Abstract: An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

Abstract: A computer-based system and method for taking clarity measurements of a gem, and a computer-readable medium having computer-executable instructions, are provided and include receiving a pixilated image of a gem and identifying pixels representing an inclusion. The method and medium further include determining characteristics of the inclusion as a function of the pixels representing the inclusion, and providing a clarity grade based upon the determined characteristics. Also provided is a method for mapping a gem, and a computer-readable medium having computer-executable instructions, which include receiving a pixilated image of a gem having facet edges, and identifying pixels representing the facet edges. The method and medium further include generating a diagram of the gem, such that the diagram is a function of the pixels representing the facet edges, and superimposing the diagram onto the pixilated image.

Abstract: A computer-implemented method is disclosed for image recognition and other applications. The method employs an SVM model and can reduce false negatives and increase recognition accuracies by raising the sample-to-support-vector ratio.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: Implementations relate to removal of one or more images from a view of a plurality of images. In some implementations, a method includes obtaining a plurality of images, programmatically analyzing the plurality of images to determine a plurality of image features, and determining one or more image categories for the plurality of images based on the image features. The method further includes identifying a subset of the plurality of images based on the image categories, wherein each image of the subset is associated with an image category for archival. The method further includes causing a user interface to be displayed that includes one or more images of the subset, receiving user input to archive at least one of the one or more images, and in response to the user input, removing the at least one of the images from a view of the plurality of images.

Abstract: Provided is an image processing system which determines at least one object using a low function device and a high function device having higher calculation capacity than the low function device. The low function device includes a part configured to output a first determination result which identifies the objects by applying a first convolution neural network to captured images of the objects, and a part configured to output the captured image to the high function device when the first determination result meets a predetermined condition. The high function device includes a part configured to apply a second convolution neural network preliminarily learned from a sample which is at least partially common to the first neural network to the captured image and to output a second determination result which identifies the object.

Abstract: Methods and electronic devices are provided in which first fingerprint information of a first portion of the user's fingerprint is obtained upon the user's fingerprint contacting the fingerprint sensor. A display object is output within a target area for the fingerprint registration, in response to obtaining the first fingerprint information. A guiding object is output to guide contact of a next portion of the user's fingerprint that is adjacent to the first portion. Next fingerprint information of the next portion of the user's fingerprint is obtained upon the user's fingerprint contacting the fingerprint sensor guided by the guiding object. Upon modifying the display object based on the obtained next fingerprint information, the steps of outputting a guiding object and obtaining a next fingerprint information are repeated until fingerprint information at least partially surrounding the first portion is obtained.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to calculate a feature descriptor for each pixel of an image. The feature descriptor generally comprises sixteen discriminative feature values. The feature values are generally computed based on arithmetic combinations of a number of neighboring pixels at predetermined locations around each pixel. The second circuit may be configured to implement a color classification process combining multiple weak depth-two decision trees in a cascade. The second circuit may generate a plurality of likelihood values expressing a likelihood of a specific color road marking at each pixel location of the image using the feature descriptor of each pixel of the image.

Abstract: A method includes identifying one or more first devices contained in at least one image captured by a mobile device. The method also includes obtaining data associated with at least one of: the one or more first devices and one or more second devices connected to or interacting with the one or more first devices. The method further includes presenting information overlaid over the at least one image by the mobile device to a user, where the presented information includes the data or information based on the data. The presented information varies based on perceived distance or proximity of the mobile device to the one or more first devices.

Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

Abstract: Methods, apparatuses and electronic devices for generating a feature vector, as well as searching methods, apparatuses and electronic devices are disclosed. The method for generating a feature vector includes: acquiring data information; extracting a semantic feature from the data information, to acquire semantic feature information; and acquiring a feature vector of the data information by using a preset function, with the semantic feature information as a parameter. The technical solution identifies picture information by recognizing semantics of image information and matching the semantics of the image information with natural language descriptions. Different from conventional image search schemes of existing search engines, this technical solution does not need to retrieve a text description of image information, but retrieves and identifies images based on the content of the image information. Therefore, results with higher accuracy may be returned compared with the existing text-based image search.

Abstract: A method and system for processing an image operates by: filtering a first real image to obtain a first feature map therefor with performances of image features improved; upscaling the obtained first feature map to improve a resolution thereof, the feature map with improved resolution forming a second feature map; and constructing, from the second feature map, a second real image having enhanced performances and a higher resolution than that of the first real image.

Abstract: There is provided an image processing apparatus in which a part used in predetermined processing is specified with use of a relatively small binarization threshold, from parts that have been converted into black pixels through binarization processing using a relatively large binarization threshold.

Abstract: Aspects of the disclosure provide a method for processing an electronic mirror image. The method can include receiving a face-included image from an electronic mirror, and determining a face model that matches the face-included image. A user identity associated with the matched face model can then be obtained. A notification is sent to a terminal corresponding to the associated user identity. The notification indicates the face-included image has been obtained at the electronic mirror.

Abstract: A human detection system may detect the presence of a human around a shovel by using an image captured by an imaging device that is attached to the shovel and that captures a human from diagonally above. The human detection system may have an extracting part that extracts a part of the captured image as a target image, and an identifying part that identifies whether an image included in the target image extracted by the extracting part is an image of a human by an image processing. A target image region in the captured image corresponds to a virtual plane region having a predetermined size in a real space. In the real space, the virtual plane region faces toward the imaging device and is inclined with respect to a horizontal surface.

Abstract: Embodiments relate to bioimpedence-based spoof detection in an optical biometric reader. For example, embodiments operate in context of a biometric reader having a platen integrated with substantially transparent bioimpedance source and receiver electrodes covered by a substantially transparent protective layer. A multi-frequency input signal can be injected from the source electrode into a purported skin site (through the protective layer), so that a response signal is received by the receiver electrode. The response signal is interrogated to formulate a dispersive bioimpedance response of the purported skin site to the input signal over multiple frequencies. A biometric spoof determination can then be made according to the dispersive bioimpedance response. The spoof determination can be used for spoof detection, presence detection, and/or other functions.

Abstract: Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

Abstract: This disclosure provides an image processing method and processing system. The method includes obtaining an image and selecting a calibration area of the image. The method also includes reading a feature parameter corresponding to the calibration area from a preset segmentation model. The method further includes segmenting the image by using the feature parameter, to generate a segmentation result corresponding to the calibration area. This image segmentation by using the feature parameter obtained from the segmentation model provides a high accuracy segmentation rate and can be applied in wide scenarios.

Abstract: Various techniques are disclosed for systems and methods to provide image resolution enhancement. For example, a method includes: receiving a reference image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; receiving a lower-resolution target image (e.g., an infrared image) of the scene; resizing the target image to a larger size; determining an adaptive-shape neighborhood for each pixel coordinate, wherein the adaptive-shape neighborhood extends from the each pixel coordinate such that those reference image pixels that are within the shape-adaptive neighborhood meet a regularity condition; determining, for each adaptive-shape neighborhood, a local estimate based on those target image pixels that are within the adaptive-shape neighborhood; and aggregating the local estimates associated with the adaptive-shape neighborhoods to provide a global estimate that corresponds to the target image with an improved resolution.

Abstract: An example apparatus for imaging in low-light environments includes a raw sensor data receiver to receive raw sensor data from an imaging sensor. The apparatus also includes a convolutional neural network trained to generate an illuminated image based on the received raw sensor data. The convolutional neural network is trained based on images captured by a sensor similar to the imaging sensor.