Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.

Abstract: Disclosed are a method and apparatus for training a classification model and a method and apparatus for classifying. A method for classifying comprises: extracting a feature from to-be-tested information inputted to a classification model having been trained; compressing the extracted feature into a low dimensional hidden feature capable of representing the to-be-tested information; performing decompression on the hidden feature to obtain a decompressed feature; performing rebuilding on the to-be-tested information based on the decompressed feature, to obtain reconstructed to-be-tested information; judging, based on a rebuild loss between the to-be-tested information and the reconstructed to-be-tested information, whether the to-be-tested information belongs to a known class or an unknown class; and performing classification on the to-be-tested information, via the classification model having been trained, in a case where it is determined that the to-be-tested information belongs to a known class.

Abstract: Methods, systems and programs for denoising a signal using discrete wavelet transformation are provided. For example, a method for denoising a signal may include determining a number of resolution levels to denoise, determining variable threshold(s) for each resolution level, applying the determined variable threshold(s) to denoise at least a detail component of each of the determined resolution levels. Each variable threshold includes a separately determined lower threshold and upper threshold. The method for denoising a signal may further include transforming, using an inverse discrete wavelet transformation, at least the denoised detail component for each of the determined resolution levels into a denoised signal.

Abstract: Information processing apparatus includes an input unit configured to input a captured image capturing a target object in a real environment, an obtaining unit configured to obtain an illumination condition in the real environment; a generation unit configured to generate a variation image as an image of a registration object reflecting the illumination condition based on three-dimensional data of the registration object and the obtained illumination condition, and a collation unit configured to collate the registration object with the target object based on a feature amount of the variation image and a feature amount of the captured image.

Abstract: Systems and methods for color selection are provided and include a mobile device having a mobile application configured to access a social media platform, retrieve a plurality of images from the social media platform, determine a dominant color for each image of the plurality of images, determine a closest matching paint color for the dominant color for each image, and display at least one of a color name and a color code associated with the closest matching paint color for the dominant color for each image.

Abstract: A method of reconstructing a three dimensional image using a structured light pattern system is provided as follows. A class identifier of an observed pixel on a captured image by a camera is extracted. The observed pixel has a coordinate (x, y) on the captured image. A first relative position of the x coordinate of the observed pixel in a tile domain of the captured image is calculated. A second relative position of one of a plurality of dots in a tile domain of a reference image using the extracted class identifier is calculated. A disparity of the observed pixel using the first relative position and the second relative position is calculated.

Abstract: Aspects of the disclosure provide for mechanisms for character recognition using neural networks. A method of the disclosure includes assigning, using a first-level classifier of a grapheme classifier, an input grapheme image to a first grapheme cluster of a plurality of grapheme clusters, wherein the first grapheme cluster comprises a first plurality of graphemes; selecting, by a processing device, a classifier from a plurality of second-level classifiers of the grapheme classifier based on the first grapheme cluster, wherein the selected classifier is trained to recognize the first plurality of graphemes; and processing the input grapheme image using the selected classifier to recognize at least one character in the input grapheme image.

Abstract: An image processing apparatus including a pixel shifting super-resolution image generation circuit configured to generate a high-resolution composite image from a plurality of images, an evaluation area setting circuit configured to set a plurality of evaluation areas within an area extraction range of the composite image, a synthesis accuracy evaluation circuit configured to evaluate, for each of the plurality of evaluation areas, a pixel filling rate for the evaluation area and calculate a plurality of pixel filling rate evaluation values, and a determination circuit configured to determine a determination area from among the plurality of evaluation areas based on the plurality of pixel filling rate evaluation values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the identity of a user. In one aspect, a method comprises: obtaining a multi-spectral image that depicts an eye of a user, wherein the multi-spectral image comprises a plurality of registered two-dimensional channels, and each two-dimensional channel corresponds to a different spectrum of the multi-spectral image; processing the multi-spectral image using an encoder neural network to generate a fused image, wherein the fused image has a single two-dimensional channel; determining a set of features characterizing the eye of the user from the fused image; and determining an identity of the user based at least in part on the set of features characterizing the eye of the user.

Abstract: In one embodiment, a method for generating completed frames from sparse data may access sample datasets associated with a sequence of frames, respectively. Each sample dataset may comprise incomplete pixel information of the associated frame. The system may generate, using a first machine-learning model, the sequence of frames, each having complete pixel information, based on the sample datasets. The first machine-learning model is configured to retain spatio-temporal representations associated with the generated frames. The system may then access a next sample dataset comprising incomplete pixel information of a next frame after the sequence of frames. The system may generate, using the first machine-learning model, the next frame based on the next sample dataset.

