Embodiments of the present application relate to a method, apparatus, and system for detecting a specified image identifier. The method includes retrieving a target image to be detected from a predetermined area, binarizing the target image to be detected to obtain a target binary image to be detected, calibrating connected domains of the target binary image to be detected, successively retrieving image features of candidate connected domains, and comparing the image features corresponding to the candidate connected domains to image features of a standard specified identifier image, wherein the candidate connected domains are determined based at least in part on the calibration of the connected domains, and determining a candidate connected domain as the location of the specified identifier image based at least in part on the comparison of the image features corresponding to the candidate connected domains to image features of the standard specified identifier image.

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.

Techniques for processing an image of a check captured using a mobile device are provided. The check image is processed to determine whether the check can be deposited at a bank via a mobile deposit process. The system can identify regions of the check—such as the endorsement area—to determine if the check has been properly endorsed. The system can be implemented on a mobile device and/or a server, where the mobile device routes the check image to the server for processing. If the check cannot be deposited, a rejection is forwarded in real time to the mobile device for possible correction.

There is provided an encoding apparatus. An acquiring unit acquires an index value of a noise amount by analyzing at least a part of an optical black area that is included in raw data. A quantizing unit quantizes the raw data based on the index value. An encoding unit encodes the quantized raw data. If the index value is a first value, the quantizing unit quantizes the raw data with a first quantization step. If the index value is a second value corresponding to a larger noise amount than a noise amount in a case where the index value is the first value, the quantizing unit quantizes the raw data with a second quantization step that is larger than the first quantization step.

A photographic scene replacement system includes a photographic scene with a detectable pattern. The system operates to capture a digital photograph of a subject and the photographic scene having the detectable pattern with a digital camera when the subject is arranged between the digital camera and the photographic scene. The system also operates to process the digital photograph at least in part by automatically detecting the detectable pattern in the digital photograph, to distinguish the subject from the photographic scene in the digital photograph.

A composition information obtaining unit calculates a mammary gland/fat ratio and a first information obtaining unit obtains imaged contrast information representing a contrast of the radiation image. A second information obtaining unit sets target application condition of X-ray, and obtains target contrast information representing an intended contrast for the radiation image based on the intended application condition. A contrast correction amount determination unit determines a contrast correction amount based on the imaged contrast information and the target contrast information. An image processing unit performs image processing, including gradation processing based on the determined contrast correction amount, on the radiation image, and obtains a processed radiation image.

The finger print detection method includes: accessing a part of an input image to generate a checked image; setting a gray level range, wherein each of a gray level of the gray level rang is larger than a minimum gray level and smaller than a maximum gray level; obtaining a detected pixel number of the checked image corresponding to all of the gray levels within the gray level range; and, checking whether the detected pixel number is larger than a product value of a preset ratio and a total pixel number of the checked image or not to determine whether the checked image is a finger print or not.

Embodiments of the present application relate to a method, apparatus, and system for detecting a specified image identifier. The method includes retrieving a target image to be detected from a predetermined area, binarizing the target image to be detected to obtain a target binary image to be detected, calibrating connected domains of the target binary image to be detected, successively retrieving image features of candidate connected domains, and comparing the image features corresponding to the candidate connected domains to image features of a standard specified identifier image, wherein the candidate connected domains are determined based at least in part on the calibration of the connected domains, and determining a candidate connected domain as the location of the specified identifier image based at least in part on the comparison of the image features corresponding to the candidate connected domains to image features of the standard specified identifier image.

A camera system processes images based on image activity data. The camera system includes an image sensor, an image pipeline, an encoder and a memory. The image sensor converts light incident upon the image sensor into raw image data. The image pipeline converts raw image data into color-space image data and calculates activity variances of the color-space image data. The encoder can determine one or more of quantization levels, block type (Intra vs Inter), determining transform size and type, and determining GOP structure or reference frame spacing for the color-space image data based on the activity variances. The memory stores the color-space image data and the activity variances.

A method for performing registration plate detection includes: performing image processing on image data of a predetermined region in an image to generate an edge image; and performing registration plate detection according to the edge image and the image data of the predetermined region to determine a location of a registration plate image within the predetermined region, for performing a post-processing corresponding to the registration plate image. The step of performing the image processing includes: performing gradient calculations on image data of the predetermined region to generate a gradient image, where the gradient image includes gradient data of the predetermined region; performing edge threshold estimation on the gradient data of the predetermined region, to update an edge threshold through a set of iterative calculations; and generating the edge image according to the edge threshold and according to selective gradient data of the predetermined region.