493results about "Image analysis" patented technology

Method for determining the pose of a camera with respect to at least one object of a real environment for use in authoring/augmented reality application that includes generating a first image by the camera capturing a real object of a real environment, generating first orientation data from at least one orientation sensor associated with the camera or from an algorithm which analyses the first image for finding and determining features which are indicative of an orientation of the camera, allocating a distance of the camera to the real object, generating distance data indicative of the allocated distance, determining the pose of the camera with respect to a coordinate system related to the real object of the real environment using the distance data and the first orientation data. May be performed with reduced processing requirements and/or higher processing speed, in mobile device such as mobile phones having display, camera and orientation sensor.

Enhanced accuracy finger position and motion sensors devices, algorithms, and methods are disclosed that can be used in a variety of different applications. The sensors can be used in conjunction with partial fingerprint imagers to produce improved fingerprint scanners. Such improved scanners can use image analysis techniques, such as interpolation between partial fingerprint images to correct for missing data, or discarding redundant partial fingerprint image data, to produce adequate fingerprint images even when the finger has not been applied to the sensor using an optimum technique.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. One claim recites a system for capturing imagery of retail items supported by a fixture, comprising: at least one camera unit having at least one operative camera to capture imagery of at least a portion of the retail items supported by the fixture; and one or more processors configured to process the captured imagery to discern identifying information associated with the imaged retail items. In one implementation the at least one camera unit comprises a plurality of cameras, wherein a field of view of a first one of the plurality of cameras overlaps with a field of view of a second one of the plurality of camera units. A great variety of other features, claims, combinations and arrangements are also detailed.

Described herein are systems and methods for performing multi-stage detection and classification of cancer regions from digitized images of biopsy slides. Novel methods for processing the digitized images to improve feature extraction and structure identification are disclosed, including but not limited to the use of quaternions, logarithmic mappings of color channels, and application of wavelets to logarithmic color channel mappings. The extracted features are utilized in improved machine learning algorithms that are further optimized to analyze multiple color channels in multiple dimensions. The improved machine learning algorithms include techniques for accelerating the training of the algorithms, making their application to biopsy detection and classification practical for the first time. The performance of the described systems and methods are further improved by the disclosure of a novel multistage machine learning scheme, in which additional classifiers are utilized to choose among the classes proposed by other classifiers in close cases.

The present invention provides methods, apparatus, and systems for analyzing a structure using thermal imaging. A plurality of images of a structure are automatically captured using one or more image capture devices. The images may be captured in one or more ranges of wavelengths of light. The images are then processed to generate image data for the images. The image data can then be analyzed to determine one or more properties of the structure. The images may be captured at an angle with respect to the structure of between approximately 45 to 135 degrees. The images may be captured during a time where one of indirect or no sunlight is present.

Provided are a face recognition apparatus and method in which a facial image is divided into facial component images. The apparatus includes a component division unit that divides an input facial image into a plurality of facial component images; a face descriptor generating unit that generates face descriptors using a transform matrix corresponding to the respective facial component images, the face descriptors being characteristic vectors; a registered face descriptor database (DB) that stores registered face descriptors; and an authentication unit that authenticates the input facial image by comparing face descriptors for the input facial image input from the face descriptor generating unit with the registered face descriptors and providing predetermined weights corresponding to each facial component to the comparison results of each facial component.

A system and method are disclosed for presenting projection image information, including a first image generating module or step, for generating a first image representing a first projection of a three-dimensional object; a second image generating module or step, for generating a second image representing a second projection of the three-dimensional object; an image display module or step, for displaying the first and second images; a region selection module or step, for selecting a first region in the first image; a correspondence module or step, for determining a second region in the second image that corresponds to the first region; and a marking module or step, for displaying a first mark on the first image to identify the first region, and a second mark on the second image to identify the corresponding second region.

A gesture spotting detection method and apparatus employ a shoulder-line algorithm. The shoulder-line detecting method recognizes a GSD calling gesture that occurs in a shoulder-line, head or higher part in a remote distance or a short distance, although a user does not have a fixed posture. In the method, an image of people is received, and skin information of a person in the image is detected to detect a face area. Then, the cloth color information of the person is modeled from the inputted image to detect a cloth area. An external space is defined from the image based on the body space area, and an edge is extracted from the image based on the body space and the external space. Then, shoulder-line information is acquired based on an energy function obtained based on the body space, the external space, and the edge.

A method including presenting a video sequence on a display where the video sequence includes video frames, calculating a quality indicator with respect to the video frames and automatically indicating which video frames meet or exceed a predetermined image quality threshold depending on the quality indicator.

The invention provides systems and methods for imaging a sample. In various embodiments, the invention provides a system comprising an image sensor, a laser for emitting excitation light for an infrared or near-infrared fluorophore, a visible light source, a notch beam splitter, a notch filter, a synchronization module, an image processing unit, an image displaying unit, and light-conducting channels. In various embodiments, the present invention provides a system comprising an image sensor, a laser for emitting excitation light for an infrared or near-infrared fluorophore, a laser clean-up filter, a notch filter, a white light source, an image processing unit, an image displaying unit, and light-conducting channels. In accordance with the present invention, the image sensor can detect both visible light and infrared light.

