Abstract: A method is disclosed of discriminating detected objects in an area with a vision apparatus. The method includes generating image data of a portion of the area using an imaging device of the object detection device, and processing the image data to classify the image data as an imaged scene type selected from a plurality of scene types stored as scene type data in the memory. The method further includes processing the image data using the object identification data to generate object detection data for each object of the plurality of objects located in the portion of the area, each object detection data having a corresponding scene type of the plurality of scene types obtained from the object identification data, and generating user a sensible output only for the object detection data having a corresponding scene type that is the same as the imaged scene type.

Abstract: A system and method to communicate between devices through natural language using instant messaging applications and interoperable public identifiers where the method comprises the stages of receiving an instant messaging module from an instant messaging client, an instant message in natural language, with an interoperable public identifier, identifying said message as a message to be processed because said interoperable public identifier corresponds to the public identifier that uniquely identifies said instant messaging module, processing said instant message and resending said message to a natural language processing module, processing the content of said message in said natural language processing module to translate said content into at least one specific command for a target device and launch the execution of the at least said command in said target device.

Abstract: A system and method to communicate between devices through natural language using instant messaging applications and interoperable public identifiers where the method comprises the stages of receiving an instant messaging module from an instant messaging client, an instant message in natural language, with an interoperable public identifier, identifying said message as a message to be processed because said interoperable public identifier corresponds to the public identifier that uniquely identifies said instant messaging module, processing said instant message and resending said message to a natural language processing module, processing the content of said message in said natural language processing module to translate said content into at least one specific command for a target device and launch the execution of the at least said command in said target device.

Abstract: A Convolutional Neural Network (CNN) includes an initial set of convolutional layers and max pooling units, in which any input is convoluted with the learned image filters and the output is a stack of the different filter responses. Max pooling produces a scaled version of the output. The process can be repeated several times, resulting in a stack of space invariant-scaled images. Since the operation is space invariant, the computations of these layers not need to be recomputed if interested just in certain regions of the image. A Region Of Interest (ROI) Pooling layer is used to select regions to be processed by the set of fully connected layers, which uses the response of the multiple convolutional layers of the network to determine the regions where the objects (of different scales) could be located. This object proposal method is implemented as a Region Of Interest (ROI) Selector.

Abstract: A method is disclosed of discriminating detected objects in an area with a vision apparatus. The method includes generating image data of a portion of the area using an imaging device of the object detection device, and processing the image data to classify the image data as an imaged scene type selected from a plurality of scene types stored as scene type data in the memory. The method further includes processing the image data using the object identification data to generate object detection data for each object of the plurality of objects located in the portion of the area, each object detection data having a corresponding scene type of the plurality of scene types obtained from the object identification data, and generating user a sensible output only for the object detection data having a corresponding scene type that is the same as the imaged scene type.

Abstract: A Convolutional Neural Network (CNN) includes an initial set of convolutional layers and max pooling units, in which any input is convoluted with the learned image filters and the output is a stack of the different filter responses. Max pooling produces a scaled version of the output. The process can be repeated several times, resulting in a stack of space invariant-scaled images. Since the operation is space invariant, the computations of these layers not need to be recomputed if interested just in certain regions of the image. A Region Of Interest (ROI) Pooling layer is used to select regions to be processed by the set of fully connected layers, which uses the response of the multiple convolutional layers of the network to determine the regions where the objects (of different scales) could be located. This object proposal method is implemented as a Region Of Interest (ROI) Selector.