The subject disclosure relates to techniques for detecting an object. A process of the disclosed technology can include steps for receiving three-dimensional (3D) Light Detection and Ranging (LiDAR) data of the object at a first time, generating a first point cloud based on the 3D LiDAR data at the first time, receiving 3D LiDAR data of the object at a second time, generating a second point cloud based on the 3D LiDAR data at the second time, aggregating the first point cloud and the second point cloud to form an aggregated point cloud, and placing a bounding box around the aggregated point cloud. Systems and machine-readable media are also provided.

Disclosed are systems and methods for full body measurements extraction using a mobile device camera. The method includes the steps of receiving one or more user parameters; receiving at least one image containing the human and a background; identifying one or more body features associated with the human; performing body feature annotation on the identified body features for generating an annotation line on each body feature corresponding to a body feature measurement, the body feature annotation utilizing an annotation deep-learning network that has been trained on annotation training data, the annotation training data comprising one or more images for one or more sample body features and an annotation line for each body feature; generating body feature measurements from the one or more annotated body features utilizing a sizing machine-learning module based on the annotated body features and the one or more user parameters; and generating body size measurements by aggregating the body feature measurements for each body feature.

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

Provided are a cutting method, apparatus and system for a point cloud model. In an embodiment, the method includes: using one two-dimensional first cutting window to select a point cloud structure comprising a target object from among one point cloud model; adjusting the depth of the first cutting window, the length, width and depth of the first cutting window constituting one three-dimensional second cutting window, the target object being located in the second cutting window; identifying and marking all point cloud structures in the second cutting window to form a plurality of three-dimensional third cutting windows, the target object being located in one of the third cutting windows; and calculating the volume ratio of the point cloud structure in each third cutting window relative to the second cutting window, and selecting the third cutting window having the largest volume ratio.

An apparatus and method for image processing is disclosed. The method may include receiving an image from a camera sensor, receiving selection of one or more target objects appearing in the image and tracking the one or more target objects over a plurality of subsequently-received images. For the subsequently-received images in turn, the method may include estimating one or more performance metric(s) associated with performing a fill-in processing operation of the one or more tracked target objects and saving the image as an optimised reference image if the respective performance metric(s) indicate an improved performance over that of one or more previously-received images from the time of receiving selection. The method may include performing the fill-in processing operation using one or more of the saved optimised reference images for output to a display screen.

A method for depth image acquisition, a device (10) for depth image acquisition, and an electronic device (100) are provided. The method includes the following. An image of a field is obtained to determine a region of interest (ROI) in the image of the field. A current distance to the ROI is obtained. A time-of-flight depth camera (20) is controlled to obtain a current depth image of the field in response to the current distance being greater than a first distance. Both a dual camera (30) and the time-of-flight depth camera (20) are controlled to obtain the current depth image of the field in response to the current distance being not greater than the first distance.

An object of the present disclosure is to provide a technique capable of acquiring an analysis target region and color information without causing a decrease in extraction accuracy of the analysis target region due to erroneous extraction of a color of a colored label, measuring a solution volume of the specimen, and determining a specimen type. The biological specimen analysis device according to the present disclosure creates a developed view by cutting out a partial region from a color image of a biological sample tube and connecting the partial region along a circumferential direction of the biological sample tube, and extracts a detection target region from the developed view (see FIG. 6B).

Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.

A diagnosis support system includes a processor. The processor is connected to a plurality of classifiers that are different in performance. The processor displays performance information of each of the classifiers side by side, receives a user's selection of the performance information displayed side by side, and inputs an input image to the classifier associated with the performance information selected by the user.

Techniques for reducing a distractor object in a first image are presented herein. A system can access a mask and the first image. A distractor object in the first image can be inside a region of interest and can have a pixel with an original attribute. Additionally, the system can process, using a machine-learned inpainting model, the first image and the mask to generate an inpainted image. The pixel of the distractor object in the inpainted image can have an inpainted attribute in chromaticity channels. Moreover, the system can determine a palette transform based on a comparison of the first image and the inpainted image. The transform attribute can be different from the inpainted attribute. Furthermore, the system can process the first image to generate a recolorized image. The pixel in the recolorized image can have a recolorized attribute based on the transform attribute of the palette transform.