Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Described herein are software and systems for analyzing videos and/or images. Software and systems described herein are configured in different embodiments to carry out different types of analyses. For example, in some embodiments, software and systems described herein are configured to locate an object of interest within a video and/or image.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

This disclosure provides a highly scalable training data preparation pipeline for data cleaning and augmentation with the aim of extracting the most meaningful information while keeping the noise level low, as well as a highly efficient distributed trainer for the deep neural networks suitable for facial recognition. The goal is to train deeper and larger neural networks with larger and higher quality facial image datasets iteratively and frequently without incurring prohibitive costs and drastic delays.

This disclosure provides a highly scalable training data preparation pipeline for data cleaning and augmentation with the aim of extracting the most meaningful information while keeping the noise level low, as well as a highly efficient distributed trainer for the deep neural networks suitable for facial recognition. The goal is to train deeper and larger neural networks with larger and higher quality facial image datasets iteratively and frequently without incurring prohibitive costs and drastic delays.

Disclosed herein are systems and methods for determining whether an unknown product matches a scanned barcode during a checkout process. An edge computing device or other computer system can receive, from an overhead camera at a checkout lane, image data of an unknown product that is placed on a flatbed scanning area, identify candidate product identifications for the unknown product based on applying a classification model and/or product identification models to the image data, and determine based on the candidate product identifications, whether the unknown product matches a product associated with a barcode that is scanned at a POS terminal in the checkout lane. The classification model can be used to determine n-dimensional space feature values for the unknown product and determine which product the unknown product likely matches. The product identification models can be used to determine whether the unknown product is one of the products that are modeled.

In some examples, a system detects a highlighted area in an image content, the highlighted area including a portion of the image content, and a highlighting mark that highlights the portion of the image content. The system applies a color adjustment process in the highlighted area, wherein the color adjustment process comprises adjusting a color of the highlighted area comprising the portion of the image content highlighted by the highlighting mark, and wherein the adjusting of the color of the highlighted area comprises adjusting color values of plural color components in a first color space.

Techniques are disclosed for identifying indicators of compromise in a variety of medical/healthcare objects. The objects may be finished products or components of medical/healthcare objects/devices. The indicators of compromise in the objects are determined/detected by analyzing their data residing in a cloud. The analysis is performed by an instant baseline engine that first establishes a rolling baseline with a centroid of a conceptual hypercube. The centroid represents the normal population of data packets. Data packets far enough away from the centroid indicate an anomaly that may be an indicator of a compromise of/in the respective object. An early detection of such indicators of compromise in the objects can prevent catastrophic downstream consequences with the potential of saving lives and/or protecting them from harm.