of effort to automate the recognition process. The majority of the aircraft recognition methods assume the successful isolation of the aircraft silhouette from the background, and only a few have actually addressed real world concerns, such as occlusion, clutter and shadows. This thesis presents an automatic aircraft recognition system, which shows improved performance with complex images. This system assumes from the start that the image could possibly be degraded, contain occlusions, clutter, camouflage, shadows and blurring. It is designed to tolerate and overcome the degradations at various analysis stages. The first part of the thesis focuses on the generic aircraft recognition problem using a generic description of aircraft parts and the geometric relationships that exist among them. The system implements line groupings in a hierarchical fashion, progressively leading towards a generic aircraft structure. A voting scheme is used to consolidate line groupings belonging to an aircraft while discouraging the formation of spurious line groupings. The aircraft identification process is carried out in the second part of the thesis, where the generically recognised aircraft is matched to model candidates. Model matching is carried out via pixellevel silhouette boundary matching. The system is tested on numerous real aircraft, scaled-down model aircraft and non-aircraft images with adverse image conditions. The developed system achieves a recognition rate of 84% at a false alarm rate of 7% on real aircraft images, and an correct matching rate of about 90% and a false matching rate of 7% on the generically recognised aircraft from model aircraft images

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications