comvis freshness

PatrickFarley · PatrickFarley · commit 4be1a4379049 · 2025-02-21T16:25:33.000-05:00
diff --git a/articles/ai-services/computer-vision/computer-vision-resource-container-config.md b/articles/ai-services/computer-vision/computer-vision-resource-container-config.md
@@ -7,7 +7,7 @@ author: aahill
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: aahi
 ---
@@ -58,7 +58,7 @@ The `Billing` setting specifies the endpoint URI of the _Azure AI services_ reso
 
 This setting can be found in the following place:
 
-* Azure portal: **Azure AI services** Overview, labeled `Endpoint`
+* Azure portal: **Azure AI services** Overview; labeled `Endpoint`
 
 Remember to add the `vision/<version>` routing to the endpoint URI as shown in the following table. 
 
@@ -97,7 +97,7 @@ The exact syntax of the host mount location varies depending on the host operati
 
 ## Example docker commands
 
-The following examples use the configuration settings to illustrate how to write and use `docker run` commands.  Once running, the container continues to run until you [stop](computer-vision-how-to-install-containers.md#stop-the-container) it.
+The following examples use the configuration settings to illustrate how to write and use `docker run` commands. Once running, the container continues to run until you [stop](computer-vision-how-to-install-containers.md#stop-the-container) it.
 
 * **Line-continuation character**: The Docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements. 
 * **Argument order**: Do not change the order of the arguments unless you're familiar with Docker containers.
@@ -112,7 +112,8 @@ Replace {_argument_name_} with your own values:
 [!INCLUDE [subdomains-note](../includes/cognitive-services-custom-subdomains-note.md)]
 
 > [!IMPORTANT]
-> The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start.  For more information, see [Billing](computer-vision-how-to-install-containers.md#billing).
+> The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start. For more information, see [Billing](computer-vision-how-to-install-containers.md#billing).
+>
 > The ApiKey value is the **Key** from the Vision resource keys page.
 
 ## Container Docker examples
@@ -170,6 +171,7 @@ Logging:Console:LogLevel:Default=Information
 
 ---
 
-## Next steps
+## Next step
 
-* Review [How to install and run containers](computer-vision-how-to-install-containers.md).
+> [div class="nextstepaction"]
+> Review [How to install and run containers](computer-vision-how-to-install-containers.md).
diff --git a/articles/ai-services/computer-vision/concept-background-removal.md b/articles/ai-services/computer-vision/concept-background-removal.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -56,6 +56,7 @@ It's important to note the limitations of background removal:
 
 The background removal feature is available through the [Segment](/rest/api/computervision/image-analysis/segment?view=rest-computervision-2023-02-01-preview&tabs=HTTP) API (`imageanalysis:segment`). See the [Background removal how-to guide](./how-to/background-removal.md) for more information.
 
-## Next steps
+## Next step
 
-* [Call the background removal API](./how-to/background-removal.md)
+> [!div class="nextstepaction"]
+> [Call the background removal API](./how-to/background-removal.md)
diff --git a/articles/ai-services/computer-vision/concept-categorizing-images.md b/articles/ai-services/computer-vision/concept-categorizing-images.md
@@ -8,14 +8,14 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
 
 # Image categorization
 
-In addition to tags and a description, Image Analysis 3.2 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.
+In addition to tags and a description, Image Analysis 3.2 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 in the same API call as the newer tags model.
 
 ## The 86-category taxonomy
 
@@ -61,6 +61,6 @@ The categorization feature is part of the [Analyze Image 3.2](/rest/api/computer
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 
-## Next steps
+## Related content
 
 Learn the related concepts of [tagging images](concept-tagging-images.md) and [describing images](concept-describing-images.md).
diff --git a/articles/ai-services/computer-vision/concept-detecting-color-schemes.md b/articles/ai-services/computer-vision/concept-detecting-color-schemes.md
@@ -8,17 +8,24 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
 
 # Color scheme detection
 
-Azure AI 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.
+Azure AI Vision can analyze the colors in an image and return various attributes that describe those colors. 
 
+## Dominant colors
+The service returns 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 color values is: black, blue, brown, gray, green, orange, pink, purple, red, teal, white, and yellow.
+
+## Accent color
 Azure AI Vision also extracts an accent color, which represents the most vibrant color in the image, based on a combination of the dominant color set and saturation. The accent color is returned as a hexadecimal HTML color code (for example, `00CC00`).
 
