An apparatus for estimating a position in an automated valet parking system includes a front camera processor processing a front image of a vehicle, a surround view monitor (SVM) processor recognizing a short-distance lane and stop line by processing a surround view image of the vehicle, a map data unit storing a high definition map, and a controller downloading a map including an area set as a parking zone from the map data unit when the entry of the vehicle to a parking lot is identified and correcting a position measurement value of the vehicle by performing map matching based on results of the recognition and processing of the front camera processor and SVM processor and the parking lot map of the map data unit when an automated valet parking start position is recognized based on the recognized short-distance lane and stop line.

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.

An image detection method and apparatus, an electronic device and a storage medium are provided, which relate to the fields of artificial intelligence, deep learning and image processing. The image detection method comprises: performing an acquisition processing on a to-be-detected imaging image to obtain an acquired image; extracting feature data of a target object in the acquired image through a preset image detection network in response to detection processing; performing a multi-dimensional detection comprising at least an imaging content indicator and a color bar indicator on the feature data of the target object according to the image detection network to obtain a detection result; wherein the target object includes imaging contents and color bars which are used to describe color information related to the imaging contents.

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.

The use of multimodal face attributes in facial recognition systems is described. In addition, use of one or more auxiliary attributes, such as a temporal attribute, can be used in combination with visual information to improve the face identification performance of a facial recognition system. In some examples, the use of multimodal face attributes in facial recognition systems can be combined with the use of one or more auxiliary attributes, such as a temporal attribute. Each of these techniques can improve the verification performance of the facial recognition system.

An ophthalmologic apparatus of an embodiment example includes a front image acquiring device, a first search processor, and a second search processor. The front image acquiring device is configured to acquire a front image of a fundus of a subject's eye. The first search processor is configured to search for an interested region corresponding to an interested site of the fundus based on a brightness variation in the front image. The second search processor is configured to search for the interested region by template matching between the front image and a template image in the event that the interested region has not been detected by the first search processor.

The present disclosure provide a method for identifying power equipment targets based on human-level concept learning, including: creating a dataset of power equipment images, and annotating power equipment in power equipment images; training neural network and Bayesian network with the annotated dataset and respectively acquire identification results and conditional probabilities; calculating probabilities of unions with the conditional probabilities; and filtering the identification result corresponding to the highest probability of the union as identification result of the dataset of the power equipment images and complete the identification of the power equipment. The present disclosure combines Mask R-CNN and probabilistic graphical model. The bottom layer uses Mask R-CNN, and the top layer uses Bayesian network to train in identifying power equipment images, so that a small amount of data samples can achieve good recognition, which improved the performance of Mask R-CNN model.

An image reconstruction method, device and apparatus and non-transitory computer-readable storage medium are disclosed. The method may include: determining norms of convolution kernels of each convolutional layer of a deep neural network model; determining the convolution kernels with norms greater than or equal to a preset threshold in each convolutional layer to obtain a target convolution kernel set of each convolutional layer; processing an input image of each convolutional layer by using the convolution kernels in the target convolution kernel set of each convolutional layer respectively, to obtain a first image processing result; obtaining a second image processing result by performing interpolation on an initial image; and determining a fusion result according to the first image processing result and the second image processing result and reconstructing the initial image according to the fusion result.

Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

A map update system includes a data transmission device mounted on a vehicle and a map server which stores map data. The data transmission device generates sensor data representing a road environment of surroundings of the vehicle in a predetermined position, calculates a matching degree between the road environment of the surroundings of the vehicle and a road environment in the predetermined position represented by the map data, and causes a communication circuit mounted on the vehicle to transmit the sensor data and information representing the matching degree to the map server. The map server transmits the map data by utilizing sensor data, among the sensor data received via a communication device, having a matching degree less than a matching degree threshold with a higher priority than sensor data having a matching degree or greater than the matching degree threshold.