Abstract: Described is a multiple-camera system and process for detecting a user within a materials handling facility and tracking a position of the user as the user moves through the materials handling facility. In one implementation, a plurality of depth sensing cameras are positioned above a surface of the materials handling facility and oriented to obtain an overhead view of the surface of the materials handling facility, along with any objects (e.g., users) on the surface of the materials handling facility. The depth information from the cameras may be utilized to detect objects on the surface of the materials handling facility, track a movement of those objects and determine if those objects are users.

Abstract: Systems and devices including an imaging sensor to capture video sequences in an environment having safety concerns therein. The systems and devices further including a processor to generate driving series based on observations from the video sequences, and generate predictions of future events based on the observations using a dual-stage attention-based recurrent neural network (DA-RNN). The DA-RNN includes an input attention mechanism to extract relevant driving series, an encoder to encode the extracted relevant driving series into hidden states, a temporal attention mechanism to extract relevant hidden states, and a decoder to decode the relevant hidden states. The processor further generates a signal for initiating an action to machines to mitigate harm to items.

Abstract: Systems and methods for detecting, decoupling and quantifying unresolved signals in trace signal data in the presence of noise with no prior knowledge of the signal characteristics (e.g., signal peak location, intensity and width) of the unresolved signals. The systems and methods are useful for analyzing any trace data signals having one or multiple constituent signals, including overlapping constituent signals, and particularly useful for analyzing data signals which often contain an unknown number of constituent signals with varying signal characteristics, such as peak location, peak intensity and peak width, and varying resolutions. A general signal model function is assumed for each unknown, constituent signal in the trace signal data.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR A VIDEO REMOTE DEPOSIT CAPTURE PLATFORM (hereinafter “RDC-Video”) provides a platform for remote deposit by obtaining images of a check from streaming video captured by a video camera, wherein the RDC-Video transforms captured check images and/or entered check deposit information inputs via RDC-Video components into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the RDC-Video may capture images of the check and send them to a financial institution for deposit processing.

Abstract: There is provided a microparticle sorting device that automatically optimizes a fluid stream. There is provided a microparticle sorting device that includes a voltage supply unit that supplies a driving voltage to a vibratory element that applies vibration to an orifice that produces a fluid stream, a charge unit that imparts charge to at least some droplets ejected from the orifice, deflecting plates, arranged opposing each other with the fluid stream S therebetween, that vary a travel direction of the droplets, and a first image sensor that acquires an image of the droplets passing between the deflecting plates.

Abstract: Systems and methods for generating a locally adaptive threshold image for foreground detection performing operations including creating a saliency edge strength image or layer indicating edge or border pixels of the nuclei by performing tensor voting on pixels neighboring the initial edge pixels within an image region to refine true edges are featured. Further, for each of a plurality of regions or blocks of the image, an adaptive threshold image is determined by sampling a foreground pixel and a background pixel for each initial edge pixel or refined edge pixel, generating histograms for both background and foreground saliency (or gradient magnitude) modulated histograms, determining a threshold range for each block of the image, and interpolating the threshold at each pixel based on the threshold range at each block. Comparing the input image with the resulting locally adaptive threshold image enables extraction of significantly improved foreground.

Abstract: An apparatus and method for performing image segmentation. The method includes the steps of filtering an X-ray image of a jaw of a patient to reduce noise. Each image pixel is clustered into one of a lesion region and a background region. Further, the method transforms each pixel included in the clustered image into a neutrosophic domain determined by computing a three-tuple including a first probability corresponding to the pixel being correctly clustered in the lesion region, a second probability corresponding to the pixel being incorrectly clustered, and a third probability corresponding to the pixel being indeterminate, and iteratively computes the first probability, the second probability, and the third probability based on a mean value of pixel intensity of a predetermined window and a homogeneity value. The image is segmented by assigning the indeterminate pixels the lesion region or the background region based on a degree of pixel indeterminacy.

Abstract: Embodiments disclosed herein provide systems and methods for matching trajectories across disjointed video views. In a particular embodiment, a method provides receiving a plurality of tagged trajectories that are tagged with an indicator of which trajectory pairs of the plurality of tagged trajectories are matching trajectory pairs and processing each of the trajectory pairs using each of a plurality of trajectory matching algorithms. The method further provides creating a model for matching trajectories based on the output of each of the plurality of trajectory matching algorithms for each of the pairs. The method further provides receiving a pair of trajectories and processing the pair of trajectories using each of the plurality of the trajectory matching algorithms. The method further provides processing the output of each of the plurality of the trajectory matching algorithms for the pair of trajectories using the model to determine whether the pair of trajectories is matching.

