Systems and methods for analyzing, segmenting, and classifying multimedia material are disclosed herein. Embodiments include (i) receiving multimedia material for analysis, (ii) extracting elements from the multimedia material and forming objects comprising the elements; (iii) segmenting the multimedia material into segments, where individual segments include objects located within a threshold distance from each other; (iv) detecting objects within each segment; (v) associating attributes with the detected objects within the segments; (vi) annotating the segments by creating a relationship tree among the objects within each segment; and (vii) storing annotations of the segments for analysis.

Provided is a segment-block-based handwritten signature authentication system and a method thereof, and more particularly, to a handwritten signature authentication system and a method thereof that enrolls a handwritten signature including handwritten signature characteristics information based on segment blocks disjointed by a user, acquires segment-block-based handwritten signature characteristics information from the handwritten signature upon request for handwritten signature authentication, and performs handwritten signature authentication by comparing the pre-enrolled handwritten signature characteristics information based on segments and the acquired handwritten signature characteristics information.

Systems, methods, and computer-readable storage media are provided for identifying (recognizing) an object from its shape in a sequence of images utilizing sequence alignment matrices (SAMs). For a given image, an object is segmented and from the segmented object, a set of key points is extracted. From the extracted key points, a set of local feature descriptors, strictly related to the key points and uniquely ordered in sequence, are extracted. The feature sequence obtained from the segmented object is aligned with a counterpart or reference image (e.g., a model or another image) using a Sequence Alignment Matrix (SAM). A custom scoring technique for the alignment provides a quality index for the identification of the object.

In one embodiment, the disclosure relates to a method for determining a degree of homogeneity in one or more inspection images of cargo in one or more containers, comprising: determining whether a zone of interest in one or more processed inspection images comprises one or more patterns, wherein the one or more processed inspection images are processed from one or more inspection images generated by an inspection system configured to inspect the one or more containers; and in the event that one or more patterns is determined and that a variation in the determined one or more patterns is identified, classifying the one or more inspection images as having a degree of homogeneity below a predetermined homogeneity threshold.

An image jaggedness filter is disclosed that can be used to detect the presence of ungrounded objects such as water droplets or coins, and delay periodic baseline adjustments until these objects are no longer present. To do otherwise could produce inaccurate normalized baseline sensor output values. The application of a global baseline offset is also disclosed to quickly modify the sensor offset values to account for conditions such as rapid temperature changes. Background pixels not part of any touch regions can be used to detect changes to no-touch sensor output values and globally modify the sensor offset values accordingly. The use of motion dominance ratios and axis domination confidence values is also disclosed to improve the accuracy of locking onto dominant motion components as part of gesture recognition.

An identification method includes: sensing movement data; capturing multiple feature data from the movement data; cutting the first feature data into a plurality of first feature segments, dividing the first feature segments into a plurality of first feature groups, and calculating multiple first similarity parameters of the first feature groups respectively corresponding to a plurality of channels; making the first feature groups correspond to the channels according to the first similarity parameters; simplifying the first feature groups corresponding to the channels respectively by a convolution algorithm to obtain a plurality of first convolution results corresponding to the first feature groups; simplifying the first convolution results corresponding to the first feature groups respectively by a pooling algorithm to obtain multiple first pooling results corresponding to the first feature groups; and combining the first pooling results corresponding to the first feature groups to generate a first feature map.

A method of image processing in a structured light imaging system is provided that includes receiving a captured image of a scene, wherein the captured image is captured by a camera of a projector-camera pair, and wherein the captured image includes a binary pattern projected into the scene by the projector, applying a filter to the rectified captured image to generate a local threshold image, wherein the local threshold image includes a local threshold value for each pixel in the rectified captured image, and extracting a binary image from the rectified captured image wherein a value of each location in the binary image is determined based on a comparison of a value of a pixel in a corresponding location in the rectified captured image to a local threshold value in a corresponding location in the local threshold image.

The image signature extraction device includes an image signature generation unit and an encoding unit. The image signature generation unit extracts region features from respective sub-regions in an image in accordance with a plurality of pairs of sub-regions in the image, the pairs of sub-regions including at least one pair of sub-regions in which both a combination of shapes of two sub-regions of the pair and a relative position between the two sub-regions of the pair differ from those of at least one of other pairs of sub-regions, and based on the extracted region features of the respective sub-regions, generates an image signature to be used for identifying the image. The encoding unit encodes the image signature.

Systems, methods, and non-transitory computer-readable media can determine training data describing respective relationships between a set of map tiles, the map tiles collectively representing a given geographic region. A model can be trained to predict a likelihood of a pair of map tiles corresponding to one or more geographic classifications based at least in part on the training data. Polygons that correspond to respective sub-regions within the geographic region can be determined based at least in part on the model.

This invention relates to a method of analyzing a factor of an attribute based on a case sample set containing combinations of image data and attribute data associated with the image data. The attribute factor analysis method includes: a division step of dividing an image region of the image data forming each element of the case sample set into parts in a mesh shape of a predetermined sample size; a reconstruction step of reconstructing, based on the case sample set, the case sample sets for the respective parts to obtain reconstructed case sample sets; an analysis step of analyzing, for each of the reconstructed case sample sets, a dependency between an explanatory variable representing a feature value of image data on each part and an objective variable representing the attribute data, to thereby obtain an attribute factor analysis result; and a visualization step of visualizing the attribute factor analysis result to produce the visualized attribute factor analysis result.