Abstract: Various embodiments include systems and methods of arthropod detection using an electronic arthropod detection device. The electronic arthropod detection device may scan a surface or a subject using one or both of a hyperspectral image sensor or a multispectral image sensor that is sensitive to multiple bands of electromagnetic radiation to detect the presence or likely presence of an arthropod in a region of interest (ROI). A camera sensitive to a visible band of electromagnetic radiation may be used to capture at least one image and provides the image(s) to an object detection model in response to determining that an arthropod is or is likely present in the ROI. Hyperspectral and/or multispectral images may be provided to the object detection model alone or in combination with visible light image(s). A processor may initiate an arthropod detected procedure in response to detecting an arthropod in the ROI.

Abstract: For information about point cloud data, a point cloud data sameness estimation apparatus and a point cloud data sameness estimation system in which accuracy of evaluating sameness is improved are provided.

Abstract: A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and a second branch pose estimation neural network; (iii) generating, by using a three dimensional reconstruction module, three dimensional reconstructions from two dimensional pose estimates produced by the second branch pose estimation neural network; and (iv) projecting, by using a reprojection module, the three dimensional reconstructions to camera image planes of respective image samples, and uploading the reprojections and image samples to an annotation server.

Abstract: Techniques related to automatically segmenting video frames into per pixel dense object of interest and background regions are discussed. Such techniques include applying a segmentation convolutional neural network (CNN) to a CNN input including a current video frame, a previous video frame, an object of interest indicator frame, a motion frame, and multiple feature frames each including features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame to generate candidate segmentations and selecting one of the candidate segmentations as a final segmentation of the current video frame.

Abstract: A high-content imaging system includes a stage, a controller, a machine learning system, and an image generator. The controller receives a request including an output imaging configuration and in response the controller: (1) selects a training model associated with the output imaging configuration, (2) determines an input imaging configuration associated with the training model, and (3) configures the high-content imaging system in accordance with the input imaging configuration. The machine learning system is configured using the training model so when the machine learning system is presented with an image acquired using the input imaging configuration, the machine learning system generates an output image in accordance with the output imaging configuration. The image generator generates an image of a sample on the stage and provides the generated image to the machine learning system and, in response, the machine learning system generates an output image in accordance with the output imaging configuration.

Abstract: In one aspect, the present disclosure relates to a method for or performing single-pass object detection and image classification. The method comprises receiving image data for an image in a system comprising a convolutional neural network (CNN), the CNN comprising a first convolutional layer, a last convolutional layer, and a fully connected layer; providing the image data to an input of the first convolutional layer; extracting multi-channel data from the output of the last convolutional layer; and summing the extracted data to generate a general activation map; and detecting a location of an object within the image by applying the general activation map to the image data.

Abstract: A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

Abstract: A method for obtaining a binocular panoramic image is performed at an electronic apparatus, including: obtaining first and second panoramic images acquired by two panoramic cameras; obtaining at least one group of a first pixel located in the first panoramic image and a second pixel located in the second panoramic image; calculating a distance between the first pixel and the second pixel in each group, and obtaining depth information corresponding to the two panoramic image according to the distance between the first pixel and the second pixel; converting the first panoramic image and the second panoramic image into a first monocular panoramic image and a second monocular panoramic image, respectively in accordance with the corresponding depth information and a preset pupil distance between a first eye and a second eye; and combining a display of the first monocular panoramic image and the second monocular panoramic image in corresponding display regions.

Abstract: Embodiments of the invention are directed to systems, methods, and devices for testing the security of a facial recognition system (FRS). A target image depicting a target person enrolled in the FRS and a tester image depicting a tester may be obtained. A plurality of transformed images may be generated from an image of the target person or the tester image. A processed tester image (e.g., one that is likely to cause the FRS to misclassify) may be identified using the plurality of transformed images, the tester image, and the target image. Data representing a light pattern can be generated using the processed tester image and the light pattern can be projected onto a second person. Another image may be captured of the second person with the light pattern as projected. This image may be provided to the FRS and a remedial action may be performed based on the corresponding output.

Abstract: A recognition method executed by a processor, includes: acquiring positional relation between a plurality of sensors each of which senses a distance to an object; provisionally classifying an orientation of the object relative to each individual sensor included in the sensors into one of a plurality of classifications based on sensing data acquired by the individual sensor; calculating likelihood of each combination corresponding to the positional relation between the sensors based on a result of provisional classification of the orientation of the object relative to the individual sensor; and classifying the orientation of the object corresponding to each individual sensor in accordance with the calculated likelihood of each combination.