Abstract: An electronic device for object tracking based on a user-specified initialization point is provided. The electronic device stores a sequence of image frames, which includes a first image frame and a second image frame. The electronic device estimates a set of feature correspondences between a first set of features points in the first image frame and a second set of feature points in the second image frame. The electronic device generates different first motion-estimate models for different groups of feature correspondences of the set of feature correspondences and further estimates, from different groups of feature correspondences, a plurality of inlier feature correspondences that correspond to the object of interest in the first image frame and the second image frame. The electronic device generates a second motion-estimate model as an optimal motion-estimate model and tracks the object of interest in the sequence of frames, based on the second motion-estimate model.

Abstract: Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.

Abstract: A method for transferring a container for holding one or more articles is provided. The method includes transferring the container using a transferring mechanism to a position which is adjacent to a destination space. The method further includes recording an image of the destination space before the container is deposited to the destination space. The method also includes performing an image analysis of the image and determining if the container is able to be sent to the destination space according to a result of the image analysis of the image.

Abstract: Representative embodiments disclose mechanisms to create a text stream from raw OCR outputs. The raw OCR output comprises a plurality of bounding boxes, each bounding box defining a region containing text which has been recognized by the OCR system. A weight matrix is calculated that comprises a weight for each pair of bounding boxes. The weight representing the probability that a pair of bounding boxes belongs to the same cluster. The bounding boxes are then clustered along the weights. The resulting clusters are first ordered using an ordering criteria. The bounding boxes within each cluster are then ordered according to a second ordering criteria. The ordered clusters and bounding boxes are then arranged into a text stream.

Abstract: Embodiments of the present disclosure pertain to systems and method for extracting data from an image. In one embodiment, a method of extracting data from an image comprises receiving, from an optical character recognition (OCR) system, OCR text in response to sending an image to the OCR system. The OCR text comprises a plurality of lines of text. Each line of text is classified as either a line item or not a line item using a machine learning algorithm, and a plurality of data fields are extracted from each line of text classified as a line item.

Abstract: Forensic method for identifying forged documents. For each of a stream of incoming jpeg images, using a processor configured for determining whether jpeg image/s is a replacement forgery by determining whether a first portion of individual image which resides at a known location (known likely to be replaced by forger) within the individual jpeg image has been replaced, including: indicator, face-djpg, for the first portion at known location; computing indicator, aka nonface-djpg, for a second portion of individual image which resides at a comparison location within the jpeg image known as unlikely to be replaced by a forger; and determining whether face-djpg and nonface-djpg fulfill predetermined logical criterion and deciding whether the individual jpeg image is a replacement forgery accordingly.

Abstract: Working efficiency in position matching of multiple point cloud data displayed on a display is improved. A point cloud data processing device includes a highlight controlling part. In a condition in which a first point cloud containing multiple markers for position matching and a second point cloud containing multiple markers for position matching are displayed on a display, when a marker of one of the first point cloud and the second point cloud is specified, the highlight controlling part highlights a corresponding marker of the other point cloud.

Abstract: Disclosed are an inspection system and an inspection method of performing image processing on an outline of an inspection object according to whether the inspection object is good or defective, and overlapping and displaying the image-processed outline with reference information for determining whether the inspection object is good or defective. The inspection system includes: a data acquisition unit configured acquire an image of an inspection object by irradiate light on the inspection object; a processing unit configured to detect an outline of the inspection object based on the image data of the inspection object; and an output unit configured to overlap and display the outline with reference information, wherein the processing unit is configured to determine whether the outline is good or defective based on the reference information to perform image processing on the outline according to whether the outline is good or defective.

Abstract: Systems and methods for testing or correcting optical pointing systems are disclosed. An optical pointing system may include an imaging sensor including a field-of-view (FOV) carried by the optical pointing system, at least one collecting device for collecting optical photons and directing the optical photons to the imaging sensor, at least one directing device for directing the at least one collecting device to different pointing vectors, and at least one non-transitory computer-readable storage medium carried by the optical pointing system having instructions encoded thereon that when executed by at least one processor operates to test the optical pointing system by, inter alia, determining a pointing error based, at least in part, on a macro image of a targeted object.

Abstract: Described is a system for determining crop residue fraction. The system includes a color video camera mounted on a mobile platform for generating a two-dimensional (2D) color video image of a scene in front or behind the mobile platform. In operation, the system separates the 2D color video image into three separate one-dimensional (1D) mixture signals for red, green, and blue channels. The three 1D mixture signals are then separated into pure 1D component signals using blind source separation. The 1D component signals are thresholded and converted to 2D binary, pixel-level abundance maps, which can then be integrated to allow the system to determine a total component fractional abundance of crop in the scene. Finally, the system can control a mobile platform, such as a harvesting machine, based on the total component fractional abundance of crop in the scene.