Implementations generally relate to evaluation of similar content-based images. In some implementations, a method includes receiving a first image, where the first image includes at least one first object. The method further includes receiving a second image, where the second image includes at least one second object. The method further includes computing a structural similarity index measure (SSIM) value based on the at least one first object and the at least one second object. The method further includes computing a scale invariant feature transform (SIFT) value based on the at least one first object and the at least one second object. The method further includes computing a histogram value based on the at least one first object and the at least one second object. The method further includes computing a similarity score based on the SSIM value, the SIFT value, and the histogram value.

An apparatus, system and method for regulating fluid flow are disclosed. The apparatus includes a flow rate sensor and a valve. The flow rate sensor uses images to estimate flow through a drip chamber and then controls the valve based on the estimated flow rate. The valve comprises a rigid housing disposed around the tube in which fluid flow is being controlled. Increasing the pressure in the housing controls the size of the lumen within the tube by deforming the tube, therefore controlling flow through the tube.

Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.

Efficient location and identification of documents in images. In an embodiment, at least one quadrangle is extracted from an image based on line(s) extracted from the image. Parameter(s) are determined from the quadrangle(s), and keypoints are extracted from the image based on the parameter(s). Input descriptors are calculated for the keypoints and used to match the keypoints to reference keypoints, to identify classification candidate(s) that represent a template image of a type of document. The type of document and distortion parameter(s) are determined based on the classification candidate(s).

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

A method for determining the position of a first part moving in relation to a second part in a system, comprising forming a code on a surface of the first part or the second part, yielding an encoded surface; detecting the code on the encoded surface; decoding the code; and determining the position of the moving part from the decoding of the code; the code comprises microstructures at precisely selected positions on the surface of the encoded part; detecting the code comprises scanning the encoded surface with a sensor in conditions selected in relation to the code formed and to the encoded surface to precisely read the code on the encoded surface.

The disclosed systems and methods include pre-calculation, per object, of object feature bin values, for identifying close matches between objects, such as text documents, that have numerous weighted features, such as specific-length word sequences. Predetermined feature weights get scaled with two or more selected adjacent scaling factors, and randomly rounded. The expanded set of weighted features of an object gets min-hashed into a predetermined number of feature bins. For each feature that qualifies to be inserted by min-hashing into a particular feature bin, and across successive feature bins, the expanded set of weighted features get min-hashed and circularly smeared into the predetermined number of feature bins. Completed pre-calculated sets of feature bin values for each scaling of the object, together with the scaling factor, are stored for use in comparing sampled features of the object with sampled features of other objects by calculating an estimated Jaccard similarity index.

A lens matching apparatus and a lens matching method are provided. In the method, respective modulation transfer function (MTF) values corresponding to multiple focus lengths of each lens are obtained, a maximum MTF value among the focus lengths of each lens is determined, and lenses are classified according to the maximum MTF value. Each MTF value is determined based on at least one first pixel having maximum light intensity and at least one second pixel having minimum light intensity. Accordingly, the lenses with the same clearness may be classified into the same group, so as to improve image-stitching and speed up the image-stitching.

Techniques are described for merging super pixels of an image. The image may include two superpixel, for which a similarity value is calculated. The similarity value is determined based on the link and cut values of the superpixels, the similarity value representing pixel-based similarity of the superpixels. The link value is determined based on the similarity between color values of the pixels in the superpixels, while the cut value is determined based on the edge pixels of the superpixels. Based on the calculated similarity value, the system determines whether to merge the superpixels and if so, merges the superpixels thereby generating another superpixel.