Patent application number | Description | Published |
20090268985 | Reduced Hardware Implementation For A Two-Picture Depth Map Algorithm - An imaging system generates a picture depth map from a pair of reduced resolution images. The system captures two full resolution images, receives image reduction image information and creates two reduced resolution images. The system computes a blur difference between the two reduced resolution images at different image locations. The system calculates the depth map based on the blur difference between the two reduced resolution images at different image locations. | 10-29-2009 |
20100079608 | Method And Apparatus For Super-Resolution Imaging Using Digital Imaging Devices - A super-resolution image is generated from a sequence of low resolution images. In one embodiment, the image shift information is measured for each of the low resolution images using an image stabilization component of an imaging device. The shift information is used to generate the super-resolution image. In another embodiment, the blurs are calculated for each of the low resolution images and are used to generate the super-resolution image. | 04-01-2010 |
20100080482 | Fast Camera Auto-Focus - A camera auto-focuses using computed blur differences between images of a three-dimensional scene. The camera computes the blur difference between two images of the scene acquired at two different picture numbers. The camera uses the computed blur difference to predict a third picture number, where the camera uses the third picture number to auto-focus a camera lens on the scene. | 04-01-2010 |
20100194971 | TWO-DIMENSIONAL POLYNOMIAL MODEL FOR DEPTH ESTIMATION BASED ON TWO-PICTURE MATCHING - Apparatus and method for electronically estimating focusing distance between a camera (still and/or video camera) and a subject. Images at different focal positions of a calibration target are collected with distances between subject positions. In one aspect, histogram matching is performed to reduce noise error. A focus matching model is then generated in response to detected blur differences between successive images of the calibration target. The focus matching model is preferably converted to a polynomial equation of a desired order to smooth out image collection noise. The focus matching model is stored for access during operation. In use, the distance to subject is estimated in response to capturing images, detecting blur differences between the images and entering the blur difference information into the matching model. | 08-05-2010 |
20110050930 | FAST ITERATIVE MOTION ESTIMATION METHOD ON GRADUALLY CHANGING IMAGES - A fast iterative motion estimation method enables motion estimation to take place with fewer computations. The motion estimation and error determination steps are combined, each position in the search area is monitored to determine whether error value reaches the minimum over iterations by keeping track of convergence status, a refinement search is applied (thus, a smaller search area than a conventional process), and the difference of the error value and the minimum error value is used for each position in the search area to decide whether to further calculate the error value for that position. Each of these modifications helps minimize the number of computations used in motion estimation. | 03-03-2011 |
20110142287 | ALGORITHMS FOR ESTIMATING PRECISE AND RELATIVE OBJECT DISTANCES IN A SCENE - A two picture matching curve information is able to be used to determine precise object distance or relative object distance in a scene. Acquiring two images with different blur information in addition to the curve information enables a device to determine distance information of objects in a scene. The distance information is able to be used in image processing including generating a depth map which is then able to be used in many imaging applications. | 06-16-2011 |
20110150447 | AUTOFOCUS WITH CONFIDENCE MEASURE - Autofocusing is performed in response to a weighted sum of previous blur difference depth estimates at each focus adjustment iteration. Variance is also determined across both past and present estimations providing a confidence measure on the present focus position for the given picture. Focus adjustment is repeated until the variance is sufficiently low as to indicate confidence that a proper focus has been attained. The method provides more accurate and rapid focusing than achieved by the best current depth-based techniques, such as those utilizing most recent depth estimation to determine the next lens position. In contrast to this, the present apparatus and method combines all previous depth estimation results in the autofocus process to determine the next lens position based on statistical models and confidence measure. | 06-23-2011 |
20110229052 | BLUR FUNCTION MODELING FOR DEPTH OF FIELD RENDERING - A method and apparatus of depth of field rendering which simulates larger apertures for images captured at a smaller aperture. The depth of field rendering provides selective simulation of out-of-focus effects which are attainable with cameras having a larger aperture when capturing images at a smaller aperture. A blur function model is created based on the relationship between the blur change and the aperture change. This model is used to determine the blur difference which would arise between two images taken at two different apertures. Then the out-of-focus effect is generated by blurring the image in a rendering process based on the blur difference. | 09-22-2011 |
