A sign language recognizer is configured to detect interest points in an extracted sign language feature, wherein the interest points are localized in space and time in each image acquired from a plurality of frames of a sign language video; apply a filter to determine one or more extrema of a central region of the interest points; associate features with each interest point using a neighboring pixel function; cluster a group of extracted sign language features from the images based on a similarity between the extracted sign language features; represent each image by a histogram of visual words corresponding to the respective image to generate a code book; train a classifier to classify each extracted sign language feature using the code book; detect a posture in each frame of the sign language video using the trained classifier; and construct a sign gesture based on the detected postures.

A sign language recognizer is configured to detect interest points in an extracted sign language feature, wherein the interest points are localized in space and time in each image acquired from a plurality of frames of a sign language video; apply a filter to determine one or more extrema of a central region of the interest points; associate features with each interest point using a neighboring pixel function; cluster a group of extracted sign language features from the images based on a similarity between the extracted sign language features; represent each image by a histogram of visual words corresponding to the respective image to generate a code book; train a classifier to classify each extracted sign language feature using the code book; detect a posture in each frame of the sign language video using the trained classifier; and construct a sign gesture based on the detected postures.

Described is a system for detecting multiple salient objects in an image using low power hardware. From consecutive pair of image frames of a set of input image frames, image channels are generated. The image channels are resized into multiple image scales that specify a relative size of a salient object in the image frames. A patch-based spectral transform is applied to overlapping image patches in the resized image channel, generating salient patches. Saliency patches are combined into a saliency map for each resized image channel, resulting in multiple saliency maps. The saliency maps are combined into an aggregate saliency map. An adaptive threshold is applied to the aggregate saliency map to determine which pixels in the aggregate saliency map correspond to a detected salient object region including a salient object. An object bounding box is generated for each salient object and output to a display.

The invention discloses a method and a system of detecting and recognizing a vehicle logo based on Selective Search, the method comprising: positioning a vehicle plate on an original image of a vehicle to obtain a vehicle plate position; coarsely positioning a vehicle logo on the original image to obtain a coarse positioning image of the vehicle logo; selecting vehicle logo candidate areas in the coarse positioning image; performing target positioning in the vehicle logo candidate areas with the Selective Search to obtain a set of target regions; training a vehicle logo location classifier with Spatial Pyramid Matching based on Sparse Coding (ScSPM) to determine the vehicle logo from the set of target regions to obtain a vehicle logo position; and training a multi-class vehicle logo recognition classifier with the ScSPM to conduct a specific type-recognition for the vehicle logo to obtain a vehicle logo recognition result.

An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

A method includes determining, at an image processing device, object quality values for a plurality of objects represented in an image. The object quality values are based on portions of image data for the image. The object quality values include a blurriness value for each object and a color value for each object. The method includes accessing, via the image processing device, object category metrics associated with an object category. The object category metrics include a blurriness metric for each object and a color metric for each object. The method also includes performing, with the image processing device, a particular image processing operation for the image based on comparisons of the object quality values for each object to corresponding object category metrics.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for categorizing items presented on webpages. An example method includes: extracting text and an image from a webpage including an item to be categorized; providing the text as input to at least one text classifier; providing the image as input to at least one image classifier; receiving at least one first score as output from the at least one text classifier, the at least one first score including a first predicted category for the item; receiving at least one second score as output from the at least one image classifier, the at least one second score including a second predicted category for the item; and combining the at least one first score and the at least one second score to determine a final predicted category for the item.

An electronic device includes a display, and a processor functionally connected with the display. The processor is configured to output content including one or more objects through the display, receive user input for specifying at least one point in the entire region of the content, determine a portion of an entire region with respect to the at least one point as a search region, obtain a saliency map associated with the content based on the search region, and determine a region of interest of the user based on the saliency map. Alternatively, the processor is configured to obtain an index map associated with the content by dividing the entire region of the content into similar regions according to a preset criterion and determine the region of interest of the user by overlapping the saliency map and the index map. It is possible to provide other embodiments.

A system for place recognition is described herein. The system for place recognition comprises a plurality of sensors, a memory, and a processor. The memory is to store instructions and is communicatively coupled to the plurality of sensors. The processor is communicatively coupled to the plurality of sensors and the memory. When the processor is to execute the instructions, the processor is to detect features in a current frame and extract descriptors of the features of the current frame. The processor is also to generate a vocabulary tree using the descriptors and determine candidate key frames based on the vocabulary tree and detected features. The processor also is to perform place recognition via a first stage matching and a second stage matching.

A sign language recognizer is configured to detect interest points in an extracted sign language feature, wherein the interest points are localized in space and time in each image acquired from a plurality of frames of a sign language video; apply a filter to determine one or more extrema of a central region of the interest points; associate features with each interest point using a neighboring pixel function; cluster a group of extracted sign language features from the images based on a similarity between the extracted sign language features; represent each image by a histogram of visual words corresponding to the respective image to generate a code book; train a classifier to classify each extracted sign language feature using the code book; detect a posture in each frame of the sign language video using the trained classifier; and construct a sign gesture based on the detected postures.