The invention provides a relevant filtering opposite-thrust target tracking method with an adaptive scale. The method comprises: an initial position and an initial scale of a to-be-tracked target in a video frame are determined, and convolution feature graphs of different layers are extracted respectively by using the initial position as the center and using a deep convolutional neural network; for the extracted convolution feature graph of each layer, tracking is carried out by using a kernel-correlation filtering tracking method to obtain a tracking result; all tracking results are combined by using an adaptive hedging algorithm to obtain a final tracking result as a final position of the to-be-tracked target, so that the to-be-tracked target in the video frame can be localized; after obtaining of the final position of the to-be-tracked target, a final scale of the to-be-tracked target is estimated by using a scale pyramid strategy; and after obtaining of the final position and the final scale of the to-be-tracked target, a to-be-tracked target image block is extracted based on the final scale by using the final position as a center and each kernel-correlation filtering tracking method is trained again to update a coefficient and a template.

Various pipe inspection systems include a camera head operatively connected to the distal end of a push-cable. Images may be automatically captured at predetermined distances of travel, or may be automatically captured based on the output signals from the auto-focus, auto-exposure and/or auto-white balance engines indicating, for example, that the camera motion within the pipe is substantially zero. Images may be captured in an automatic mode at predetermined intervals as the camera head travels within the pipe or in an override mode initiated by operator command. The system may include a data transmission circuit that transmits data between a plurality of nodes at a frequency that does not substantially interfere with a normal base band video transmission frequency.

A computer-implemented method for tracking a human face in a target video includes obtaining target video data of a human face; and estimating parameters of a target human face model, based on the target video data. A first subset of the parameters represents a geometric shape and a second subset of the parameters represents an expression of the human face. At least one of the estimated parameters is modified in order to obtain new parameters of the target human face model, and output video data are generated based on the new parameters of the target human face model and the target video data.

A registration framework is presented that registers volumetric breast images captured before surgery with intraoperative surface images. The framework may be implemented using either image-based or model-based registration techniques. The method contains the steps of: identifying an air/tissue boundary from a volumetric image created at a first time; processing the volumetric image with an image filter to emphasize the air/tissue boundary; and registering a surface optically scanned image, with the filtered volumetric image, where the surface optically scanned image is created at a second time.

The invention provides a full-automatic three-dimension characteristic extracting method. The method comprises the steps of firstly converting the three-dimension point clouds of all single view fields obtained by a stereoscopic vision measuring system into depth images of a single view field; splicing the point clouds of the single view fields; carrying out automatic searching and extracting on the corresponding depth images according to the known positioning characteristic of a workpiece to obtain the positioning characteristic of the workpiece, thus automatically converting the spliced three-dimension point clouds into the workpiece coordinate system; subsequently inputting the ideal coordinates of all characteristics to be detected of the workpiece in the workpiece design model; dividing the characteristics into a plurality of types such as aperture, tendon, plane and the like; automatically extracting the depth images respectively and obtaining the two-dimension coordinates of depth images; searching the three-dimension points of two-dimension depth image coordinates under the corresponding workpiece coordinate system; and obtaining the three-dimension information of each characteristic by calculation. The method has the characteristics of full automation, good flexibility and good expandability, and can be used for automatically extracting three-dimension characteristicsof various workpieces with known positioning characteristics.

The invention discloses a segmentation method of pathological section unconventional cells based on a multi-scale hybrid segmentation model. The method comprises steps that positive and negative samples are respectively scaled to low-resolution, medium-resolution and high-resolution images, the full convolutional network algorithm is utilized for training to acquire a convergent low-resolution segmentation model, a medium-resolution segmentation model and a high-resolution model; a multi-scale hybrid segmentation model is acquired through fusion by a model integration method; after the effective discriminating area of a new pathological section is processed through utilizing the data enhancement method during testing, the processed effective discriminating area is inputted to the multi-scale hybrid segmentation model, the probability of each pixel in the effective segmentation area is outputted, pixels with probability values greater than the threshold t are taken as abnormal cell pixels and are recorded as 1, remaining pixels are taken as normal cell pixels and are recorded as 0, binary images predicted by the multi-scale hybrid segmentation model are acquired, and post-processingon the binary images is carried out to acquire the final segmentation result. The method is advantaged in that high precision is realized, and a Dice value is above 0.869.

A method of detecting objects using multiple sets of object probability data relating to different respective expected object sizes relative to the image size comprises the steps of: (a) storing a list of locations in an image where an object has been detected, the list defining a probability data set and a probability value for each detected object; (b) detecting object locations in a set of object probability data as locations at which a local maximum probability value exceeds a threshold probability value and, if that location is substantially similar to a location previously stored in the list, where the local maximum probability value exceeds that relating to the previously stored location; and (c) repeating step (b) successively in respect of each set of probability data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

On the basis of captured images at the time of projecting multiple-frequency slit-shaped light patterns having no overlapping edge positions onto an object, the edge portions of the slit-shaped light patterns are identified. When the edge portions overlap in the captured images of two or more slit-shaped light patterns, the reliability of the computed distance values of the positions corresponding to the edges is lowered.