Abstract: The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

Abstract: Free space machine interface and control can be facilitated by predictive entities useful in interpreting a control object's position and/or motion (including objects having one or more articulating members, i.e., humans and/or animals and/or machines). Predictive entities can be driven using motion information captured using image information or the equivalents. Predictive information can be improved applying techniques for correlating with information from observations.

Abstract: Systems and methods are described herein for analyzing the application of adhesives. An adhesive comprising a fluorescent property is applied to a component. The component is illuminated using one or more of wavelengths of light from a light source, wherein the adhesive is configured to absorb the one or more wavelengths of light and fluoresce in response. An image of the component is captured using a camera while the component is illuminated using the light source. One or more fluorescence characteristics from the image are determined and a state of the applied adhesive is determined based on the one or more fluorescence characteristics.

Abstract: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

Abstract: An information processing apparatus, comprising: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: acquire observation information obtained through observation of a target region from a flying object flying in outer space; classify the target object by inputting the observation information acquired to a classifier so trained as to output a classification result obtained by classifying a target object present in the target region if the observation information is input; accept designation input for designating the target object; and output the observation information including a classification result of the target object designated.

Abstract: In implementations of the subject matter described herein, a solution for object detection is proposed. First, a feature(s) is extracted from an image and used to identify a candidate object region in the image. Then another feature(s) is extracted from the identified candidate object region. Based on the features extracted in these two stages, a target object region in the image and a confidence for the target object region are determined. In this way, the features that characterize the image from the whole scale and a local scale are both taken into consideration in object recognition, thereby improving accuracy of the object detection.

Abstract: A method for enhancing a set of object scan images may be provided. The set of object scan images comprises at least two scan images. The method comprises determining first distance and determining an interpolated scan image, by applying an interpolation algorithm comprising determining any pixel of the interpolated scan image as a white pixel if the corresponding pixels on the scan images are both white. The same applies to black pixels. The method may further comprise determining any pixel which corresponding pixels are black on one of the two image scans and white on the other image scans as white (black) if a predefined percentage of the directly surrounding pixels is white (black) in the interpolated scan image. Furthermore, the method comprises inserting the interpolated scan image between the first scan image and the second scan image and repeating the above steps until a stop condition is met.

Abstract: Various embodiments of present invention provide systems, methods, and computer-program products containing executable code for processing a package for shipping with a common carrier. Various embodiments include a wireless computing device that may be one of several types of devices such as smartphones, mobile telephones, mobile computers, portable digital assistants, laptop computers, gaming devices, electronic tablets, or other types of similar devices that may be used to facilitate the shipping and expedited tracking of objects having a tracking number via multimedia capture and processing.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from a user device. The image may be a screenshot captured by the user device from a display. The computing device may classify the image based on features present in the image and detect a salient object contained within the image. The computing device may identify the object in the image and one or more sources of the object in the image.

Abstract: The method includes: determining a reference sensor from at least two sensors and sampling moments of the reference sensor; when obtaining observation data of a sensor K at a moment t1, determining a type of the sensor K; if the sensor K is a first sensor of a first type, determining a sampling moment T1 that is closest to t1 and an interpolation coefficient ?1, and calculating first rotation data and first translation data that are at T1, and a first covariance matrix; if the sensor K is a second sensor of a second type, determining two sampling moments T2 and T3 that are closest to t1 and interpolation coefficients ?2 and ?3, and calculating second rotation data and second translation data that are of the second sensor from T2 to T3, and a second covariance matrix; merging rotation data, translation data, and covariance matrices that are at the sampling moments.

Abstract: ADVANCED RESPONSE PROCESSING IN WEB DATA COLLECTION discloses processor-implemented apparatuses, methods, and systems of processing unstructured raw HTML responses collected in the context of a data collection service, the method comprising, in one embodiment, receiving raw unstructured HTML documents and extracting text data with associated meta information that may comprise style and formatting information. In some embodiments data field tags and values may be assigned to the text blocks extracted, classifying the data based on the processing of Machine Learning algorithms. Additionally, blocks of extracted data may be grouped and re-grouped together and presented as a single data point. In another embodiment the system may aggregate and present the text data with the associated meta information in a structured format. In certain embodiments the Machine Learning model may be a model trained on a pre-created training data set labeled manually or in an automatic fashion.

