Generating an image of a kanji character did not transfer at test to the decision as to whether the visually presented kanji character was vertically segmented or not, whereas it did transfer to a semantic decision as to whether the kanji character had a concrete or abstract meaning. Seeing a...
With segmentation, an image is divided into a set of non-overlapping regions, each with its particular shape, border, and semantic meaning. When applied to multiple tissue compartments, i.e., in a multi-class fashion, tissue segmentation can allow to distinguish the tumor from other tissues, ...
Image Segmentation Using Hierarchical Merge Tree and Contour shape Current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse. A classical approach to obtain a single meaning... SM Yawalkar 被引量: 0发表: 2017年 Image Segmentation through a Hierarc...
Composite utterances are utterances that are built from multiple signs of multiple types, meaning that in any conversational 'move' speech, gestures, eye-g... Terry,Janzen - 《Cognitive Linguistics》 被引量: 6发表: 2017年 The multimodal texture of engagement: Prosodic language, gaze and posture...
CNNs provide the benefit that their learned features are translationally equivariant, meaning that image features can be recognized regardless of their position in the image. This makes such models useful for processing images with multiple similar features, and robust against variation in position or ...
It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL...
Final image contains only those red objects which are triangular or circular in shape. Finding the black regions within the accepted red objects area, results in extraction of pictogram. This pictogram is then matched with templates in database, hence recognizing the meaning of road sign. The ...
Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose...
Every layer within a deep learning model must store its weights, gradients, and neuron activations, meaning that as more blocks are added, the model requires more memory to store these quantities during training and inference. These complexity considerations are why optimizations like dilated ...
Reduced sensitivity is due to an increase in false negatives (FNs), meaning the algorithm fails to predict the specific class and suggests the pixel belongs to the background or to an incorrect foreground class. Analog, a reduced specificity is caused by an increase in false positives (FPs)....