Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: A method, system, and computer readable storage for identifying regions of interest on a casino gaming table. Regions of interest are locations on an image where relevant actions are typically going to occur. For example, locations of betting area region of interest and card area regions of interest are determined so that these areas can be analyzed on a video camera. The analyzed areas can then be used for things such as determining and tracking player betting amounts, determining if the game is being dealt properly (by analyzing the cards dealt to determine if the proper cards are dealt and/or the proper payouts are made).

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.