Abstract: Methods and apparatus, including computer program products, are provided for machine learning including deep convolutional neural networks. In some example embodiments, there may be provided a method that includes receiving, at a trained machine learning model, a portion of a test image; activating, at the machine learning model, a convolutional result formed based on the portion of the test image, the activation based on neighboring regions in the test image; and providing, by the machine learning model, an activated output. Related systems, methods, and articles of manufacture are also described.

Abstract: A vehicular lighting system includes a light projecting device disposed at each headlight of a vehicle. The light projecting device is part of the respective headlight. The light projecting device, when operated, projects an animated sequence of visual images or a sequence of visual light patterns on a wall in front of the vehicle. The projected animated sequence of visual images or projected sequence of visual light patterns is viewable at the wall by a driver of the vehicle. The light projecting device may include a matrix of light emitting diodes. The visual images or visual light pattern projected by the light projecting device, when operated, may change as distance between the vehicle and the wall decreases.

Abstract: The present disclosure provides a picture processing method and device, including: an integrated circuit chip IC receiving a to-be-processed picture sent by a graphics processor GPU; the IC pre-processing the to-be-processed picture; the IC performing counter-distortion process on the pre-processed picture; and the IC outputting the picture which is subjected to the counter-distortion process for display.

Abstract: A method in an electronic device includes identifying an authorized user of the electronic device being present within an environment of the electronic device. The method also includes identifying at least one other person also being present within the environment of the electronic device. The method then includes selecting, with one or more processors of the electronic device, content previously partially consumed by the authorized user and the at least one other person and presenting, with the one or more processors, the content on a user interface of the electronic device.

Abstract: A handheld imaging device has a data receiver that is configured to receive reference encoded image data. The data includes reference code values, which are encoded by an external coding system. The reference code values represent reference gray levels, which are being selected using a reference grayscale display function that is based on perceptual non-linearity of human vision adapted at different light levels to spatial frequencies. The imaging device also has a data converter that is configured to access a code mapping between the reference code values and device-specific code values of the imaging device. The device-specific code values are configured to produce gray levels that are specific to the imaging device. Based on the code mapping, the data converter is configured to transcode the reference encoded image data into device-specific image data, which is encoded with the device-specific code values.

Abstract: A handheld imaging device has a data receiver that is configured to receive reference encoded image data. The data includes reference code values, which are encoded by an external coding system. The reference code values represent reference gray levels, which are being selected using a reference grayscale display function that is based on perceptual non-linearity of human vision adapted at different light levels to spatial frequencies. The imaging device also has a data converter that is configured to access a code mapping between the reference code values and device-specific code values of the imaging device. The device-specific code values are configured to produce gray levels that are specific to the imaging device. Based on the code mapping, the data converter is configured to transcode the reference encoded image data into device-specific image data, which is encoded with the device-specific code values.

Abstract: Provided are a disease region extraction apparatus, a disease region extraction method, and a disease region extraction program that can extract an infarction region even in an image in which it is difficult to prepare a large amount of data indicating a correct infarction region. A disease region extraction apparatus includes: an image acquisition unit that acquires a first image obtained by capturing an image of a subject that has developed a disease; an estimated image derivation unit that estimates a second image, whose type is different from the type of the first image, from the first image to derive an estimated image; and a disease region extraction unit that extracts a disease region from the estimated image.

Abstract: A method of operation of a compute system includes: capturing an image of a surface of a tire; identifying a tire wear in the image; categorizing the tire wear as a bald region, a crack, a foreign object, low tread, or a combination thereof; generating a wear report includes identifying the tire wear as the bald region, the crack, the foreign object, low tread, or the combination thereof; and transferring the wear report for displaying on a display.

Abstract: A dual sensor imaging system and an imaging method thereof are provided. The method includes: identifying an imaging scene; controlling a color sensor and an IR sensor to respectively capture color images and IR images by adopting capturing conditions suitable for the imaging scene; calculating a signal-to-noise ratio (SNR) difference between each color image and the IR images, and a luminance mean value of each color image; selecting the color image and IR image captured under capturing conditions of having the SNR difference less than an SNR threshold and the luminance mean value greater than a luminance threshold to execute a feature domain transformation to extract partial details of the imaging scene; and fusing the selected color image and IR image to adjust the partial details of the color image according to a guidance of the partial details of the IR image to obtain a scene image with full details.

Abstract: This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize a neural network architecture to train a domain migration function and a hashing function. The domain migration function is configured to transform sketches into synthetic images, and the hashing function is configured to generate hash codes from synthetic images and authentic images in a manner that preserves semantic consistency across the sketch and image domains. The hash codes generated from the synthetic images can be used for accurately identifying and retrieving authentic images corresponding to sketch queries, or vice versa.