+## Black and white detection
 Azure AI Vision also returns a boolean value indicating whether the image is a black-and-white image.
 
 ## Color scheme detection examples
diff --git a/articles/ai-services/computer-vision/concept-detecting-domain-content.md b/articles/ai-services/computer-vision/concept-detecting-domain-content.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
diff --git a/articles/ai-services/computer-vision/concept-detecting-image-types.md b/articles/ai-services/computer-vision/concept-detecting-image-types.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -112,4 +112,7 @@ The following JSON responses illustrates what Azure AI Vision returns when indic
 
 The image type detection feature is part of the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `ImageType` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"imageType"` section.
 
-* [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
+## Next step
+
+> [!div class="nextstepaction"]
+> [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
diff --git a/articles/ai-services/computer-vision/concept-generate-thumbnails-40.md b/articles/ai-services/computer-vision/concept-generate-thumbnails-40.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -38,6 +38,7 @@ The generated bounding box can vary widely depending on what you specify for asp
 
 The smart cropping feature is available through the [Analyze Image 4.0 API](https://aka.ms/vision-4-0-ref). Include `SmartCrops` in the **features** query parameter. Also include a **smartcrops-aspect-ratios** query parameter, and set it to a decimal value for the aspect ratio you want (defined as width / height) in the range [0.75, 1.80]. Multiple aspect ratio values should be comma-separated. If no aspect ratio value is provided the API will return a crop with an aspect ratio that best preserves the image’s most important region.  
 
-## Next steps
+## Next step
 
-* [Call the Analyze Image API](./how-to/call-analyze-image-40.md)
+> [!div class="nextstepaction"]
+> [Call the Analyze Image API](./how-to/call-analyze-image-40.md)
diff --git a/articles/ai-services/computer-vision/concept-generating-thumbnails.md b/articles/ai-services/computer-vision/concept-generating-thumbnails.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -46,8 +46,6 @@ The following table illustrates thumbnails defined by smart-cropping for the exa
 
 ## Use the API
 
-
 The generate thumbnail feature is available through the [Get Thumbnail](/rest/api/computervision/generate-thumbnail/generate-thumbnail?view=rest-computervision-v3.2&tabs=HTTP) and [Get Area of Interest](/rest/api/computervision/get-area-of-interest/get-area-of-interest?view=rest-computervision-v3.2&tabs=HTTP) API. You can call this API through a native SDK or through REST calls. 
 
-
 * [Generate a thumbnail (how-to)](./how-to/generate-thumbnail.md)
diff --git a/articles/ai-services/computer-vision/concept-model-customization.md b/articles/ai-services/computer-vision/concept-model-customization.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
diff --git a/articles/ai-services/computer-vision/concept-tagging-images.md b/articles/ai-services/computer-vision/concept-tagging-images.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -143,6 +143,6 @@ The tagging feature is part of the [Analyze Image](/rest/api/computervision/anal
 
 * [Quickstart: Image Analysis REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 
-## Next steps
+## Related content
 
 Learn the related concepts of [categorizing images](concept-categorizing-images.md) and [describing images](concept-describing-images.md).
diff --git a/articles/ai-services/computer-vision/how-to/background-removal.md b/articles/ai-services/computer-vision/how-to/background-removal.md
@@ -9,7 +9,7 @@ ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ---
 
 # Remove the background in images
@@ -21,21 +21,21 @@ This article demonstrates how to call the Image Analysis 4.0 API to segment an i
 > [!IMPORTANT]
 > Background removal is only available through direct REST API calls. It is not available through the SDKs.
 
+> [!IMPORTANT]
+> Background removal is only available in certain Azure regions. See [Region availability](./../overview-image-analysis.md#region-availability)
+
+
 ## Prerequisites
 
 This guide assumes you have successfully followed the steps mentioned in the [quickstart](../quickstarts-sdk/image-analysis-client-library-40.md) page. This means:
-
 * You have <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision"  title="created a Vision resource"  target="_blank">created a Vision resource </a> and obtained a key and endpoint URL.
 * You have successfully made a `curl.exe` call to the service (or used an alternative tool). You modify the `curl.exe` call based on the examples here.
 
