pr review items, and last-minute PM requests

PatrickFarley · PatrickFarley · commit 16bd5ec97d04 · 2023-03-06T19:20:37.000-05:00
diff --git a/articles/cognitive-services/Computer-vision/concept-background-removal.md b/articles/cognitive-services/Computer-vision/concept-background-removal.md
@@ -40,7 +40,7 @@ The following example images illustrate what the Image Analysis service returns
 |---------|---------|---------|
 | :::image type="content" source="media/background-removal/building-1.png" alt-text="Photo of a city near water.":::    |  :::image type="content" source="media/background-removal/building-1-result.png" alt-text="Photo of a city near water; sky is transparent.":::       |   :::image type="content" source="media/background-removal/building-1-matte.png" alt-text="Alpha matte of a city skyline.":::      |
 |   :::image type="content" source="media/background-removal/person-5.png" alt-text="Photo of a group of people using a tablet.":::  |    :::image type="content" source="media/background-removal/person-5-result.png" alt-text="Photo of a group of people using a tablet; background is transparent.":::     |   :::image type="content" source="media/background-removal/person-5-matte.png" alt-text="Alpha matte of a group of people.":::      |
-|   :::image type="content" source="media/background-removal/bears.png" alt-text="Photo of a group of bears in the woods.":::  |    :::image type="content" source="media/background-removal/bears-result.png" alt-text="Photo of a group of bears; background is transparent out.":::     |   :::image type="content" source="media/background-removal/bears-alpha.png" alt-text="Alpha matte of a group of bears.":::      |
+|   :::image type="content" source="media/background-removal/bears.png" alt-text="Photo of a group of bears in the woods.":::  |    :::image type="content" source="media/background-removal/bears-result.png" alt-text="Photo of a group of bears; background is transparent.":::     |   :::image type="content" source="media/background-removal/bears-alpha.png" alt-text="Alpha matte of a group of bears.":::      |
 
 
 ## Limitations
diff --git a/articles/cognitive-services/Computer-vision/concept-model-customization.md b/articles/cognitive-services/Computer-vision/concept-model-customization.md
@@ -172,7 +172,7 @@ Below are the possible reasons:
 
 ### What is the expected latency for predictions with custom models?
 
-We do not recommend you use custom models for business critical environments due to potential high latency. When customers train custom models in Vision Studio, those custom models belong to the Computer Vision resource that they were trained under, and the customer is able to make calls to those models using the **Analyze Image** API. When they make these calls, the custom model is loaded in memory, and the prediction infrastructure is initialized. While this happens, customers might experience longer than expected latency to receive prediction results. Microsoft is working on making latency improvements in the near future.
+We do not recommend you use custom models for business critical environments due to potential high latency. When customers train custom models in Vision Studio, those custom models belong to the Computer Vision resource that they were trained under, and the customer is able to make calls to those models using the **Analyze Image** API. When they make these calls, the custom model is loaded in memory, and the prediction infrastructure is initialized. While this happens, customers might experience longer than expected latency to receive prediction results.
 
 ## Next steps
 
diff --git a/articles/cognitive-services/Computer-vision/how-to/background-removal.md b/articles/cognitive-services/Computer-vision/how-to/background-removal.md
@@ -9,6 +9,7 @@ ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: how-to
 ms.date: 03/03/2023
+ms.custom: references_regions
 ---
 
 # Remove the background from images
diff --git a/articles/cognitive-services/Computer-vision/how-to/blob-storage-search.md b/articles/cognitive-services/Computer-vision/how-to/blob-storage-search.md
@@ -21,37 +21,37 @@ To get started with the **Search photos with natural language** or **Video summa
 > [!IMPORTANT]
 > You need to create your storage account on the same Azure subscription as the Computer Vision resource you're using in the **Search photos with natural language** or **Video summary and frame locator** scenarios as shown below.
 
-:::image type="content" source="../media/storage-instructions/subscription.png" alt-text="Show the resource":::
+:::image type="content" source="../media/storage-instructions/subscription.png" alt-text="Screenshot of resource selection.":::
 
 ## Create a new storage account
 
 To get started, [create a new storage account](https://ms.portal.azure.com/#create/Microsoft.StorageAccount).
 
-:::image type="content" source="../media/storage-instructions/create-storage.png" alt-text="Blob storage creation":::
+:::image type="content" source="../media/storage-instructions/create-storage.png" alt-text="Screenshot of Blob storage creation.":::
 
 
 Fill in the required parameters to configure your storage account, then select `Review` and `Create`. 
 
 Once your storage account has been deployed, select `Go to resource` to open the storage account overview. 
 
-:::image type="content" source="../media/storage-instructions/go-to-resource.png" alt-text="Go to resource":::
+:::image type="content" source="../media/storage-instructions/go-to-resource.png" alt-text="Screenshot of Go to resource button.":::
 
 
 ## Configure CORS rule on the storage account 
 
 In your storage account overview, find the **Settings** section in the left hand navigation and select `Resource sharing (CORS)`, shown below.
 
