Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: Imagery captured by an autonomous robot is analyzed to discern digital watermark patterns. In some embodiments, identical but geometrically-inconsistent digital watermark patterns are discerned in an image frame, to aid in distinguishing multiple depicted instances of a particular item. In other embodiments, actions of the robot are controlled or altered in accordance with image processing performed by the robot on a digital watermark pattern. The technology is particularly described in the context of retail stores in which the watermark patterns are encoded, e.g., on product packaging, shelving, and shelf labels. A great variety of other features and arrangements are also detailed.

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and/or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and/or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

Abstract: Imagery captured by an autonomous robot is analyzed to discern digital watermark patterns. In some embodiments, identical but geometrically-inconsistent digital watermark patterns are discerned in an image frame, to aid in distinguishing multiple depicted instances of a particular item. In other embodiments, actions of the robot are controlled or altered in accordance with image processing performed by the robot on a digital watermark pattern. The technology is particularly described in the context of retail stores in which the watermark patterns are encoded, e.g., on product packaging, shelving, and shelf labels. A great variety of other features and arrangements are also detailed.

Abstract: Imagery captured by an autonomous robot is analyzed to discern digital watermark patterns. In some embodiments, identical but geometrically-inconsistent digital watermark patterns are discerned in an image frame, to aid in distinguishing multiple depicted instances of a particular item. In other embodiments, actions of the robot are controlled or altered in accordance with image processing performed by the robot on a digital watermark pattern. The technology is particularly described in the context of retail stores in which the watermark patterns are encoded, e.g., on product packaging, shelving, and shelf labels. A great variety of other features and arrangements are also detailed.

Abstract: The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and/or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

Abstract: The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and/or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

Abstract: This disclosure describes novel methods for generating unique copies of content. One method combines the functions of the master copy and unique copy watermarks. In particular, the method generates a unique copy by varying the manner in which the master copy watermark is embedded in unique copies of a content item. In one embodiment, the master copy watermark is repeated within the content item and its location is varied in a unique pattern that comprises the unique copy watermark. The unique copy is generated by producing a copy in which master copy watermarks are embedded in a unique pattern. For instance in one embodiment, the locations of the master copy watermarks in the content item are represented as a vector of delta values in which each delta value corresponds to the distance between a corresponding instance of the master copy watermark, and a neighboring instance of the master copy watermark.

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: Methods and apparatuses are provided for determining a first fingerprint of audio or video data. The first fingerprint is stored in a database or record including information pertaining to a first user or first account. The audio or video content is altered to yield a different version of the audio or video content, and a second fingerprint is determined from the different version of the audio or video content. The second fingerprint is stored in a database or recording including information pertaining to a second user or second account.

Abstract: A shopper is presented with a customized online store whose inventory is defined by the shopper. In one embodiment, specification of the inventory is conducted in a bricks and mortar store—either during checkout, or by the shopper walking the aisles and scanning items with a barcode scanner pen or the like. The inventory may be defined—at least in part—by scanning items in the shopper's home. A variety of other novel features are also disclosed.

Abstract: The present disclosure relates generally to digital watermarking including watermarking conveyed through multiple channels. One claim recites a method to detect digital watermarks carried in different channels of a physical object. The method comprising: receiving first optically captured image data, the first optically captured image data corresponding to a first channel which is detectable with visible lighting; receiving second optically captured image data, the second optically captured image data corresponding to a second channel with is detectable in a first non-visible spectrum; receiving third optically captured image data, the third optically captured image data corresponding to a third channel with is detectable in a second non-visible spectrum; and using a programmed electronic processor, analyzing the first, second and third optically captured image data to detect watermark components embedded therein. Of course, other claims and combinations are provided too.

Abstract: In one arrangement, information useful in understanding the content of user speech (e.g., phonemes identified by a speech recognition algorithm, data indicating the gender of the speaker, etc.) is determined at an apparatus (e.g., a cell phone), and accompanies speech data sent from that apparatus. (Steganographic encoding of the speech data can be employed to convey this information.) A receiving device can use this accompanying information to better understand the content of the speech. A great variety of other features and arrangements—some dealing with imagery rather than audio—are also detailed.

Abstract: A shopper is presented with a customized online store whose inventory is defined by the shopper. In one embodiment, specification of the inventory is conducted in a bricks and mortar store—either during checkout, or by the shopper walking the aisles and scanning items with a barcode scanner pen or the like. The inventory may be defined—at least in part—by scanning items in the shopper's home. A variety of other novel features useful in on-line shopping are also disclosed.

Abstract: The present disclosure relates to processing media content (e.g., video or audio signals).