Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

Abstract: An optotype presenting apparatus of a display type with which a polarization red-green test with accuracy can be performed has a display, a polarization optical member including first and second optical regions changing light from the display into light having perpendicular polarizing axes, a selector, regions where optotypes for left and right eyes are to be presented, and a controller, wherein, when a polarization red-green test chart is selected with the selector, the controller controls the display to display a left-eye polarization red-green optotype in pixel regions corresponding to the first regions within the left-optotype-presenting region and display pixel regions in white corresponding to the second regions within the left-optotype-presenting region, while the controller controls to display a right-eye polarization red-green optotype in pixel regions corresponding to the second regions within the right-optotype-presenting region and display pixel regions in white corresponding to the first regions

Abstract: An optotype presenting apparatus of a display type with which a polarization red-green test with accuracy can be performed has a display, a polarization optical member including first and second optical regions changing light from the display into light having perpendicular polarizing axes, a selector, regions where optotypes for left and right eyes are to be presented, and a controller, wherein, when a polarization red-green test chart is selected with the selector, the controller controls the display to display a left-eye polarization red-green optotype in pixel regions corresponding to the first regions within the left-optotype-presenting region and display pixel regions in white corresponding to the second regions within the left-optotype-presenting region, while the controller controls to display a right-eye polarization red-green optotype in pixel regions corresponding to the second regions within the right-optotype-presenting region and display pixel regions in white corresponding to the first regions

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: One embodiment of the invention provides a process and related apparatus for obtaining quantitative data about a 2-dimensional, 3-dimensional image, or other dimensional image. For example, the invention is capable of classifying and counting the number of entities an image contains. Each entity comprises an entity, structure, or some other type of identifiable portion of the image having definable characteristics. The entities located within an image may have a different shape, color, texture, or other definable characteristic, but still belong to the same classification. In other instances, entities comprising a similar color, and texture may be classified as one type while entities comprising a different color, and texture may be classified as another type. An image may contain multiple entities and each entity may belong to a different class.

Abstract: One embodiment of the invention provides a process and related apparatus for obtaining quantitative data about a 2-dimensional, 3-dimensional image, or other dimensional image. For example, the invention is capable of classifying and counting the number of entities an image contains. Each entity comprises an entity, structure, or some other type of identifiable portion of the image having definable characteristics. The entities located within an image may have a different shape, color, texture, or other definable characteristic, but still belong to the same classification. In other instances, entities comprising a similar color, and texture may be classified as one type while entities comprising a different color, and texture may be classified as another type. An image may contain multiple entities and each entity may belong to a different class.