An image depth decoder includes an NIR image buffer, a reference image ring buffer and a pattern matching engine. The NIR image buffer stores an NIR image inputted by a stream. The reference image ring buffer stores a reference image inputted by a stream. The pattern matching engine is coupled to the NIR image buffer and the reference image ring buffer, and performs a depth computation according to the NIR image and the reference image to output at least one depth value.

Systems and methods are provided for processing and extracting content from an image captured using a mobile device. In one embodiment, an image is captured by a mobile device and corrected to improve the quality of the image. The corrected image is then further processed by adjusting the image, identifying the format and layout of the document, binarizing the image and extracting the content using optical character recognition (OCR). Multiple methods of image adjusting may be implemented to accurately assess features of the document, and a secondary layout identification process may be performed to ensure that the content being extracted is properly classified.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

The method and system may be used to provide an indication of a color value for a particular siding sample and to color match a specific siding product to the color value of the siding sample. The system receives a digital image of a siding sample and a desired color value to be matched. A color query module plots this desired color value as a desired color point in a multidimensional color space together with a plurality of color reference points. Each color reference point represents the color value of an existing siding product. The system determines a distance between the desired color point and each plotted color reference point within the color space and identifies the siding product associated with the color reference point that is located the shortest distance to the desired color point within the color space.

An image capturing device includes an image capturing unit, a light filter module, a light sensing unit, an infrared light source and a processing unit. The light filter module includes a first light filter and a second light filter. The first light filter filters an infrared and allows a visible light to pass. The second light filter filters a visible light and allows an infrared to pass. The light sensing unit senses an environmental light. When the processing unit determines that an intensity of the environmental light is higher than a threshold, the processing unit alternately switches the first light filter and the second light filter to a front of the image capturing unit, controls the image capturing unit to capture a visible light image through the first light filter, and controls the image capturing unit to capture an infrared image through the second light filter.

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.

A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.

Disclosed are a method, an apparatus, a device and a storage medium for extracting a cardiovisceral vessel from a CTA image, including: performing corrosion operation and expansion operation on an image data successively via a preset structural element to obtain a structure template, wherein the image data is a coronary angiography image after a downsampling processing, and the structure template is a structure excluding a pulmonary region; performing a transformation in layer-by-layer on slice images of the structure template to acquire a first ascending aorta structure in the structure template, and acquiring an aorta center coordinate and an aorta radius in the last layer of slice image of the structure template; and establishing a binarized spherical structure according to the aorta center coordinate and the aorta radius, and synthesizing a second ascending aorta structure by combining the first ascending aorta structure with the structure template and the binarized spherical structure.

Disclosed are embodiments for facilitating automatic-guided image capturing and presentation. In some embodiments, the method includes capturing an image of an item, removing a background of the image frame, performing manual mask editing, generating an item listing based on the manually edited mask, inferring item information from the image frame and applying the inferred item information to an item listing.

A convolutional neutral network identification efficiency increasing method is applied to a related device. The convolutional neutral network identification efficiency increasing method includes analyzing an input image to acquire foreground information, utilizing the foreground information to generate a foreground mask, and transforming the input image into an output image via the foreground mask. The output image is used to be an input of the convolutional neutral network identification for preferred object identification efficiency.