 The [quickstart](../quickstarts-sdk/image-analysis-client-library-40.md) shows you how to extract visual features from an image. However, the concepts are similar to background removal. Therefore you benefit from starting with the quickstart and making modifications.
 
-> [!IMPORTANT]
-> Background removal is only available in certain Azure regions. See [Region availability](./../overview-image-analysis.md#region-availability)
+## Authentication to the service
 
-## Authenticate against the service
-
-To authenticate against the Image Analysis service, you need a Computer Vision key and endpoint URL.
+To authenticate to the Image Analysis service, you need a Computer Vision key and endpoint URL.
 
 > [!TIP]
 > Don't include the key directly in your code, and never post it publicly. See the Azure AI services [security](../../security-features.md) article for more authentication options like [Azure Key Vault](../../use-key-vault.md). 
@@ -227,9 +227,9 @@ The API returns an image the same size as the original for the `foregroundMattin
 [!INCLUDE [image-analysis-error-codes-40](../includes/image-analysis-error-codes-40.md)]
 
 
+## Next step
 
-## Next steps
-
-[Background removal concepts](../concept-background-removal.md)
+> [!div class="nextstepaction"]
+> [Background removal concepts](../concept-background-removal.md)
 
 
diff --git a/articles/ai-services/computer-vision/how-to/blob-storage-search.md b/articles/ai-services/computer-vision/how-to/blob-storage-search.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 
@@ -23,6 +23,9 @@ To get started with the **Search photos with image retrieval** scenario in Visio
 >
 > :::image type="content" source="../media/storage-instructions/subscription.png" alt-text="Screenshot of resource selection.":::
 
+> [!IMPORTANT]
+> Image retrieval APIs are only available in certain geographic regions. See [Region availability](../overview-image-analysis.md#region-availability).
+
 
 ## Create a new storage account
 
diff --git a/articles/ai-services/computer-vision/how-to/coco-verification.md b/articles/ai-services/computer-vision/how-to/coco-verification.md
@@ -15,12 +15,11 @@ ms.author: pafarley
 
 # Check the format of your COCO annotation file
 
-<!-- nbstart https://raw.githubusercontent.com/Azure-Samples/cognitive-service-vision-model-customization-python-samples/main/docs/check_coco_annotation.ipynb -->
 
 [!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
 
 > [!TIP]
-> This article is based on the Jupyter notebook _check_coco_annotation.ipynb_. **[Open in GitHub](https://github.com/Azure-Samples/cognitive-service-vision-model-customization-python-samples/blob/main/docs/check_coco_annotation.ipynb)**.
+> This article is based on the Jupyter notebook *check_coco_annotation.ipynb*. **[Open in GitHub](https://github.com/Azure-Samples/cognitive-service-vision-model-customization-python-samples/blob/main/docs/check_coco_annotation.ipynb)**.
 
 This guide demonstrates how to check if the format of your annotation file is correct. First, install the python samples package from the command line:
 
