We continue discussing Signature verification methods. We reviewed the stages involved with Signature verification. Then we also enumerated the feature extraction techniques. After that, we compared online and offline verification techniques. We also discussed the limitations of image processing and the adaptations for video processing.Then we proceeded to discussing image embedding in general.
We discussed Convolutional Neural Network (CNN) in image and object embedding in a shared space followed by shape signature and image retrieval.
The purpose of the embedding is to map an image to a point in the embedding space so that it is close to a point attributed to a 3D model of a similar object. A large amount of training data consisting of images synthesized from 3D shapes is used to train the CNN.
By using synthesized images, the embedding space is computed from clean 3D models without noise. This enables better object similarities. In addition 2D shape views are tossed in which boost the shape matching. This use of embedding space is a novel approach and enables a better domain for subsequent image and shape retrievals. Moreover the embedding space does away with linear classifiers. This yields robust comparision of real world images to 3D models. Previously, this was susceptible to image nuisance factors from real world images and the linear classifiers could not keep up. On the other hand the use of CNN mitigates this because it does better with image invariance learning - a technique that focuses on the salient invariant embedded objects rather than noise.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well. In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The shared embedding space allows new images to be introduced into the space anytime. The CNN merely takes the new image as input and uses the output. If we have to embed new shapes, then it must preserve the pairwise distances between the added shape and the existing shapes within the space. The embedding space is constructed from an initial collection of 3D shapes. Introducing new shapes subsequently tends to violate the space. Instead if we could treat this an optimization problem, then we can preserve the criteria for the embedding space.
We discussed Convolutional Neural Network (CNN) in image and object embedding in a shared space followed by shape signature and image retrieval.
The purpose of the embedding is to map an image to a point in the embedding space so that it is close to a point attributed to a 3D model of a similar object. A large amount of training data consisting of images synthesized from 3D shapes is used to train the CNN.
By using synthesized images, the embedding space is computed from clean 3D models without noise. This enables better object similarities. In addition 2D shape views are tossed in which boost the shape matching. This use of embedding space is a novel approach and enables a better domain for subsequent image and shape retrievals. Moreover the embedding space does away with linear classifiers. This yields robust comparision of real world images to 3D models. Previously, this was susceptible to image nuisance factors from real world images and the linear classifiers could not keep up. On the other hand the use of CNN mitigates this because it does better with image invariance learning - a technique that focuses on the salient invariant embedded objects rather than noise.
The CNN approach consists of four major components: embedding space construction, training image synthesis, CNN training phase, and the final testing phase. In the first phase, a collection of 3D images is embedded into a common space. In the second phase, the training data is synthesized using 3D shapes in a rendering process which yields annotations as well. In the third phase, a network is trained to learn the mapping between images and 3D shape induced embedding space. Lastly, the trained network is applied on new images to obtain an embedding into the shared space. This facilitates image and shape retrieval.
The shared embedding space allows new images to be introduced into the space anytime. The CNN merely takes the new image as input and uses the output. If we have to embed new shapes, then it must preserve the pairwise distances between the added shape and the existing shapes within the space. The embedding space is constructed from an initial collection of 3D shapes. Introducing new shapes subsequently tends to violate the space. Instead if we could treat this an optimization problem, then we can preserve the criteria for the embedding space.
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