20110249173 | FOUR-DIMENSIONAL POLYNOMIAL MODEL FOR DEPTH ESTIMATION BASED ON TWO-PICTURE MATCHING - Camera depth estimation is performed in response to picture matching based on blur difference computed between images captured at different focal positions. A blur difference model is stored in the camera based on characterization of the camera with a series of matching curves in which blur difference varies depending on the focal length, aperture, subject distance, and lens focus position. A four-dimensional polynomial model is created to fit the matching curves for use in estimating subject distance. During operation, images are captured for use in estimating subject distance. Motion compensation is applied and blur difference is determined. Blur difference is utilized in the polynomial model to estimate subject distance. Subject distance estimates can be output or utilized within an auto focus process to provide accurate focus adjustments. | 10-13-2011 |
20120236170 | BLUR DIFFERENCE ESTIMATION USING MULTI-KERNEL CONVOLUTION - An apparatus and method for rapidly and accurately determining blur differences between captured images. Blur change is modeled as a point spread function from a first position to a second position, which is approximated in response to performing a series of convolutions using at least two different blur kernels having different variance. The kernel with a larger variance is used first to speed processing, after which a kernel having a smaller variance is utilized to attain desired accuracy. Any number of kernels can be utilized with decreasing variance to achieve any desired level of accuracy. The apparatus and method can be utilized within a wide range of image capture and/or processing devices, and can be utilized within camera focus mechanisms to increase focusing speed and accuracy. | 09-20-2012 |
20120249816 | FOCUS DIRECTION DETECTION CONFIDENCE SYSTEM AND METHOD - An apparatus and method for determining confidence in focus direction detection, including the steps of capturing a plurality of images, calculating sets of matching errors and blur difference estimations relating to the images, capturing a plurality of object images; calculating sets of matching errors and blur difference estimations relating to the images, calculating a confidence indicator as a function of either the matching errors or blur difference estimations, and automatically adjusting a focus control element in response to said confidence indicator exceeding a threshold value. | 10-04-2012 |
20120249833 | MOTION ROBUST DEPTH ESTIMATION USING CONVOLUTION AND WAVELET TRANSFORMS - Apparatus and method for electronically estimating focusing distance between a camera (still and/or video camera) and a subject. Images at different focal positions of a calibration target are collected to arrive at a focus matching model for a given imaging apparatus. In operation, at least two images are captured and convolutions performed which approximate the modeling of blur change as a point spread function. Wavelet transforms are applied to the images after each convolution and images are compared based on the wavelet variance differences to provide a motion robust blur difference determination. Applying the blur differences to the focus matching model provides an estimate of focusing distance, which can be utilized such as for controlling camera focus. | 10-04-2012 |
20120250999 | DETECTION OF LOW CONTRAST FOR IMAGE PROCESSING - There are many applications that conduct both generation of contrast or complexity level and motion estimation for video processing. The applications often use a block matching technique. An embedded system such as a personal digital camera is an example of such an application. Additionally, comparison of error differences around the location of minimum error in a motion estimation error table is able to be used to determine low contrast in a scene. | 10-04-2012 |
20130141537 | Methodology For Performing Depth Estimation With Defocused Images Under Extreme Lighting Conditions - A methodology for performing a depth estimation procedure with defocused images under extreme lighting conditions includes a camera device with a sensor for capturing blur images of a photographic target under extreme lighting conditions. The extreme lighting conditions may include over-exposed conditions and/or under-exposed conditions. The camera device also includes a depth generator that performs the depth estimation procedure by utilizing the captured blur images. The depth estimation procedure includes a clipped-pixel substitution procedure to compensate for the extreme lighting conditions. | 06-06-2013 |
20130141615 | System And Method For Utilizing Scene Detection In A Depth Estimation Procedure - A system and method for performing a depth estimation procedure includes a camera device with a sensor device for capturing blur images of a photographic target. Each of the captured blur images has a corresponding scene type that includes either a Gaussian scene type or a pillbox scene type. A depth generator performs a scene recognition procedure to identify the appropriate scene type for the respective blur images. The depth generator then selects an effective depth estimation procedure depending upon the detected scene type. | 06-06-2013 |
