The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

Devices, systems, and methods of generating and providing a target topographic map for finishing a photomask blank are disclosed. A method includes receiving topographic data corresponding to an uncompleted photomask blank, receiving functional specifications for flatness of an acceptable photomask blank, and generating the target topographic map for first and/or second major surfaces of the blank, which provides instructions for removing material from the first and/or second major surfaces such that the first and second major surfaces achieve a flatness that passes each functional specification. The amount of material removed reflects a reduction in material necessary to pass the functional specifications. The method further includes transmitting the target topographic map to the finishing device to utilize a finishing technique to implement changes to the photomask blank according to the target topographic map by removing the material from the photomask blank to achieve a photomask blank that passes the functional specifications.

The present disclosure provides an object detection method and an object detection device. The object detection device includes: a heterogeneous processor and a memory, the heterogeneous processor including: a processing unit and a programmable logic unit, wherein the programmable logic unit is configured to receive a to-be-detected image, perform feature extraction on the to-be-detected image, and write an extracted feature into the memory; the processing unit is configured to read the feature from the memory, perform target object detection according to the feature, and output a detection result to the programmable logic unit; and the programmable logic unit is further configured to receive the detection result, generate prompt information according to the detection result, and output the prompt information.

A labeling device includes: an image-signal acquisition unit that acquires an image signal indicating an image captured by a camera; an image recognition unit that has learned by machine learning and performs image recognition on the captured image; and a learning-data-set generation unit that generates, by performing labeling on each object included in the captured image on the basis of a result of image recognition, a learning data set including image data corresponding to each object and label data corresponding to each object.

The invention relates to a method for automated calibration of sensors of a vehicle, wherein at least one first passive optical sensor and at least one second active optical sensor are calibrated by a calibration unit based on a matching spatial orientation of recognised environmental features in transformed sensor data of the first sensor and the sensor data captured by the second sensor.

Methods, apparatus, systems, and articles of manufacture to simulate sensor data are disclosed. An example apparatus includes a noise characteristic identifier to extract a noise characteristic associated with a feature present in first sensor data obtained by a physical sensor. A feature identifier is to identify a feature present in second sensor data. The second sensor data is generated by an environment simulator simulating a virtual representation of the real sensor. A noise simulator is to synthesize noise-adjusted simulated sensor data based on the feature identified in the second sensor data and the noise characteristic associated with the feature present in the first sensor data.

Described are systems and methods for determining an agent that performed an event within a materials handling facility. A series of overhead images that include representations of the event location and one or more agents are processed to determine a motion or movement of the agent over a period of time. For example, a motion model representative of a motion of the agent over a period of time is generated from the images. A distance between the motion model and the event location is also determined. An association between the agent and the event may be determined based on the motion model and the distance between the motion model and the event location.

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups object into object type clusters based on the micro-feature vectors.