Abstract: Disclosed is a facial recognition system/method, utilizing modules to perform the following routine: extracting a feature descriptor from a detected feature point of a detected face in an input image frame; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching. Optionally, the above described steps may be coupled to a Point-of-Recognition (POR) provisioning, enabling an on-demand gate-keeping and/or payment processing for an end-user at an event/venue entry or point-of-sale.

Abstract: Disclosed is a facial recognition system/method, utilizing modules to perform the following routine: extracting a feature descriptor from a detected feature point of a detected face in an input image frame; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching. Optionally, the above described steps may be coupled to a Point-of-Recognition (POR) provisioning, enabling an on-demand gate-keeping and/or payment processing for an end-user at an event/venue entry or point-of-sale.

Abstract: The present invention discloses methods and systems face recognition. Face recognition involves receiving an image/frame, detecting one or more faces in the image, detecting feature points for each of the detected faces in the image, aligning and normalizing the detected feature points, extracting feature descriptors based on the detected feature points and matching the extracted feature descriptors with a set of pre-stored images for face recognition.

Abstract: The present invention discloses methods and systems face recognition. Face recognition involves receiving an image/frame, detecting one or more faces in the image, detecting feature points for each of the detected faces in the image, aligning and normalizing the detected feature points, extracting feature descriptors based on the detected feature points and matching the extracted feature descriptors with a set of pre-stored images for face recognition.

Abstract: The present invention discloses methods, systems and computer programmable products for detecting license plates and recognizing characters in the license plates. The system receives an image and identifies one or more regions including a license plate. The one or more regions are converted into a plurality of binarized images, which are then filtered to remove noise. Next, one or more clusters of characters are identified in the plurality of binarized images. The one or more clusters of characters are analyzed to recognize a set of characters, wherein each character in the set includes a confidence value.

Abstract: The present invention discloses methods and systems for recognizing an object in an input image based on stored training images. An object recognition system the input image, computes a signature of the input image, compares the signature with one or more stored signatures and retrieves one or more matching images from the set of training images. The matching images are then displayed to the user for further action.

Abstract: A text query from a user may be received. Playlists associated with the text query may be identified. A relevance score may be calculated for each of the playlists based at least in part on a relevance of the corresponding playlist to the text query. Furthermore, content items may be identified where each of the content items is included in at least one of the playlists. For each of the identified content items, one or more playlists that includes the corresponding content item may be identified. A score for each of the content items may be calculated based on the relevance score for each of the playlists that includes the corresponding content item. Furthermore, a new playlist may be generated for the user based on the scores for each of the content items.

Abstract: Various implementations described herein are directed to an integrated circuit having core circuitry with an array of bitcells arranged in columns of bitcells that may represent bits. A first column of bitcells may represent a nearest bit of the bits, and a last column of bitcells may represent a farthest bit of the bits. The integrated circuit may include sense amplifier circuitry coupled to the core circuitry to assist with accessing data stored in the array of bitcells. The integrated circuit may include multiplexer circuitry coupled to the sense amplifier circuitry. The integrated circuit may include first bypass circuitry coupled to outputs of the sense amplifier circuitry at the farthest bit. The integrated circuit may include second bypass circuitry coupled to an output of the multiplexer circuitry at the nearest bit.

Abstract: Various implementations described herein are directed to an integrated circuit having core circuitry with an array of bitcells arranged in columns of bitcells that may represent bits. A first column of bitcells may represent a nearest bit of the bits, and a last column of bitcells may represent a farthest bit of the bits. The integrated circuit may include sense amplifier circuitry coupled to the core circuitry to assist with accessing data stored in the array of bitcells. The integrated circuit may include multiplexer circuitry coupled to the sense amplifier circuitry. The integrated circuit may include first bypass circuitry coupled to outputs of the sense amplifier circuitry at the farthest bit. The integrated circuit may include second bypass circuitry coupled to an output of the multiplexer circuitry at the nearest bit.

Abstract: The present invention discloses methods and systems face recognition. Face recognition involves receiving an image/frame, detecting one or more faces in the image, detecting feature points for each of the detected faces in the image, aligning and normalizing the detected feature points, extracting feature descriptors based on the detected feature points and matching the extracted feature descriptors with a set of pre-stored images for face recognition.