-:::image type="content" source="../media/storage-instructions/go-to-cors.png" alt-text="Find resource sharing":::
+:::image type="content" source="../media/storage-instructions/go-to-cors.png" alt-text="Screenshot of resource sharing screen.":::
 
 
-Create a CORS rule by setting the **Allowed Origins** field to `https://<your resource>.azurewebsites.net`.
+Create a CORS rule by setting the **Allowed Origins** field to `https://portal.vision.cognitive.azure.com`.
 
 In the Allowed Methods field, select the `GET` checkbox to allow an authenticated request from a different domain. In the **Max age** field, enter the value `9999`, and click `Save`. 
 
 [Learn more about CORS support for Azure Storage](/rest/api/storageservices/cross-origin-resource-sharing--cors--support-for-the-azure-storage-services).
 
 
-:::image type="content" source="../media/storage-instructions/cors-rule.png" alt-text="Show completed CORS":::
+:::image type="content" source="../media/storage-instructions/cors-rule.png" alt-text="Screenshot of completed CORS screen.":::
 
 
 This will allow Vision Studio to access images and videos in your blob storage container to extract insights on your data.
@@ -61,7 +61,7 @@ This will allow Vision Studio to access images and videos in your blob storage c
 In the **Try with your own video** or **Try with your own image** section in Vision Studio, select the storage account that you configured with the CORS rule. Select the container in which your images or videos are stored. If you don't have a container, you can create one and upload the images or videos from your local device. If you have updated the CORS rules on the storage account, refresh the Blob container or Video files on container sections.
 
 
-:::image type="content" source="../media/storage-instructions/video-selection.png" alt-text="Show upload in VS":::
+:::image type="content" source="../media/storage-instructions/video-selection.png" alt-text="Screenshot of image upload in Vision Studio.":::
 
 
 
diff --git a/articles/cognitive-services/Computer-vision/how-to/model-customization.md b/articles/cognitive-services/Computer-vision/how-to/model-customization.md
@@ -34,6 +34,9 @@ This guide shows you how to create and train a custom image classification model
 
 Begin by going to [Vision Studio](https://portal.vision.cognitive.azure.com/) and selecting the **Image analysis** tab. Then select either the **Extract common tags from images** tile for image classification or the **Extract common objects in images** tile for object detection. This guide will demonstrate a custom image classification model. 
 
+> [!IMPORTANT]
+> To train a custom model in Vision Studio, your Azure subscription needs to be approved for access. Please request access using [this form](https://aka.ms/visionaipublicpreview).
+
 On the next screen, the **Choose the model you want to try out** drop-down lets you select the Pretrained Vision model (to do ordinary Image Analysis) or a custom trained model. Since you don't have a custom model yet, select **Train a custom model**.
 
 ![Choose Resource Page]( ../media/customization/custom-model.png)
diff --git a/articles/cognitive-services/Computer-vision/includes/image-analysis-curl-quickstart-40.md b/articles/cognitive-services/Computer-vision/includes/image-analysis-curl-quickstart-40.md
@@ -14,7 +14,7 @@ ms.author: pafarley
 ms.custom: seodec18, ignite-2022, references_regions
 ---
 
-Use the Image Analysis REST API to to read text and generate captions for the image (version 4.0 only).
+Use the Image Analysis REST API to read text and generate captions for the image (version 4.0 only).
 
 > [!TIP]
 > The Analysis 4.0 API can do many different operations. See the [Analyze Image how-to guide](../how-to/call-analyze-image-40.md) for examples that showcase all of the available features.
diff --git a/articles/cognitive-services/Computer-vision/overview-image-analysis.md b/articles/cognitive-services/Computer-vision/overview-image-analysis.md
@@ -68,19 +68,19 @@ Version 4.0 also lets you use dense captioning, which generates detailed caption
 
 [Image captions (v3.2)](concept-describing-images.md) [(v4.0 preview)](concept-describe-images-40.md)
 
-:::image type="content" source="Images/description.png" alt-text="Photo of cows with a simple description on the right":::
+:::image type="content" source="Images/description.png" alt-text="Photo of cows with a simple description on the right.":::
 
 ### Detect objects
 
 Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process further relationships between the objects in an image. It also lets you know when there are multiple instances of the same tag in an image. [Detect objects (v3.2)](concept-object-detection.md) [(v4.0 preview)](concept-object-detection-40.md)
 
-:::image type="content" source="Images/detect-objects.png" alt-text="Photo of an office with a rectangle drawn around a laptop":::
+:::image type="content" source="Images/detect-objects.png" alt-text="Photo of an office with a rectangle drawn around a laptop.":::
 
 ### Tag visual features
 
 Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn't limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on. [Tag visual features (v3.2)](concept-tagging-images.md) [(v4.0 preview)](concept-tag-images-40.md)
 
-:::image type="content" source="Images/tagging.png" alt-text="Photo of a skateboarder with tags listen on the right":::
+:::image type="content" source="Images/tagging.png" alt-text="Photo of a skateboarder with tags listed on the right.":::
 
 ### Get the area of interest / smart crop
 
