A method and apparatus for detecting a digital stamp pattern are disclosed. Keypoints and descriptors are extracted from an original template pattern image. A low resolution original document and at least one lower resolution template pattern image are template-matched to detect a matched region based on match correlation coefficients. This region is cropped out of a full resolution original document. Keypoints and descriptors are extracted from the cropped region, and are matched with stamp pattern keypoints and descriptors using feature based pattern matching. A transformation matrix is used to detect scaling, rotation, and translation of a detected digital stamp pattern in the cropped region. A number of qualified matches determined using feature based pattern matching or the transformation matrix are checked against a pre-set threshold. If a pre-set threshold is exceeded, an alert is generated for a possible security issue. Otherwise, a no security issues signal may be generated.

A method of improving an efficiency of forming the golden samples obtains an image with a chip. Position information of the chip in the image is obtained based on the image. A target region on the image is labeled based on the position information. The target region is a region of the image covered by the chip. The target region is cut from the image to obtain a golden sample. An image process apparatus is also provided.

A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.

A 3D shape matching method and a 3D shape matching device based on 3D local feature description using SGHs are provided. In the method, the spherical neighborhood of the feature point is not only divided based on space but also divided based on geometry, the spherical neighborhood of the feature point is not only divided based on the radial direction and the azimuth respectively but also divided based on the elevation, and the spherical neighborhood of the feature point is not only divided based on the deviation angle deviating from the z axis but also divided based on the deviation angle deviating from the x axis. When the deviation angle deviating from the z axis of the spherical neighborhood is divided, the deviation angle is divided more densely where it is closer to the positive direction of the z axis.

The present disclosure discloses a microwave identification method, which is implemented on at least one device, including at least one processor and at least one storage device, the method including: the at least one processor obtains microwave data; the at least one processor generates an image of one or more objects based on the microwave data; the at least one processor obtains a model of each of the one or more objects; and based on the model of each of the one or more objects, the at least one processor identifies the one or more objects in the image of the one or more objects.

According to an embodiment, an image processing device includes one or more processors. The one or more processors are configured to: acquire an image; detect a first repeated pattern from the image; detect an object included in the first repeated pattern; and output the object as a second repeated pattern.

State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Aspects of the present disclosure relate to a method of identifying a vehicle, and a system thereof. The method can include receiving a first image of a vehicle from a first camera and classifying the vehicle in the first image with a vehicle class label. The method can also include determining a first vehicle fingerprint for the vehicle. The method can also include detecting any changes in the first vehicle fingerprint and the vehicle class label after a first time period. The detected changes in the first vehicle fingerprint can correspond to a disassembly state of the vehicle. The method can also include performing, if the vehicle class label is unchanged, at least one action in response to detected changes in the first vehicle fingerprint.

A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.