Merge pull request #205366 from PatrickFarley/comvis-updates

[cog svcs] Comvis updates

Author: Court72

articles/cognitive-services/Computer-vision/concept-brand-detection.md

@@ -13,7 +13,7 @@ ms.date: 07/05/2022
 ms.author: pafarley
 ---
 
-# Detect popular brands in images
+# Brand detection
 
 Brand detection is a specialized mode of [object detection](concept-object-detection.md) that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.
 

articles/cognitive-services/Computer-vision/concept-categorizing-images.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Categorize images by subject matter
+# Image categorization
 
 In addition to tags and a description, Image Analysis can return the taxonomy-based categories detected in an image. Unlike tags, categories are organized in a parent/child hierarchy, and there are fewer of them (86, as opposed to thousands of tags). All category names are in English. Categorization can be done by itself or alongside the newer tags model.
 

articles/cognitive-services/Computer-vision/concept-describing-images.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Describe images with human-readable language
+# Image description generation
 
 Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
 

articles/cognitive-services/Computer-vision/concept-detecting-adult-content.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Detect adult content
+# Adult content detection
 
 Computer Vision can detect adult material in images so that developers can restrict the display of these images in their software. Content flags are applied with a score between zero and one so developers can interpret the results according to their own preferences.
 

articles/cognitive-services/Computer-vision/concept-detecting-color-schemes.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Detect color schemes in images
+# Color schemes detection
 
 Computer Vision analyzes the colors in an image to provide three different attributes: the dominant foreground color, the dominant background color, and the larger set of dominant colors in the image. The set of possible returned colors is: black, blue, brown, gray, green, orange, pink, purple, red, teal, white, and yellow.
 

articles/cognitive-services/Computer-vision/concept-detecting-domain-content.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Detect domain-specific content
+# Domain-specific content detection
 
 In addition to tagging and high-level categorization, Computer Vision also supports further domain-specific analysis using models that have been trained on specialized data.
 

articles/cognitive-services/Computer-vision/concept-detecting-image-types.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Detecting image types with Computer Vision
+# Image type detection
 
 With the [Analyze Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API, Computer Vision can analyze the content type of images, indicating whether an image is clip art or a line drawing.
 

articles/cognitive-services/Computer-vision/concept-face-detection.md

@@ -1,5 +1,5 @@
 ---
-title: "Face detection and attributes concepts"
+title: "Face detection and attributes - Face"
 titleSuffix: Azure Cognitive Services
 description: Learn more about face detection; face detection is the action of locating human faces in an image and optionally returning different kinds of face-related data.
 services: cognitive-services
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: face-api
 ms.topic: conceptual
-ms.date: 06/13/2022
+ms.date: 07/20/2022
 ms.author: pafarley
 ---
 

articles/cognitive-services/Computer-vision/concept-face-recognition.md

@@ -1,5 +1,5 @@
 ---
-title: "Face recognition concepts"
+title: "Face recognition - Face"
 titleSuffix: Azure Cognitive Services
 description: Learn the concept of Face recognition, its related operations, and the underlying data structures.
 services: cognitive-services
@@ -9,32 +9,18 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: face-api
 ms.topic: conceptual
-ms.date: 06/13/2022
+ms.date: 07/20/2022
 ms.author: pafarley
 ---
 
-# Face recognition concepts
+# Face recognition
 
-This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, Face recognition refers to the method of verifying or identifying an individual by their face. 
+This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, face recognition is the act of verifying or identifying individuals by their faces. Face recognition is important in implementing the identity verification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
 
-Verification is one-to-one matching that takes two faces and returns whether they are the same face, and identification is one-to-many matching that takes a single face as input and returns a set of matching candidates. Face recognition is important in implementing the identity verification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
-
-Try out the capabilities of face recognition quickly and easily using Vision Studio.
+You can try out the capabilities of face recognition quickly and easily using Vision Studio.
 > [!div class="nextstepaction"]
 > [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
 
