Concepts and technologies disclosed herein are directed to image classification attack mitigation. According to one aspect of the concepts and technologies disclosed herein, a system can obtain an original image and reduce a resolution of the original image to create a reduced resolution image. The system can classify the reduced resolution image and output a first classification. The system also can classify the original image via deep learning image classification and output a second classification. The system can compare the first classification and the second classification. In response to determining that the first classification and the second classification match, the system can output the second classification of the original image. In response to determining that the first classification and the second classification do not match, the system can output the first classification of the original image.

A system for detecting clouds and cloud shadows is described. In one approach, clouds and cloud shadows within a remote sensing image are detected through a three step process. In the first stage a high-precision low-recall classifier is used to identify cloud seed pixels within the image. In the second stage, a low-precision high-recall classifier is used to identify potential cloud pixels within the image. Additionally, in the second stage, the cloud seed pixels are grown into the potential cloud pixels to identify clusters of pixels which have a high likelihood of representing clouds. In the third stage, a geometric technique is used to determine pixels which likely represent shadows cast by the clouds identified in the second stage. The clouds identified in the second stage and the shadows identified in the third stage are then exported as a cloud mask and shadow mask of the remote sensing image.

An image matching device that performs matching between a first image and a second image includes: a frequency characteristic acquisition unit configured to acquire a frequency characteristic of the first image and a frequency characteristic of the second image; a frequency characteristic synthesizing unit configured to synthesize the frequency characteristic of the first image and the frequency characteristic of the second image to generate a synthesized frequency characteristic; a determination unit configured to perform frequency transformation on the synthesized frequency characteristic to calculate a correlation coefficient map whose resolution coincides with a target resolution, and perform matching between the first image and the second image based on a matching score calculated from the correlation coefficient map; and a regulation unit configured to regulate the target resolution based on the matching score.

A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.

In one or more implementations, content generated using a client application may be associated with a classification. A number of classifications may be recommended to users of a client application based on alphanumeric characters entered by the users. Additionally, a number of classifications may be recommended to the users of the client application based one or more additional criteria, such as recently used classifications or classifications having at least a threshold frequency of use by additional users of the client application.

A failure identification and handling method includes capturing video including a failure code, identifying the failure code in the video and obtaining an identified failure code, determining failure related information corresponding to the failure code based on the identified failure code and generating display data based on the failure related information, and displaying the failure related information based on the display data. A failure identification and handling system includes an imaging unit, an identification unit, a determination unit, and a display unit. The imaging unit captures video including a failure code. The identification unit identifies the failure code in the video and obtains an identified failure code. The determination unit determines failure related information corresponding to the failure code based on the identified failure code, and generates display data based on the failure related information. The display unit displays the failure related information based on the display data.

Embodiments of the present invention are directed to a computer-implemented method for multiscale representation of input data. A non-limiting example of the computer-implemented method includes a processor receiving an original input. The processor downsamples the original input into a downscaled input. The processor runs a first convolutional neural network (“CNN”) on the downscaled input. The processor runs a second CNN on the original input, where the second CNN has fewer layers than the first CNN. The processor merges the output of the first CNN with the output of the second CNN and provides a result following the merging of the outputs.

Concepts and technologies disclosed herein are directed to image classification attack mitigation. According to one aspect of the concepts and technologies disclosed herein, a system can obtain an original image and reduce a resolution of the original image to create a reduced resolution image. The system can classify the reduced resolution image and output a first classification. The system also can classify the original image via deep learning image classification and output a second classification. The system can compare the first classification and the second classification. In response to determining that the first classification and the second classification match, the system can output the second classification of the original image. In response to determining that the first classification and the second classification do not match, the system can output the first classification of the original image.

Methods of detecting and/or identifying an artificial target within an image are provided. These methods comprise: applying to a region of the image a primary classification algorithm for performing a feature extraction of the image region, the primary classification algorithm being based on a spectral profile defined by one or more spectral signatures with one or more features in at least part of the infrared spectrum; obtaining a relation between the extracted features of the image region and the spectral profile; verifying whether a level of confidence of the obtained relation between the extracted features and the spectral profile is higher than a first predetermined confirmation level; and, in case of positive (or true) result of said verification, determining that the image region corresponds to artificial target to be detected, thereby obtaining a confirmed artificial target. Systems and computer programs are also provided that are suitable for performing said methods.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving mission specific data. Converting the mission specific data into attribute classes recognizable by image recognition classifiers, where the image recognition classifiers are pre-trained to detect objects corresponding to the attributed classes within digital images. Obtaining, from a wearable camera system, low resolution images of a scene and high resolution images of the scene. Detecting, within the low resolution images using a low resolution image classifier, an object that corresponds to one of the attribute classes. In response to detecting the object that corresponds to one of the attribute classes, providing, for presentation to a user of the wearable camera system by a presentation system, an first alert indicating a potential detection of the suspect, and providing the high resolution images to a high resolution image classifier. Obtaining, from the high resolution image classifier, a confirmation of the detected object from the low resolution images. In response to obtaining the confirmation, providing, for presentation to the user by the presentation system, a second alert indicating confirmation that the object has been detected.