Abstract: A biometric imaging device is provided with: placement unit that supports a biological object, imaging unit that takes images of the biological object, and determining unit that determines whether or not the biological object is placed on the placement unit, based on whether or not a feature of the biological object has changed at a place where the placement unit and the biological object are in contact, in the image(s).

Abstract: The present invention relates to an intelligent recognition method for job contents of barbershop employees, which belongs to the technical field of machine vision, comprising the following steps: S1: setting installation locations and installation conditions for collection devices; S2: establishing a label set of barbershop employee faces and customer faces, and training a face recognition model; S3: establishing an action label set related to items, tools and people, and training a barbershop action behavior recognition model; S4: conducting action behavior recognition for a practical barbershop service scenario by using the trained face recognition model and the barbershop action behavior recognition model; and constructing behavior sequences of “action pairs” involving elements from customers, employees and actions; S5: establishing a job content recognition label; and constructing a deep neural network model for job content recognition to determine service job contents of barbershop employees for custome

Abstract: An infrared vision positioning method for concrete pumping liquid level in tube of steel tubular arch bridge is provided. In this method, the perspective geometry algorithm is used to construct the pose transformation matrix to obtain the relative pose between the camera and the arch bridge steel pipe, the image is transformed into orthography by perspective transformation, and the real coordinates of the liquid level in the infrared image can be directly solved by the scale, so as to realize the accurate positioning of the liquid level; the proposed method is simple in structure and can realize real-time operation, which greatly improves the calculation efficiency of coordinates in infrared images, compared with the conventional knocking method, the advantage is that it does not require staff in an overhead operating, and the feedback of the liquid level position can be faster and more efficient.

Abstract: Methods and systems for identifying areas of interest in an image are disclosed. To manage identification of areas of interest in an image, a trained inference model may generate inferences based on the pixels of the image. The inferences may include areas of interest that contributed to the generation of the inferences. Some areas of interest may be highly relevant to the inferences and may be classified as primary areas of interest. Auxiliary areas of interest may also be identified using a trained inference model. Auxiliary areas of interest may be obtained by calculating gradients for each pixel that contributed to the identification of the primary areas of interest. By rank ordering the pixels, pixels with the highest contribution to the identification of the primary areas of interest may be identified. Proximate groupings of these pixels may be classified as auxiliary areas of interest in the image.

Abstract: A system and method for image correction is provided. The method includes: receiving an original image including a metal artifact; pre-correcting the original image; generating correction data based on the original image and the pre-corrected image; weighting the original image and the pre-corrected image based on the correction data and a regularity degree of the metal artifact, the regularity degree of the metal artifact being associated with actual projection data of the metal artifact and ideal projection data of the metal artifact; and generating a corrected image based on the weighting.

Abstract: The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Abstract: Provided is an information processing method that includes: performing a conversion process on the first feature information to obtain a first conversion result; performing the conversion process on the second feature information to obtain a second conversion result; performing a projection process on the first conversion result to obtain a first projection result; performing the projection process on the second conversion result to obtain a second projection result; calculating an error between the first projection result and the second projection result; and training the second model to reduce the error. The conversion process produces an error between the first projection result and the second projection result that is greater than the error between a projection result obtained by performing the projection process on the first feature information and a projection result obtained by performing the projection process on the second feature information.

Abstract: A first image frame in a first resolution format comprising image signal intensity values mapped to first pixel locations has sampling points offset from the centers of first pixel locations according to associated jitter vectors. The image signal intensity values are mapped to second pixel locations in a second image frame in a second resolution format based at least in part on the jitter vectors, the second resolution format being higher resolution than the first resolution format. The mapped image signal intensity values are combined with image signal intensity values of an accumulated history image buffer according to coefficients predicted by a neural network such as based on magnitudes of the jitter vectors. Interpolated pixel image intensities are added to the accumulated history buffer for empty or null pixel locations in the second image frame in the second image format. Upsampling artifacts such as checkerboarding and aliasing are reduced.

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

Abstract: A method includes acquiring an MRI complex signal having a plurality of complex echoes during an SWI sequence. The method includes phase filtering each complex echo of the plurality of complex echoes. The method also includes generating a respective phase image and a respective magnitude image from each phase filtered complex echo. The method further includes combining separately the respective magnitude images of the plurality of complex echoes with each other to generate a combined magnitude image and the respective phase images of the plurality of complex echoes with each other to generate a combined phase image. The method includes generating a complex image from both the combined magnitude image and the combined phase image. The method includes utilizing a deep learning-based denoising network to denoise the complex image to generate a denoised complex image.

