29 research outputs found
Polygonal Approximation of Digital Curves Using Evolutionary Programming
El artÃculo propone un método basado en Programación Evolutiva para solucionar la aproximación poligonal de curvas digitales. La solución proporcionada por el método consiste en una secuencia de segmentos de lÃnea que será traducida a primitivas de movimiento Avanzar y Rotar en un robot Cartesiano 2D. El método propuesto encuentra automáticamente el número de segmentos asà como sus puntos iniciales y finales. El método ha sido probado en un conjunto de curvas digitales de prueba que exhibe principalmente dos caracterÃsticas: openess y straightness, en diferentes grados. Se demuestra que el método propuesto proporciona buenos resultados para la aproximación de curvas en el conjunto de pruebas. Presentamos resultados cualitativos y cuantitativos de estas pruebas.<br>This paper proposes an Evolutionary Programming (EP) approach to solve the polygonal approximation of digital curves. The solution provided by the method consists of a sequence of straight line segments to be applied as Advance and Rotate motion primitives of a 2D Cartesian robot. The proposed approach finds automatically the number of segments and the startingand ending points of each of them. We have tested our approach on a test set of digital curves that exhibits two main qualitative features: openess and straightness, in different degrees. We show that our method obtains good results for approximating the curves in the test set. We present both quantitative and qualitative results of these test
Towards Facial Gesture Recognition in Photographs of Patients with Facial Palsy
Humans express their emotions verbally and through actions, and hence emotions play a fundamental role in facial expressions and body gestures. Facial expression recognition is a popular topic in security, healthcare, entertainment, advertisement, education, and robotics. Detecting facial expressions via gesture recognition is a complex and challenging problem, especially in persons who suffer face impairments, such as patients with facial paralysis. Facial palsy or paralysis refers to the incapacity to move the facial muscles on one or both sides of the face. This work proposes a methodology based on neural networks and handcrafted features to recognize six gestures in patients with facial palsy. The proposed facial palsy gesture recognition system is designed and evaluated on a publicly available database with good results as a first attempt to perform this task in the medical field. We conclude that, to recognize facial gestures in patients with facial paralysis, the severity of the damage has to be considered because paralyzed organs exhibit different behavior than do healthy ones, and any recognition system must be capable of discerning these behaviors
Automatic Facial Palsy Diagnosis as a Classification Problem Using Regional Information Extracted from a Photograph
The incapability to move the facial muscles is known as facial palsy, and it affects various abilities of the patient, for example, performing facial expressions. Recently, automatic approaches aiming to diagnose facial palsy using images and machine learning algorithms have emerged, focusing on providing an objective evaluation of the paralysis severity. This research proposes an approach to analyze and assess the lesion severity as a classification problem with three levels: healthy, slight, and strong palsy. The method explores the use of regional information, meaning that only certain areas of the face are of interest. Experiments carrying on multi-class classification tasks are performed using four different classifiers to validate a set of proposed hand-crafted features. After a set of experiments using this methodology on available image databases, great results are revealed (up to 95.61% of correct detection of palsy patients and 95.58% of correct assessment of the severity level). This perspective leads us to believe that the analysis of facial paralysis is possible with partial occlusions if face detection is accomplished and facial features are obtained adequately. The results also show that our methodology is suited to operate with other databases while attaining high performance, even though the image conditions are different and the participants do not perform equivalent facial expressions