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Event cameras are a new type of image sensors that output changes in light intensity (events) instead of absolute intensity values. They have a very high temporal resolution and a high dynamic range. In this paper, we propose a method to detect and decode binary square markers using an event camera. We detect the edges of the markers by detecting line segments in an image created from events in the current packet. The line segments are combined to form marker candidates. The bit value of marker cells is decoded using the events on their borders. To the best of our knowledge, no other approach exists for detecting square binary markers directly from an event camera using only the CPU unit in real-time. Experimental results show that the performance of our proposal is much superior to the one from the RGB ArUco marker detector. The proposed method can achieve the real-time performance on a single CPU thread., Comment: An error in the abstract of the IEEE Access version has been corrected in this version. Link to the IEEE Access paper: https://doi.org/10.1109/ACCESS.2021.3058423

espanolEl objetivo de este trabajo fue proponer un prototipo de medicion de color y temperatura construido con los sensores DHT11 de temperatura y TCS230-3200 de color conectados a un Arduino Nano, con una conexion serial se transfirio la informacion a un ordenador con los programas PXL-DAQ y Microsoft Excel® 2013, siendo las mediciones de color registradas en el modelo de color RGB para su conversion al sistema CIE L*a*b*. Un equipo de medicion simultanea de color y temperatura a nivel piloto, como el que se propone, se convierte en una alternativa economica frente a los costosos equipos comerciales. Con el prototipo se analizo la perdida de luminosidad y la variacion temperatura del yacon (Smallanthus sonchifolius), y los cambios de indice de pardeamiento y diferencia de color. Estos mostraron un comportamiento similar al encontrado en la literatura. EnglishThe objective of this work was to propose a color and temperature measurement prototype constructed with the temperature sensor DHT11 and the color sensor TCS230-3200 connected to an Arduino Nano, with a serial connection information was transferred to a computer with PXL-DAQ and Microsoft Excel® 2013 programs, the color measurements were recorded in RGB color model for a conversion to CIE L*a*b* system. A simultaneous color and temperature measurement equipment at pilot level, as the one proposed in this paper, becomes an economical alternative to costly commercial equipment. With the prototype the luminosity loss and changes of temperature of yacon (Smallanthus sonchifolius) were quantified, and the changes of browning indexes and color difference. These parameters presented a behavior similar to that found in the literature.

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features. Usualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.