A quantification system (300) is described that includes: at least one input (310) configured to provide at least one input medical image and provide a location of interest in the at least one input medical image; and a mapping circuit (325). The mapping circuit (325) is configured to: compute a direct quantification result from the location of interest of the at least one input medical image to a quantification of interest and output a direct first quantification result therefrom; generate a saliency map as part of the computation of the direct quantification result; and derive a segmentation from the saliency map, such that the segmentation independently generates a second quantification result that is within a result range of the direct first quantification result.

The present disclosure discloses a method of ice distribution and data processing for ship ice resistance experiment in broken ice field, the ice floe is uniformly distributed in the broken ice field before each ship ice resistance experiment with eliminating the mutually overlapped broken ice to make it close to the experimental design working condition, and independent repeated experiments are carried out on each ship ice resistance experiment to reduce the influence of accidental factors; after finishing the experiment, the experimental data are divided into intervals and the experimental data corresponding to unreasonable subdomains are eliminated by mathematical statistics, and the uncertainty analysis is carried out; image correction is carried out for pictures of in broken ice field in the pre-experimental data, and the pictures are divided to obtain subdomain pictures to calculate the actual coverage rate of each subdomain; the ice resistance in the experimental data corresponding to each remaining subdomain is modified, so as to make the experimental data more reliable.

A weight estimation unit estimates, from a person's image in which a person is included, a weight of a human body part of the person in accordance with an existence probability of the human body part of the person. A feature creation unit creates a feature for verification based on a weight of the person's image and a weight estimated by the weight estimation unit. A verification feature storage unit stores a feature of the person who is the object of verification as a verification feature. A person verification unit compares the feature for verification created by the feature creation unit and the verification feature stored in the verification feature storage unit.

A gauze detection system is provided that is capable of effectively detecting a gauze pad in the patient's body during surgery without applying special processing to the gauze pad. A gauze detection system 100, includes: an image input section 1 to input a taken image of an operative field; a determination section 2 to determine whether a determination target region contains a feature of a gauze image by image processing, the region having a predetermined size in an input image; and a determination result output section 3 to report detection of a gauze pad in the input image in a case of the determination target region being determined by the determination section 2 to contain the feature of the gauze image.

Systems and methods for predicting items within content and using improved, fine-grained image classification techniques to produce images used to identify consumer products in the real-world by allowing for the recognition of a product using an image captured under a variety of conditions and environments, such as angles, lighting, camera settings, and the like.

An image tracking method of the present invention includes the following steps: (A) obtaining a plurality of original images by using an image capturing device; (B) transmitting the plurality of original images to a computing device, and generating a position box based on a preset image set; (C) obtaining an initial foreground image including a target object, and an identified foreground image is determined based on a pixel ratio and a first threshold; (D) obtaining a feature and obtaining a first feature score based on the feature of the identified foreground images; and (E) generating a target object matching result based on the first feature score and a second threshold, and recording a moving trajectory of the target object based on the target object matching result.

Embodiments of this disclosure provide a lane detection apparatus and method and an electronic device. First, preprocessing is performed based on semantic segmentation to remove interference objects in a binary image, which may improve accuracy of a lane line detection and may be applicable to various road scenarios, and the result based on semantic segmentation may automatically extract a lane line region image containing one or more lane lines, thereby automatically performing perspective transformation to perform search and fitting on the one or more lane lines, including achieving multi-lane detection. And furthermore, by synthesizing detection results of a plurality of input images, accuracy and integrity of the lane detection may further be improved.

In the present disclosure, a candidate area is determined based on a pixel having a specific color included in an input image, and an area is determined to be a processing target from the candidate area based on a pixel having a predetermined color different from the specific color included in the candidate area. Further, a second binary image in which a pixel corresponding to the pixel having the specific color is converted into a white pixel is generated by converting, in a first binary image obtained by the input image being binarized, a pixel that is included in the area determined to be the processing target and corresponds to the pixel having the specific color, into a white pixel.

An image processing device which is capable of accurately detects and corrects areas affected by clouds even if multiple types or layers of clouds present in images, is disclosed. The device includes: a cloud spectrum selection unit for selecting at least one spectrum for each of pixels from spectra of one or more clouds present in an input image; an endmember extraction unit for extracting spectra of one or more endmembers other than the one or more clouds from those of the input image; and an unmixing unit for deriving fractional abundances of the respective spectra of one or more endmembers and a selected spectrum of one of the one or more clouds for the each of pixels in the input image.

A method for dynamically quantizing feature maps of a received image. The method includes convolving an image based on a predicted maximum value, a predicted minimum value, trained kernel weights and the image data. The input data is quantized based on the predicted minimum value and predicted maximum value. The output of the convolution is computed into an accumulator and re-quantized. The re-quantized value is output to an external memory. The predicted min value and the predicted max value are computed based on the previous max values and min values with a weighted average or a pre-determined formula. Initial min value and max value are computed based on known quantization methods and utilized for initializing the predicted min value and predicted max value in the quantization process.