acrolinx

Author: PatrickFarley

articles/ai-services/computer-vision/concept-brand-detection.md

@@ -18,7 +18,7 @@ Brand detection is a specialized mode of [object detection](concept-object-detec
 
 The Azure AI Vision service detects whether there are brand logos in a given image; if there are, it returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.
 
-The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for is not detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
+The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for isn't detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
 
 ## Brand detection example
 
@@ -40,7 +40,7 @@ The following JSON responses illustrate what Azure AI Vision returns when detect
 ]
 ```
 
-In some cases, the brand detector will pick up both the logo image and the stylized brand name as two separate logos.
+In some cases, the brand detector picks up both the logo image and the stylized brand name as two separate logos.
 
 ![A gray sweatshirt with a Microsoft label and logo on it](./Images/gray-shirt-logo.jpg)
 
@@ -69,6 +69,6 @@ In some cases, the brand detector will pick up both the logo image and the styli
 
 ## Use the API
 
-The brand detection feature is part of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Brands` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"brands"` section.
+The brand detection feature is part of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Brands` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"brands"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-domain-content.md

@@ -14,15 +14,15 @@ ms.author: pafarley
 
 # Domain-specific content detection
 
-In addition to tagging and high-level categorization, Azure AI Vision also supports further domain-specific analysis using models that have been trained on specialized data.
+In addition to tagging and high-level categorization, Azure AI Vision also supports further domain-specific analysis using models that are trained on specialized data.
 
 There are two ways to use the domain-specific models: by themselves (scoped analysis) or as an enhancement to the image [categorization](./concept-categorizing-images.md) feature.
 
 ### Scoped analysis
 
 You can analyze an image using only the chosen domain-specific model by calling the [Models/\<model\>/Analyze](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API.
 
-The following is a sample JSON response returned by the **models/celebrities/analyze** API for the given image:
+The following is a sample JSON response returned by the `models/celebrities/analyze` API for the given image:
 
 ![Satya Nadella standing, smiling](./images/satya.jpeg)
 
@@ -51,7 +51,7 @@ The following is a sample JSON response returned by the **models/celebrities/ana
 
 ### Enhanced categorization analysis
 
-You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API call.
+You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API call.
 
 In this case, the 86-category taxonomy classifier is called first. If any of the detected categories have a matching domain-specific model, the image is passed through that model as well and the results are added.
 
@@ -99,7 +99,7 @@ Currently, Azure AI Vision supports the following domain-specific models:
 | celebrities | Celebrity recognition, supported for images classified in the `people_` category |
 | landmarks | Landmark recognition, supported for images classified in the `outdoor_` or `building_` categories |
 
-Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-computervision-v3.1&tabs=HTTP) API will return this information along with the categories to which each model can apply:
+Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-computervision-v3.1&tabs=HTTP) API returns this information along with the categories to which each model can apply:
 
 ```json
 {
@@ -133,6 +133,6 @@ Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-
 
 ## Use the API
 
-This feature is available through the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.1&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"details"` section.
+This feature is available through the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.1&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"details"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-object-detection.md

@@ -14,9 +14,9 @@ ms.author: pafarley
 
 # Object detection
 
-Object detection is similar to [tagging](concept-tag-images-40.md), but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat, and person, the object detection operation will list those objects with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same object in an image.
+Object detection is similar to [tagging](concept-tag-images-40.md), but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat, and person, the object detection operation lists those objects with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same object in an image.
 
-The object detection function applies tags based on the objects or living things identified in the image. There is no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the object detection function only finds objects and living things, while the tag function can also include contextual terms like "indoor", which can't be localized with bounding boxes.
+The object detection function applies tags based on the objects or living things identified in the image. There's no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the object detection function only finds objects and living things, while the tag function can also include contextual terms like "indoor," which can't be localized with bounding boxes.
 
 Try out the capabilities of object detection quickly and easily in your browser using Vision Studio.
 
@@ -25,7 +25,7 @@ Try out the capabilities of object detection quickly and easily in your browser
 
 ## Object detection example
 
-The following JSON response illustrates what the Analyze API returns when detecting objects in the example image.
+The following JSON response illustrates what the Analyze Image API returns when detecting objects in the example image.
 
 ![A woman using a Microsoft Surface device in a kitchen](./Images/windows-kitchen.jpg)
 
@@ -93,9 +93,9 @@ The following JSON response illustrates what the Analyze API returns when detect
 
 It's important to note the limitations of object detection so you can avoid or mitigate the effects of false negatives (missed objects) and limited detail.
 
-* Objects are generally not detected if they're small (less than 5% of the image).
-* Objects are generally not detected if they're arranged closely together (a stack of plates, for example).
-* Objects are not differentiated by brand or product names (different types of sodas on a store shelf, for example). However, you can get brand information from an image by using the [Brand detection](concept-brand-detection.md) feature.
+* Objects are usually not detected if they're small (less than 5% of the image).
+* Objects are usually not detected if they're arranged closely together (a stack of plates, for example).
+* Objects aren't differentiated by brand or product names (different types of sodas on a store shelf, for example). However, you can get brand information from an image by using the [Brand detection](concept-brand-detection.md) feature.
 
 ## Use the API