20130141630 | OPTIMAL BLUR MATCHING SELECTION FOR DEPTH ESTIMATION - Autofocusing is performed in response to a capturing object images upon which multiple depth estimation techniques are applied to yield a plurality of iterations. An iteration from one of these depth estimation techniques is selected based on results, such as based on largest absolute value, and checked. If the iteration fails the check, another of the iterations is selected and tested. Once a valid iteration is found, additional focus positions are executed in like manner from which an accurate focus position is determined. | 06-06-2013 |
20130142386 | System And Method For Evaluating Focus Direction Under Various Lighting Conditions - A system and method for generating a direction confidence measure includes a camera sensor device that captures blur images of a photographic target. A depth estimator calculates matching errors for the blur images. The depth estimator then generates the direction confidence measure by utilizing the matching errors and a dynamic optimization constant that is selected depending upon image characteristics of the blur images. | 06-06-2013 |
20130142394 | System And Method For Performing Depth Estimation Utilizing Defocused Pillbox Images - A system and method for performing a depth estimation procedure utilizing defocused pillbox images includes a camera device with a sensor device for capturing pillbox blur images of a photographic target. The camera utilizes a depth estimator for performing a Gaussianization procedure that transforms the pillbox blur images into corresponding Gaussian blur images. The Gaussianization procedure is performed by convolving the pillbox blur images with a Gaussianization kernel to generate the corresponding Gaussian blur images. The depth estimator then utilizes the Gaussian blur images for effectively performing the depth estimation procedure. | 06-06-2013 |
20130142415 | System And Method For Generating Robust Depth Maps Utilizing A Multi-Resolution Procedure - A system and method for generating robust depth maps includes a depth estimator that creates a depth map pyramid structure that includes a plurality of depth map levels that each have different resolution characteristics. In one embodiment, the depth map levels include a fine-scale depth map, a medium-scale depth map, and a coarse scale depth map. The depth estimator evaluates depth values from the fine-scale depth map by utilizing fine-scale confidence features, and evaluates depth values from the medium-scale depth map and the coarse-scale depth map by utilizing coarse-scale confidence features. The depth estimator then fuses optimal depth values from the different depth map levels into an optimal depth map. | 06-06-2013 |
20130182152 | CAMERA AUTOFOCUS ADAPTIVE BLUR MATCHING MODEL FITTING - Autofocusing is performed in response to a weighted sum of previous blur difference depth estimates after being adaptively fitted at each focus adjustment iteration. Variance is also determined across both past and present estimations providing a confidence measure on the present focus position for the given picture. In one embodiment focus adjustment are repeated until the variance is sufficiently low as to indicate confidence that a proper focus has been attained. The method increases accuracy and speed of focusing by utilizing previous depth estimates while adapting the matching data to overcome distortion, such as due to saturation, cut-off and noise. | 07-18-2013 |
20130258096 | System And Method For Performing Depth Estimation By Utilizing An Adaptive Kernel - A system and method for supporting a depth estimation procedure by utilizing an adaptive kernel includes a capture subsystem for capturing images of a photographic target. The capture subsystem includes an aperture that is adjustable for admitting reflected light from the photographic target to a sensor device. An adaptive kernel is designed in a kernel design procedure based upon symmetry characteristics of the aperture. The adaptive kernel may be designed in either a frequency-domain kernel design procedure or in a spatial-domain kernel design procedure. A depth estimator utilizes the adaptive kernel for performing the depth estimation procedure. | 10-03-2013 |
20140064552 | System And Method For Utilizing Enhanced Scene Detection In A Depth Estimation Procedure - A system for performing an enhanced scene detection procedure including a sensor device for capturing blur images of a photographic target. The blur images each correspond to a scene type that is detected from a first scene type which is typically a pillbox blur scene, and a second scene type which is typically a Gaussian scene type. A scene detector performs an initial scene detection procedure to identify a candidate scene type for the blur images. The scene detector then performs the enhanced scene detection procedure to identify a final scene type for the blur images. | 03-06-2014 |
20140132822 | MULTI-RESOLUTION DEPTH-FROM-DEFOCUS-BASED AUTOFOCUS - A hierarchical method of achieving auto focus using depth from defocus is described herein. The depth from defocus technique is performed hierarchically in the resolution that is determined to be optimal at each step. Where higher resolution gives the better accuracy but requires more computational costs, the optimal resolution is estimated based on the target accuracy and the possible max blur amount at each step, which determines the amount of the computation and the number of pixels in the focus area. The proposed multi-resolution depth-from-defocus-based autofocus enables the reduction in the required resource, which is beneficial in the system where resource is limited. | 05-15-2014 |