Abstract: A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

Abstract: Embodiments of a system and method for sorting and delivering articles in a processing facility based on image data are described. Image processing results such as rotation notation information may be included in or with an image to facilitate downstream processing such as when the routing information cannot be extracted from the image using an unattended system and the image is passed to an attended image processing system. The rotation notation information may be used to dynamically adjust the image before presenting the image via the attended image processing system.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from a user device. The image may be a screenshot captured by the user device from a display. The computing device may classify the image based on features present in the image and detect a salient object contained within the image. The computing device may identify the object in the image and one or more sources of the object in the image.

Abstract: A device configured to emulate an actor in a correlithm object processing system includes a memory and a processor. The memory stores an actor table that includes a string correlithm object comprising a plurality of sub-string correlithm objects. The actor is implemented by the processor and receives an input correlithm object, determines n-dimensional distances between the input correlithm object and at least a portion of the plurality of sub-string correlithm objects, and identifies a sub-string correlithm object from the actor table with the shortest determined n-dimensional distance. The actor outputs the real-world output value associated with the identified sub-string correlithm object in the actor table.

Abstract: A weight-bearing stance corrected foot orthotic (12) is adapted to be used on the foot (2) of a anaesthetised patient in the supine or prone position, the weight bearing stance corrected foot orthotic (12) adapted to dorsiflex the toes of the forefoot of the patient an angle of between 30° to 50° to the plane of the foot (2). A surgical foot orthotic (12) is formed by the steps of disposing a patient's foot (2) or both feet in a weight bearing neutral position and dorsiflexing the forefoot or forefeet of the patient such that patient's foot (2) or feet are in or towards the Windlass configuration. A first orthotic (5) correcting the stance of the patient in the weight bearing position with dorsiflexed forefoot is formed and this is scanned to form an electronic image of the corrective load bearing surface.

Abstract: Generally, an information processing device according to one embodiment includes storage, a receiver, a detector, an extractor, and a position specifier. The storage stores therein, imaging positional information indicating, identification information on a landmark set, and registration landmark information, in association with one another. The receiver receives a captured image for position specification from an onboard device. The extractor extracts imaging landmark information serving as information on a detected landmark from the captured image for position specification. The position specifier specifies the position indicated by the imaging positional information associated with the identification information on the landmark set including the registration landmark information similar to the imaging landmark information as the position of a vehicle at time when the captured image for position specification is taken.

Abstract: Embodiments of systems and methods for detecting expected user intervention across multiple blades during a Keyboard, Video, and Mouse (KVM) session are discussed. In an embodiment, a chassis may include an Enclosure Controller (EC) coupled to a plurality of Information Handling Systems (IHSs) in a chassis, the EC comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the EC to: select a first IHS to initiate a first KVM session; register for a notification from the first IHS while the first IHS performs one or more operations; switch to a second IHS to initiate a second KVM session; and during the second KVM session, receive the notification.

Abstract: Methods, systems, and computer program products for determining model-related bias associated with training data are provided herein. A computer-implemented method includes obtaining, via execution of a first model, class designations attributed to data points used to train the first model; identifying any of the data points associated with an inaccurate class designation and/or a low-confidence class designation; training a second model using the data points from the dataset, but excluding the identified data points; determining bias related to at least a portion of those data points used to train the second model by: modifying one or more of the data points used to train the second model; executing the first model using the modified data points; and identifying a change to one or more class designations attributed to the modified data points as compared to before the modifying; and outputting identifying information pertaining to the determined bias.

Abstract: Systems and methods for correlating item data are disclosed. A system for correlating item data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving text and image data associated with a reference item from a remote device; converting, using a computer-modeled embedding layer, at least one image to an image embedding; comparing the image embedding to reference embeddings stored in a database; selecting a subset of the candidate item text as candidate text data based on the comparison; selecting a subset of the candidate item images as candidate image data based on the comparison; selecting a text correlation model; determining a first similarity score; selecting an image correlation model; determining a second similarity score; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.