Object recognition is something that our brains do instantly and automatically. It requires semantic knowledge, which means you know the word and description, and you can recognize an object by those definitions. Prior knowledge is necessary. If you see a photograph of a new car but have never seen that model before, you can extrapolate on your visual and semantic knowledge of cars to recognize it as a new model. Object recognition is a hot topic for computer scientists interested in artificial intelligence. A computer system struggles with vision because it lacks our innate logic and semantic knowledge of objects. It only has a list of rules to make and educated guess of an object in a photograph, while never having seen it before.
Match the Edges
A visual picture of the outlines of objects can provide information. Some objects have distinct profiles. Highlights and shadows reveal shapes of objects. A horse is something that is tall with four legs and a tail. A bicycle tire can resemble two circles, one inside the other. Problem arise, however, when an object is seen from an oblique angle. Changes in saturation or color do little to affect edges. Adjust the contrast to clarify edges of objects.
Process of Elimination
Consider the positions of objects in photographs. There may or may not be correlation between the objects. For example, if you recognize a cup and saucer, there is a chance that the objects in view are part of a place setting and there may be related objects, such as a scone or sugar packets. Look at all of the objects as a set to discover more information about what is pictured. The relationship of objects is important. Judge a set of objects on their distance from one another and their prominence in the photo.
Greyscale Matching
Look at the photograph in black and white. Without color information, some objects become clear. For example, a field of similar grays may become clear as a sky when the color information is taken away. Black-and-white pictures show definite light, shadows and mid-tones. This technique finds what is similar in a photograph. Also, try limiting the channels to red, blue or green. Information about the objects may show themselves.
Gradient Matching
Objects with similar light patterns may be related. Gradients usually show an object's dimension. An object with a clear spherical gradient can be assumed to be round in three dimensions. The evenness of light on an object shows how regular a shape is. This technique is about texture. A smooth object has a smooth gradient.
Consult the Database
Search your image database for easily recognizable images. Objects such a bicycle or a pair of boots have clearly defined shapes that change little over the whole set of bicycles or boots. Also, search the knowledge you already have of defined objects for a match and/or similar properties. Do a comparison of similar objects for further details.
Object recognition is something that our brains do instantly and automatically. It requires semantic knowledge, which means you know the word and description, and you can recognize an object by those definitions. Prior knowledge is necessary. If you see a photograph of a new car but have never seen that model before, you can extrapolate on your visual and semantic knowledge of cars to recognize it as a new model. Object recognition is a hot topic for computer scientists interested in artificial intelligence. A computer system struggles with vision because it lacks our innate logic and semantic knowledge of objects. It only has a list of rules to make and educated guess of an object in a photograph, while never having seen it before.
Match the Edges
A visual picture of the outlines of objects can provide information. Some objects have distinct profiles. Highlights and shadows reveal shapes of objects. A horse is something that is tall with four legs and a tail. A bicycle tire can resemble two circles, one inside the other. Problem arise, however, when an object is seen from an oblique angle. Changes in saturation or color do little to affect edges. Adjust the contrast to clarify edges of objects.
Process of Elimination
Consider the positions of objects in photographs. There may or may not be correlation between the objects. For example, if you recognize a cup and saucer, there is a chance that the objects in view are part of a place setting and there may be related objects, such as a scone or sugar packets. Look at all of the objects as a set to discover more information about what is pictured. The relationship of objects is important. Judge a set of objects on their distance from one another and their prominence in the photo.
Greyscale Matching
Look at the photograph in black and white. Without color information, some objects become clear. For example, a field of similar grays may become clear as a sky when the color information is taken away. Black-and-white pictures show definite light, shadows and mid-tones. This technique finds what is similar in a photograph. Also, try limiting the channels to red, blue or green. Information about the objects may show themselves.
Gradient Matching
Objects with similar light patterns may be related. Gradients usually show an object's dimension. An object with a clear spherical gradient can be assumed to be round in three dimensions. The evenness of light on an object shows how regular a shape is. This technique is about texture. A smooth object has a smooth gradient.
Consult the Database
Search your image database for easily recognizable images. Objects such a bicycle or a pair of boots have clearly defined shapes that change little over the whole set of bicycles or boots. Also, search the knowledge you already have of defined objects for a match and/or similar properties. Do a comparison of similar objects for further details.
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