Abstract: Various implementations described herein are directed to an integrated circuit. The integrated circuit may include dummy wordline circuitry having a dummy wordline driver coupled to a dummy wordline load via a dummy wordline. The integrated circuit may include underdrive circuitry coupled to the dummy wordline between the dummy wordline driver and the dummy wordline load. The underdrive circuitry may generate an underdrive on the dummy wordline when the dummy wordline is selected and driven by the dummy wordline driver.

Abstract: An initial plurality of search results is obtained based on a search query pertaining to a topic of interest. A determination is made of whether to perform clustering on the initial plurality of search results. Upon determining not to perform clustering on the search result, a user interface with the initial plurality of search results is provided. Upon determining to perform clustering on the initial plurality of search results, a user interface with a modified plurality of search results is provided. The user interface is to position a first result in the modified plurality of search results adjacent to a position of a second search result in the modified plurality of search results. The first search result is associated with a channel and the second search result is associated with the channel.

Abstract: Various implementations described herein are directed to an integrated circuit. The integrated circuit may include dummy wordline circuitry having a dummy wordline driver coupled to a dummy wordline load via a dummy wordline. The integrated circuit may include underdrive circuitry coupled to the dummy wordline between the dummy wordline driver and the dummy wordline load. The underdrive circuitry may generate an underdrive on the dummy wordline when the dummy wordline is selected and driven by the dummy wordline driver.

Abstract: The present invention discloses methods and systems for recognizing an object in an input image based on stored training images. An object recognition system the input image, computes a signature of the input image, compares the signature with one or more stored signatures and retrieves one or more matching images from the set of training images. The matching images are then displayed to the user for further action.

Abstract: Various implementations described herein are directed to an integrated circuit having core circuitry with an array of memory cells arranged in columns. The integrated circuit may include write assist circuitry having a column selector that accesses the memory cells via a bitline coupled to each of the columns. The write assist circuitry may include a first node that couples the column selector to a discharge circuit and a feedback circuit. The write assist circuitry may include a second node that couples a trigger circuit to the discharge circuit and the feedback circuit. The trigger circuit enables the discharge circuit, discharges the second node, and is disabled after discharging the second node. The discharge circuit discharges the first node, and the feedback circuit tracks the first node and disables the discharge circuit.

Abstract: A search result is modified to include a predetermined number of channels within a predetermined number of a plurality of entries in the search result. The search result is generated in response to a search query. The search result is modified to comprise a predetermined number of channels within a predetermined number of the plurality of entries. The modified search result is updated based on the plurality of entries and a subset of the updated modified search result is selected to be presented in response to the search query. The subset of the updated modified search result comprises the channel and at least one selection of data content.

Abstract: The present invention discloses methods, systems and computer programmable products for detecting license plates and recognizing characters in the licence plates. The system receives an image and identifies one or more regions including a license plate. The one or more regions are converted into a plurality of binarized images, which are then filtered to remove noise. Next, one or more clusters of characters are identified in the plurality of binarized images. The one or more clusters of characters are analyzed to recognize a set of characters, wherein each character in the set includes a confidence value.

Abstract: The present invention discloses methods and systems for detecting a human body in an image using a machine learning model. The method includes selecting one or more candidate regions from one or more regions in an image based on a pre-defined threshold. Then, a body is detected in a candidate region of the one or more candidate regions, based on a set of pair-wise constraints. The body detection further includes detection of various body parts. Thereafter, a score is computed for each detected body part and a final score for the candidate region is computed, based on the scores of the detected body parts.

Abstract: The present invention discloses methods and systems for detecting a human body in an image using a machine learning model. The method includes selecting one or more candidate regions from one or more regions in an image based on a pre-defined threshold. Then, a body is detected in a candidate region of the one or more candidate regions, based on a set of pair-wise constraints. The body detection further includes detection of various body parts. Thereafter, a score is computed for each detected body part and a final score for the candidate region is computed, based on the scores of the detected body parts.

Abstract: The present invention discloses methods and systems face recognition. Face recognition involves receiving an image/frame, detecting one or more faces in the image, detecting feature points for each of the detected faces in the image, aligning and normalizing the detected feature points, extracting feature descriptors based on the detected feature points and matching the extracted feature descriptors with a set of pre-stored images for face recognition.