Abstract: A control device 20 includes an image acquisition unit 203 configured to acquire a radiographic image obtained by irradiating a subject F with radiation and capturing an image of the radiation passing through the subject F, a noise map generation unit 204 configured to derive an evaluation value obtained by evaluating spread of a noise value from a pixel value of each pixel in the radiographic image on the basis of relationship data indicating a relationship between the pixel value and the evaluation value and generate a noise map that is data in which the derived evaluation value is associated with each pixel in the radiographic image, and a processing unit 205 configured to input the radiographic image and the noise map to a trained model 207 constructed in advance through machine learning and execute image processing of removing noise from the radiographic image.

Abstract: This invention provides a system and a computer implemented method to match gemstones with records in a gemstone database. In one embodiment, said computer implemented method comprises the steps of: a) Acquiring a source image of a gemstone; b) Receiving an input for identifying a corresponding gemstone in said gemstone database; c) Retrieving a set of reference data; d) Extracting gemstone features from said source image; e) Performing alignment of said gemstone features with said set of reference data and performing a homography transformation to map said gemstone features onto a standard plane to obtain a transformed source image from said source gemstone; f) Obtaining a transformed reference image from said set of reference data; g) Computing a similarity value by matching said transformed source image against said transformed reference image; and h) Determining whether said gemstone in question matches said corresponding gemstone based on said similarity value.

Abstract: A computer-implemented method is provided for processing images. The method can include down-sampling a plurality of first images having a first resolution for obtaining a plurality of second images having a second resolution and training an artificial neural network model to process an input image and output an output image having a higher resolution than the input image.

Abstract: A method for identifying traffic sign is provided. The method obtains an original image of a traffic sign, and performs an image enhancing processing on the original image to form an enhanced image. The method performs a first conversion on the original image and the enhanced image to together form a first matrix of image data, and identifies a position of the traffic sign accordingly. The method further masks the original image and the enhanced image respectively to form a first identified region of the original image and a second identified region of the enhanced image accordingly. The method further performs a second conversion on the first identified region and the second identified region to together form a second matrix of the identified region of the image data, and identifies passage instruction information of the traffic sign accordingly. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: An image restoration method and apparatus are provided. The image restoration method includes determining auxiliary data corresponding to a plurality of filter kernels by filtering target data with the plurality of filter kernels, determining new input data by combining the auxiliary data with at least some input data of layers of a neural network-based image restoration model, generating, based on the new input data, a restored image of the input image by executing the neural network-based image restoration model, wherein the filter kernels are not part of the neural network-based image restoration model.

Abstract: Methods and systems for identifying quantisation parameters for a Deep Neural Network (DNN). The method includes determining an output of a model of the DNN in response to training data, the model of the DNN comprising one or more quantisation blocks configured to transform a set of values input to a layer of the DNN prior to processing the set of values in accordance with the layer, the transformation of the set of values simulating quantisation of the set of values to a fixed point number format defined by one or more quantisation parameters; determining a cost metric of the DNN based on the determined output and a size of the DNN based on the quantisation parameters; back-propagating a derivative of the cost metric to one or more of the quantisation parameters to generate a gradient of the cost metric for each of the one or more quantisation parameters; and adjusting one or more of the quantisation parameters based on the gradients.

Abstract: An encoding apparatus partitions a digital image into multiple regions for subsequent encoding. A first encryption code is associated with a first region, a second encryption code is associated with a second region and the first code, and a third code is associated with the first code, the second code and a third region. An authentication apparatus authenticates the digital image in an inverse process.

Abstract: A method of obtaining X-Model/Plus-Model filter by combining and then mathematical reducing SSF (Sharpening Spatial Filter) and CF (Clamp Filter) and scaling high quality video with this filter in real time on field programmable gate array (FPGA) is provided. With the method, the developed X-Model/Plus-Model filter was applied before the application of interpolation to increase the quality of the scaled video. The said filter is applied in real time with advantages such as low computational complexity, low memory requirement, low power and low resource consumption, and high operating frequency.

Abstract: Aspects of the present invention relate to a computer-implemented training method for training a segmentation model to segment an image. The method includes receiving a plurality of training data sets each including image data representing an image comprising a foreground and a background; and sample image data comprising one or more sample image occurring in the background of the image. The image data is captured by at least one visible electromagnetic radiation imaging device. The method includes processing each training data set using the segmentation model. The processing of each training data includes supplying the sample image data to the segmentation model; and segmenting the image data to generate a candidate segmentation in dependence on the sample image data. An error is determined for the candidate segmentation. The segmentation model is updated in dependence on the determined error.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a denoising neural network and, once the denoising neural network is trained, generating new images using the denoising neural network. In particular, the described techniques include obtaining data specifying a trained initial denoising neural network, obtaining training data, and training, using the training data and on a denoising objective, a target denoising neural network. The target denoising neural network includes the core-subnetwork of the trained initial denoising neural network, but also includes a target encoder and decoder neural networks that are not included in the trained initial denoising neural network. By training a target neural network that includes the core-subnetwork of a trained initial denoising neural network, the system achieves stable training of a large-scale target denoising neural networks that can be used to generate high resolution images.