-## Related data structures
-
-The recognition operations use mainly the following data structures. These objects are stored in the cloud and can be referenced by their ID strings. ID strings are always unique within a subscription, but name fields may be duplicated.
-
-|Name|Description|
-|:--|:--|
-|DetectedFace| This single face representation is retrieved by the [face detection](./how-to/identity-detect-faces.md) operation. Its ID expires 24 hours after it's created.|
-|PersistedFace| When DetectedFace objects are added to a group, such as FaceList or Person, they become PersistedFace objects. They can be [retrieved](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039524c) at any time and don't expire.|
-|[FaceList](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039524b) or [LargeFaceList](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc)| This data structure is an assorted list of PersistedFace objects. A FaceList has a unique ID, a name string, and optionally a user data string.|
-|[Person](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523c)| This data structure is a list of PersistedFace objects that belong to the same person. It has a unique ID, a name string, and optionally a user data string.|
-|[PersonGroup](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) or [LargePersonGroup](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/599acdee6ac60f11b48b5a9d)| This data structure is an assorted list of Person objects. It has a unique ID, a name string, and optionally a user data string. A PersonGroup must be [trained](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395249) before it can be used in recognition operations.|
-|PersonDirectory | This data structure is like **LargePersonGroup** but offers additional storage capacity and other added features. For more information, see [Use the PersonDirectory structure (preview)](./how-to/use-persondirectory.md).
 
 ## Recognition operations
 
@@ -54,6 +40,20 @@ The [Identify](https://westus.dev.cognitive.microsoft.com/docs/services/563879b6
 
 The [Verify](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523a) operation takes a single face ID (from a DetectedFace or PersistedFace object) and a Person object. It determines whether the face belongs to that same person. Verification is one-to-one matching and can be used as a final check on the results from the Identify API call. However, you can optionally pass in the PersonGroup to which the candidate Person belongs to improve the API performance.
 
+## Related data structures
+
+The recognition operations use mainly the following data structures. These objects are stored in the cloud and can be referenced by their ID strings. ID strings are always unique within a subscription, but name fields may be duplicated.
+
+|Name|Description|
+|:--|:--|
+|DetectedFace| This single face representation is retrieved by the [face detection](./how-to/identity-detect-faces.md) operation. Its ID expires 24 hours after it's created.|
+|PersistedFace| When DetectedFace objects are added to a group, such as FaceList or Person, they become PersistedFace objects. They can be [retrieved](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039524c) at any time and don't expire.|
+|[FaceList](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039524b) or [LargeFaceList](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/5a157b68d2de3616c086f2cc)| This data structure is an assorted list of PersistedFace objects. A FaceList has a unique ID, a name string, and optionally a user data string.|
+|[Person](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523c)| This data structure is a list of PersistedFace objects that belong to the same person. It has a unique ID, a name string, and optionally a user data string.|
+|[PersonGroup](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) or [LargePersonGroup](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/599acdee6ac60f11b48b5a9d)| This data structure is an assorted list of Person objects. It has a unique ID, a name string, and optionally a user data string. A PersonGroup must be [trained](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395249) before it can be used in recognition operations.|
+|PersonDirectory | This data structure is like **LargePersonGroup** but offers additional storage capacity and other added features. For more information, see [Use the PersonDirectory structure (preview)](./how-to/use-persondirectory.md).
+
+
 ## Input data
 
 Use the following tips to ensure that your input images give the most accurate recognition results:
@@ -73,4 +73,4 @@ Use the following tips to ensure that your input images give the most accurate r
 
 Now that you're familiar with face recognition concepts, Write a script that identifies faces against a trained PersonGroup.
 
-* [Face client library quickstart](./quickstarts-sdk/identity-client-library.md)
+* [Face quickstart](./quickstarts-sdk/identity-client-library.md)

articles/cognitive-services/Computer-vision/concept-generating-thumbnails.md

@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18
 ---
 
-# Generating smart-cropped thumbnails with Computer Vision
+# Smart-cropped thumbnails
 
 A thumbnail is a reduced-size representation of an image. Thumbnails are used to represent images and other data in a more economical, layout-friendly way. The Computer Vision API uses smart cropping, together with resizing the image, to create intuitive thumbnails for a given image.
 
@@ -48,4 +48,4 @@ The following table illustrates typical thumbnails generated by Computer Vision
 
 The generate thumbnail feature is available through the [Get Thumbnail](https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f20c) and [Get Area of Interest](https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/b156d0f5e11e492d9f64418d) APIs. You can call this API through a native SDK or through REST calls. 
 
-* [Quickstart: Computer Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+* [Generate a thumbnail (how-to)](./how-to/generate-thumbnail.md)