Abstract: In implementations of systems for single image reflection removal, a computing device implements a removal system to receive data describing a digital image that depicts light reflected by a surface and light transmitted through the surface. The removal system predicts an edge map of a transmitted image for the light transmitted through the surface by processing the data using a first machine learning model trained on a first type of training data. A reflected component is predicted for the light reflected by the surface by processing the data using a second machine learning model trained on a second type of training data. A corrected digital image is generated that does not depict the light reflected by the surface based on the data, the edge map of the transmitted image, and the reflected component.

Abstract: Traditional systems used for fashion attribute detection struggle to generate accurate predictions due to presence of large intra-class and relatively small inter-class variations in data related to the fashion attributes. The disclosure herein generally relates to image processing, and, more particularly, to a method and system for fashion attribute detection. The method proposes F-AttNet, an attribute extraction network to leverage the performance of fine-grained localized fashion attribute recognition. F-AttNet comprises Attentive Multi-scale Feature Encoder (AMF) blocks that encapsulate multi-scale fine-grained attribute information upon adaptive recalibration of channel weights. F-AttNet is designed by hierarchically stacking the AMF encoders to extract deep fine-grained information across multiple scales.

Abstract: A direct structured illumination microscopy (dSIM) reconstruction method is provided. First, a time domain modulation signal is extracted through a wavelet. Then, an incoherent signal is converted into a coherent signal. Next, an accumulation amount at each pixel is calculated. Finally, a super-resolution image is generated by using a correlation between signals at different spatial positions. An autocorrelation algorithm of dSIM is insensitive to an error of a reconstruction parameter. dSIM bypasses a complex frequency domain operation in structured illumination microscopy (SIM) image reconstruction, and prevents an artifact caused by the parameter error in the frequency domain operation. The dSIM algorithm has high adaptability and can be used in laboratory SIM, nonlinear SIM imaging systems, or commercial systems.

Abstract: The present invention facilitates detection of an object imaged by a three-dimensional sensor. The image processing device includes: a distance calculating unit that, on the basis of three-dimensional data acquired by the three-dimensional sensor, calculates a distance between each of points in the three-dimensional data and a reference plane; a distance image creating unit that creates a distance image having pixel values constituted by values each calculated on the basis of the distance calculated by the distance calculating unit; and an image processing unit that carries out image processing on the distance image. The reference plane may be a bearing surface bearing the object or a surface parallel with the bearing surface. The reference plane may also be a surface of the object. The image processing device may include the three-dimensional sensor.

Abstract: A three-dimensional object detecting device generates a mask image that masks regions outside a three-dimensional object candidate region in a difference image of a first overhead image and a second overhead view image for which the imaging locations O are mutually aligned, identifies a near ground contact line of a three-dimensional object based on a masked difference image. The difference image is masked with the mask image, finds an end point of the three-dimensional object based on the masked difference image, identifies the width of the three-dimensional object based on a distance between a non-masking region boundary and the end point of the three-dimensional object in the mask image, identifies a far ground contact line of the three-dimensional object based on the width of the three-dimensional object and the near ground contact line, and identifies the location of the three-dimensional object in the difference image based on the near ground contact line and the far ground contact line.