Abstract: Some of pixels in a general image sensor for image capturing are designated as vision pixels used for vision recognition. Optical information obtained only from the vision pixels is used to perform vision recognition in the vision recognition mode. Capturing image data is generated based on optical information obtained from all the pixels in the image data in the image-shooting mode. A digital signal converter that converts optical information obtained only from vision pixels into a digital signal is separately provided in addition to a digital signal converter that converts optical information of all pixels in the image sensor into a digital signal.

Abstract: An image processing device includes: a reference area setting unit which sets a reference area including an indicated segment; an extraction unit which extracts an interest object feature quantity indicating a first feature from the reference area; an interest area setting unit which sets an area of interest in a third image, based on a relationship between a position of a feature point extracted from a feature area which is an area corresponding to an object of interest and included in a second image and a position of a feature point in the third image corresponding to the extracted feature point; and a tracking unit which determines for each of two or more of plural segments included in the area of interest with use of the interest object feature quantity whether the segment is a segment corresponding to the object of interest.

Abstract: A system for thermal transient imaging of an object includes a radiation source configured to irradiate the object with radiation in accordance with an excitation profile. An imaging device includes an array of detector pixels and is configured to detect thermal radiation from a surface of the object. A processor is configured to obtain a series of frames of thermal image data of the surface acquired by the imaging device over time when the object is irradiated by the radiation source. The processor is further configured to process, using the excitation profile, the series of frames of thermal image data to transform the thermal image data to an equivalent wave field representation that represents a series of depth-resolved virtual images of the object.

Abstract: A method is described including storing reference vector data corresponding to user gestures at a plurality of neurons at pattern matching hardware, receiving real time signals from the sensor array and performing gesture recognition using the pattern matching hardware to compare incoming vector data corresponding to the real time signals with the reference vector data.

Abstract: A novel method for simulating radiation dose reduction that enables previews of low-dose x-ray projection images, low-dose computed tomography images and/or cone-beam CT images. Given an existing projection or set of projections of the patient acquired at a nominal dose, the method provides a means to produce highly accurate preview images that accurately reflect the image quality associated with reduced radiation dose. The low-dose preview image accounts for characteristics of the imaging system, including blur, variations in detector gain and electronic noise, and does so in a manner that yields accurate depiction of the magnitude and correlation of image noise in the preview images. A calibration step may be included to establish the system-specific relationship between the mean and variance in detector signal, and incorporate an accurate model for system blur such that correlations in the resulting LDP images are accurate.

Abstract: A learning system (100) includes an information transfer apparatus (10) and a learning processing apparatus (20). The information transfer apparatus (10) includes: an analysis unit (11) that obtains data serving as a learning target, compares the obtained data with a reference model, and assigns, to the data, an index indicating a degree of worthiness of the data as the learning target; and a transmission processing unit (12) that transmits the data to the learning processing apparatus (20) based on a rule that has been set using the index. The learning processing apparatus (20) includes a learning processing unit (21) that updates the model or generates a new model based on the data transmitted from the information transfer apparatus (10).

Abstract: A method and system of selecting a surface finish for a computer designed component associated with an anatomical area of a specific patient. A computer designed component is created. The component includes at least one surface. An interface of finishing software displays an image of the component and the at least one surface. A plurality of finishes available for the at least one surface is displayed. A selection of one of the plurality of finishes for the at least one surface is input by a user. The component data and finishing data may then be sent to a computer aided manufacturing system to manufacture the computer designed component.

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: An image screening apparatus includes an image scanner that scans an object and generates an image of the object; at least one portable image viewing device; and a controller including circuitry configured to transmit the generated image to the at least one portable image viewing device, trigger display of the transmitted image on a screen of at least one of the portable image viewing devices, and match the displayed image with the scanned object. An area of interest of the scanned object can be displayed on one portable image viewing device by hovering the one portable image viewing device over the area of interest.

Abstract: The present document is directed to methods and systems that identify and characterize subimages in images that each includes an image of a human face. In certain implementations, values for attributes, such as age, ethnicity, and gender, are assigned to face-containing subimages by the currently disclosed methods and systems. In these implementations, multiple feature-extractors output feature vectors that are distributed among attribute classifiers which consist of individual classifiers and, more often, multiple individual classifiers within aggregate classifiers. Attribute classifiers return indications of attribute values along with a probability value. Attribute classifiers are ordered with respect to reliability and applied in reliability order to generate attribute-assignment paths through a logical attribute-assignment tree, with uncertain attribute assignments generating multiple lower-level pathways.

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: Approaches provide for identifying text represented in image data as well as determining a location or region of the image data that includes the text represented in the image data. For example, a camera of a computing device can be used to capture a live camera view of one or more items. The live camera view can be presented to the user on a display screen of the computing device. An application executing on the computing device or at least in communication with the computing device can analyze the image data of the live camera view to identify text represented in the image data as well as determine locations or regions of the image that include the representations.