In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit. The loss computation unit computes a difference between a ground truth label of the image data and the merged score merged by the merging unit as a loss. Then, the parameter correction unit corrects the weight computation parameters so as to reduce the loss.

An apparatus for selecting a training image of a deep learning model and a method thereof are disclosed. The apparatus includes an input device and a controller. The input device receives a simulation image and information about an object in the simulation image from a simulation tool and receives a training image corresponding to the simulation image from an image conversion device. The controller detects a similarity between a structure of the object in the simulation image and a structure of an object in the training image and determines validity of the training image based on the detected similarity.

Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.

The image matching apparatus (2000) acquires a ground-view image (20) and an aerial-view image (30). The image matching apparatus (2000) extracts features from the ground-view image (20). The image matching apparatus (2000) extracts features from the aerial-view image (30). The image matching apparatus (2000) extracts a plurality of partial aerial regions (32) from the aerial-view image (30), and extracts features from each partial aerial region (32). The image matching apparatus (2000) computes, for each partial aerial region (32), a combined aerial feature by combining the features of the partial aerial region (32) and the features of the aerial-view image (30). The image matching apparatus (2000) determines, for each partial aerial region (32), whether the partial aerial region (32) matches the ground-view image (20) by comparing the combined aerial feature of the partial aerial region (32) and the features of the ground-view image (20).

There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining an original image of the semiconductor specimen, the original image having a first region annotated as enclosing a defective feature; specifying a second region in the original image containing the first region, giving rise to a contextual region between the first region and the second region; identifying in a target image of the specimen a set of candidate areas matching the contextual region in accordance with a matching measure; selecting one or more candidate areas from the set of candidate areas; and pasting the first region or part thereof with respect to the one or more candidate areas, giving rise to an augmented target image usable for defect examination on the semiconductor specimen.

A radar image processing device includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: determine a search range based on a reference block and a layover direction, the reference block being set as an area of interest in a radar image generated from data obtained by an imaging radar, the layover direction being a direction in which layover occurs in the radar image and being estimated from an incident direction of an electromagnetic wave used for observation by the imaging radar; extract a similar block that is similar to the reference block and included in the search range by searching the search range; and perform filtering processing for reducing speckles generated in the radar image by using the reference block and the extracted similar block.

Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.

Provided are an image processing apparatus and an operation method of the image processing apparatus. The image processing apparatus includes a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory to, by using one or more convolution neural networks, extract target features by performing a convolution operation between features of target regions having same locations in a plurality of input images and a first kernel set, extract peripheral features by performing a convolution operation of features of peripheral regions located around the target regions in the plurality of input images and a second kernel set, and determine a feature of a region corresponding to the target regions in an output image, based on the target features and the peripheral features.

A method includes obtaining, by a processing device, an impact analysis configuration related to an image sensor operation type for an autonomous vehicle (AV), receiving, by the processing device, image data from a sensing system including at least one image sensor of the AV, causing, by the processing device, fault detection to be performed based on the image data, causing, by the processing device, a fault notification to be generated using the impact analysis configuration, and sending, by the processing device to a data processing system of the AV, the fault notification to perform at least one action to address the fault notification. The fault notification includes a fault summary related to the image sensor operation type.

A hologram detection method according to an aspect of the disclosure, includes: inputting a first image, obtained by capturing a detection object on the basis of first flash intensity, to a neural network model to obtain a first detection result value representing the detection or not of a hologram for each of predetermined at least one detection unit regions; and comparing a threshold value with the first detection result value obtained for each detection unit region to determine the detection or not of a hologram in the first image and a first detection unit region where a hologram is detected.