1222 results about "Saliency map" patented technology

Automatically determining image quality of a machine generated image may generate a local saliency map of the image to obtain a set of unsupervised features. The image is run through a trained convolutional neural network (CNN) to extract a set of supervised features from a fully connected layer of the CNN, the image convolved with a set of learned kernels from the CNN to obtain a complementary set of supervised features. The set of unsupervised features and the complementary set of supervised features are combined, and a first decision on gradability of the image is predicted. A second decision on gradability of the image is predicted based on the set of supervised features. Whether the image is gradable is determined based on a weighted combination of the first decision and the second decision.

Described, is a system for object detection via multi-scale attentional mechanisms. The system receives a multi-band image as input. Anti-aliasing and downsampling processes are performed to reduce the size of the multi-band image. Targeted contrast enhancement is performed on the multi-band image to enhance a target color of interest. A response map for each target color of interest is generated, and each response map is independently processed to generate a saliency map. The saliency map is converted into a set of detections representing potential objects of interest, wherein each detection is associated with parameters, such as position parameters, size parameters, an orientation parameter, and a score parameter. A post-processing step is applied to filter out false alarm detections in the set of detections, resulting in a final set of detections. Finally, the final set of detections and their associated parameters representing objects of interest is output.

In some embodiments, the invention includes a method for locating text in digital images. The method includes scaling a digital image into images of multiple resolutions and classifying whether pixels in the multiple resolutions are part of a text region. The method also includes integrating scales to create a scale integration saliency map and using the saliency map to create initial text bounding boxes through expanding the boxes from rectangles of pixels including at least one pixel to include groups of at least one pixel adjacent to the rectangles, wherein the groups have a particular relationship to a first threshold. The initial text bounding boxes are consolidated. In other embodiments, a method includes classifying whether pixels are part of a text region, creating initial text bounding boxes, and consolidating the initial text bounding boxes, wherein the consolidating includes creating horizontal projection profiles having adaptive thresholds and vertical projection profiles having adaptive thresholds.

The invention discloses a method for evaluating the visual comfort degree of a three-dimensional image. The method comprises the steps that first a three-dimensional saliency map of a right viewpoint image is obtained by extracting a saliency map of the right viewpoint image and a deep saliency map of a right parallax image, then parallax amplitude, parallax gradient and spatial frequency characteristics are extracted according to three-dimensional saliency map weighing, a characteristic vector of the three-dimensional image is obtained, characteristic vectors of all the three-dimensional images in a three-dimensional image set are trained by utilizing support vector regression, finally each three-dimensional image in the three-dimensional image set is tested by utilizing a support vector regression training model obtained through training, and an objective visual comfort degree evaluation predicted value is obtained. The method has the advantages that characteristic vector information of the three-dimensional images has strong stability and can well reflect the variation situation of the visual comfort degree of the three-dimensional images, so that correlation of an objective evaluation result and subjective perception is effectively improved.

A mobile device uses an image-driven view management approach for annotating images in real-time. An image-based layout process used by the mobile device computes a saliency map and generates an edge map from a frame of a video stream. The saliency map may be further processed by applying thresholds to reduce the number of saliency levels. The saliency map and edge map are used together to determine a layout position of labels to be rendered over the video stream. The labels are displayed in the layout position until a change of orientation of the camera that exceeds a threshold is detected. Additionally, the representation of the label may be adjusted, e.g., based on a plurality of pixels bounded by an area that is coincident with a layout position for a label in the video frame.

Systems and methods for image attention analysis are described. In one aspect, image attention is modeled by preprocessing an image to generate a quantized set of image blocks. A contrast-based saliency map for modeling one-to-three levels of image attention is then generated from the quantized image blocks.

The invention provides a visual saliency detection method combined with image classification. The method comprises the steps of utilizing a visual saliency detecting model which comprises an image coding network, an image decoding network and an image identification model, using a multidirectional image as an input of the image coding network, and extracting an image characteristic on the condition of multiple resolution as a coding characteristic vector F; fixing a weight except for the last two layers in the image coding network, and training network parameters for obtaining a visual saliency picture of an original image; using the F as the input of the image decoding network, and performing normalization processing on the saliency picture which corresponds with the original image; for the input F of the image decoding network, finally obtaining a generated visual saliency picture through an upsampling layer and a nonlinear sigmoid layer; by means of the image identification network, using the visual saliency picture of the original image and the generated visual saliency picture as the input, performing characteristic extraction by means of a convolutional layer with a small convolution kernel and performing pooling processing, and finally outputting probability distribution of the generated picture and probability distribution of classification labels by means of three total connecting layers. The method provided by the invention realizes quick and effective image analysis and determining and furthermore realizes good effects such as saving manpower and physical resource costs and remarkably improving accuracy in practices such as image marking, supervising and behavior predicating.