A Novel Approach Based on PCNNs Template for Fingerprint Image Thinning

A PCNNs-based square-and-triangle-template method for binary fingerprint image thinning is proposed. The algorithm is iterative, in which a combined sequential and parallel processing is employed to accelerate execution. When a neuron satisfies the square template, the pixel corresponding to this neuron will be noted during the process and be deleted until the end of the iteration; on the other hand, if a neuron meets a triangle template, it will be removed directly. In addition, this proposed algorithm can be effective for fingerprint thinning without considering the direction. The results showed that, with combined sequential and parallel conditions for border pixels removal, the algorithm could not only speed up the fingerprint thinning process, but also be applied to other common images. Furthermore, this algorithm might be applied to fingerprint identification systems to save the time for identifying and eliminating spurious minutia

EVALUACIÓN DE REDES NEURONALES PULSANTES PARA DETECCIÓN DE CAMBIOS EN IMÁGENES SATELITALES

ResumenExisten diversas aplicaciones dentro del procesamiento digital como el análisis del subsuelo, identificación de cambios en la vegetación después de un fenómeno meteorológico, modificaciones en zonas urbanas, entre otras. Hay una gran variedad de métodos que son aplicados para el análisis de imágenes satelitales como el análisis de textura, la detección de bordes, aplicación de la matriz de co-ocurrencia, etcétera. Otro método usado es el de PCNN (Pulse-Coupled Neural Networks), en el cual cada neurona en la red corresponde a un pixel en escala de grises, estas neuronas entran a un proceso que en unión con un umbral generan un pulso como respuesta. En el presente trabajo se tiene como objetivo la evaluación del método de PCNN para detección de cambios en imágenes satelitales. Primeramente, se hizo el registro de dos imágenes satelitales de años diferentes, posteriormente se seleccionaron las regiones a analizar y a aplicar el método de PCNN con un total de 20 iteraciones por cada región. Tras analizar los resultados obtenidos, se concluye que las iteraciones generadas por el algoritmo de PCNN generan un patrón que es útil para el análisis de cambios estructurales, de igual manera los valores de las gráficas pueden ser analizados para determinar los cambios estructurales.Palabras Claves: Detección de cambios, imágenes satelitales, redes neuronales pulsantes. AbstractThere are various applications within the digital processing such as the analysis of the subsoil, identifying changes in vegetation after a weather phenomenon, changes in urban areas, among others. There are a variety of methods that applied the analysis of satellite images as texture analysis, edge detection, application of co-occurrence matrix, and so on. Another method used is PCNN (Pulse-Coupled Neural Networks), in which each neuron in the network corresponds to a pixel grayscale, these neurons enter a process in conjunction with a threshold generate a pulse in response. In this papier, it has target at evaluating the PCNN method for detecting changes in satellite images. First was the registration of two images from different years, then the regions were selected and analyzed applying the method PCNN a total of 20 iterations for each region. After analyzing the results, it is concluded that the iterations generated by the algorithm PCNN generate a pattern that is useful for the analysis of structural changes, just as the values of the graphs can be analyzed to determine the structural changes. Keywords: Detection of changes, satellite imagery, pulsed neural networks

Selective combination of visual and thermal imaging for resilient localization in adverse conditions: Day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions

Resilient Perception for Outdoor Unmanned Ground Vehicles

This thesis promotes the development of resilience for perception systems with a focus on Unmanned Ground Vehicles (UGVs) in adverse environmental conditions. Perception is the interpretation of sensor data to produce a representation of the environment that is necessary for subsequent decision making. Long-term autonomy requires perception systems that correctly function in unusual but realistic conditions that will eventually occur during extended missions. State-of-the-art UGV systems can fail when the sensor data are beyond the operational capacity of the perception models. The key to resilient perception system lies in the use of multiple sensor modalities and the pre-selection of appropriate sensor data to minimise the chance of failure. This thesis proposes a framework based on diagnostic principles to evaluate and preselect sensor data prior to interpretation by the perception system. Image-based quality metrics are explored and evaluated experimentally using infrared (IR) and visual cameras onboard a UGV in the presence of smoke and airborne dust. A novel quality metric, Spatial Entropy (SE), is introduced and evaluated. The proposed framework is applied to a state-of-the-art Visual-SLAM algorithm combining visual and IR imaging as a real-world example. An extensive experimental evaluation demonstrates that the framework allows for camera-based localisation that is resilient to a range of low-visibility conditions when compared to other methods that use a single sensor or combine sensor data without selection. The proposed framework allows for a resilient localisation in adverse conditions using image data but also has significant potential to benefit many perception applications. Employing multiple sensing modalities along with pre-selection of appropriate data is a powerful method to create resilient perception systems by anticipating and mitigating errors. The development of such resilient perception systems is a requirement for next-generation outdoor UGVs