@@ -42,12 +41,12 @@ purpose = Purpose.TRAINING # or Purpose.EVALUATION
 check_coco_annotation_file(json.loads(coco_file_path.read_text()), annotation_kind, purpose)
 ```
 
-<!-- nbend -->
 
 ## Use COCO file in a new project
 
 Once your COCO file is verified, you're ready to import it to your model customization project. See [Create and train a custom model](model-customization.md) and go to the section on selecting/importing a COCO file&mdash;you can follow the guide from there to the end.
 
-## Next steps
+## Next step
 
-* [Create and train a custom model](model-customization.md)
+> [!div class="nextstepaction"]
+> [Create and train a custom model](model-customization.md)
diff --git a/articles/ai-services/computer-vision/how-to/generate-thumbnail.md b/articles/ai-services/computer-vision/how-to/generate-thumbnail.md
@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -40,8 +40,9 @@ To call the API, do the following steps:
 
 A successful response writes the thumbnail image to the file specified in `<thumbnailFile>`. If the request fails, the response contains an error code and a message to help determine what went wrong. If the request seems to succeed but the created thumbnail isn't a valid image file, it's possible that your key is not valid.
 
-## Next steps
+## Next step
 
 If you'd like to call Image Analysis APIs using a native SDK in the language of your choice, follow the quickstart to get set up.
 
-- [Quickstart (Image Analysis)](../quickstarts-sdk/image-analysis-client-library.md)
+> [!div class=nextstepaction]
+> [Quickstart (Image Analysis)](../quickstarts-sdk/image-analysis-client-library.md)
diff --git a/articles/ai-services/computer-vision/how-to/shelf-planogram.md b/articles/ai-services/computer-vision/how-to/shelf-planogram.md
@@ -7,13 +7,15 @@ manager: nitinme
 ms.service: azure-ai-vision
 ms.custom: build-2023
 ms.topic: how-to
-ms.date: 02/12/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
 
 # Shelf planogram compliance (preview)
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 A planogram is a diagram that indicates the correct placement of retail products on shelves. The Planogram Compliance API lets you compare analysis results from a photo to the store's planogram input. It returns an account of all the positions in the planogram, and whether a product was found in each position.
 
 :::image type="content" source="../media/shelf/planogram.png" alt-text="Photo of a retail shelf with detected products outlined and planogram position rectangles outlined separately.":::
@@ -298,7 +300,7 @@ Paired planogram position ID and corresponding detected object from product unde
 | `positionId` | string | The position ID from the planogram matched to the corresponding detected object. | No |
 | `detectedObject` | DetectedObjectApiModel | Describes a detected object in an image. | No |
 
-## Next steps
+## Related content
 
 * [Image Analysis overview](../overview-image-analysis.md)
 * [API reference](/rest/api/computervision/operation-groups?view=rest-computervision-2023-04-01-preview)
diff --git a/articles/ai-services/computer-vision/includes/spatial-analysis-deprecation.md b/articles/ai-services/computer-vision/includes/spatial-analysis-deprecation.md
@@ -6,7 +6,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: include
-ms.date: 09/10/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
diff --git a/articles/ai-services/computer-vision/spatial-analysis-camera-placement.md b/articles/ai-services/computer-vision/spatial-analysis-camera-placement.md
@@ -7,7 +7,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: how-to
-ms.date: 01/20/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ---
@@ -153,7 +153,7 @@ The following illustrations simulate the view from a camera placed in the ideal
 
 Organic queues are lines that form organically. This style of queue is acceptable if queues don't form beyond 2-3 people and the line forms within the zone definition. If the queue length is typically more than 2-3 people, we recommend using a retractable belt barrier to help guide the queue direction and ensure the line forms within the zone definition.
 
-## Next steps
+## related content
 
 <!--* [Deploy a People Counting web application](spatial-analysis-web-app.md)-->
 * [Configure Spatial Analysis operations](./spatial-analysis-operations.md)
diff --git a/articles/ai-services/computer-vision/spatial-analysis-container.md b/articles/ai-services/computer-vision/spatial-analysis-container.md
@@ -7,7 +7,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/19/2024
+ms.date: 02/21/2025
 ms.collection: "ce-skilling-fresh-tier2, ce-skilling-ai-copilot"
 ms.author: pafarley
 ms.custom: devx-track-azurecli
@@ -568,7 +568,7 @@ In this article, you learned concepts and workflow for downloading, installing,
 * Container images run as IoT Modules in Azure IoT Edge.
 * Configure the container and deploy it on a host machine.
 
-## Next steps
+## Related content
 
 <!--* [Deploy a People Counting web application](spatial-analysis-web-app.md)-->
 * [Configure Spatial Analysis operations](spatial-analysis-operations.md)
diff --git a/articles/ai-services/computer-vision/spatial-analysis-local.md b/articles/ai-services/computer-vision/spatial-analysis-local.md
diff --git a/articles/ai-services/computer-vision/spatial-analysis-operations.md b/articles/ai-services/computer-vision/spatial-analysis-operations.md
diff --git a/articles/ai-services/computer-vision/spatial-analysis-zone-line-placement.md b/articles/ai-services/computer-vision/spatial-analysis-zone-line-placement.md
diff --git a/articles/ai-services/computer-vision/use-case-alt-text.md b/articles/ai-services/computer-vision/use-case-alt-text.md
diff --git a/articles/ai-services/computer-vision/use-case-dwell-time.md b/articles/ai-services/computer-vision/use-case-dwell-time.md
diff --git a/articles/ai-services/computer-vision/use-case-queue-time.md b/articles/ai-services/computer-vision/use-case-queue-time.md
diff --git a/articles/ai-services/computer-vision/vehicle-analysis.md b/articles/ai-services/computer-vision/vehicle-analysis.md
