1963 results about "Change detection" patented technology

Systems and methods for automatically importing, refreshing and maintaining corrections to a list of contacts through addition, deletion, and change detection, and for merging disparate sources of data into a single unified list of contacts, according to configurable rule sets for resolving conflicts between the merged sources' values for any given field. Record sets are compared and automatically matched without requiring a unique contact identifier or key field; new records and deleted records are detected; conflicting information for any given field in a record is resolved; and updates to a local database are applied such that any override or augmentation of the data in the local database can persist for a given record. Multiple overlapping contact data sources are merged so as to identify common records, and the data combined so as to preserve as much information as possible, while concurrently handling conflicting data as it is encountered.

Video is processed by maintaining a background model for the video, detecting a target in the video, detecting if the target is a stationary target, and classifying the stationary target as an insertion in the background model or a removal from the background model.

A display input device comprises a display unit having a flexibility; and a first form change detection unit having a flexibility. The first form change detection unit is able to detect a deformation ascribed to the flexibility as a change in a electrical property. A display input system further comprises a display driving unit that supplies a display signal to the display unit and a signal judging unit that judges a input data based on the change in a electrical property in the first form change detection unit. The input of a first data can be performed by adding the deformation to the display input device.

Aspects of the invention relate to image change detection systems, methods, and articles of manufacture. According to one aspect, a method of identifying differences between a plurality of images is described. The method includes loading a source image and a target image into memory of a computer, constructing source and target edge images from the source and target images to enable processing of multiband images, displaying the source and target images on a display device of the computer, aligning the source and target edge images, switching displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.

Systems and methods for foreground analysis in real-time video include background subtraction and foreground detection, shadow removal, quick lighting change adaptation, static foreground region detection, foreground fragment reduction, and frame level change detection. Processes include background image extraction and foreground detection, integrating texture information of the background image and a current frame to remove false positive foreground areas resulting from lighting changes, integrating pixel intensity information by determining a cross-correlation of intensities between a current frame and the background image for each pixel in a foreground mask to remove image shadows. Static foreground region detection and fragment reduction are also included.

A system and method for on-the-fly detection of scene changes within a video stream through statistical analysis of a portion of the macroblocks comprising each video frame as they are processed using inter-frame coding. If the statistical analysis of the selected macroblocks of the current frame differs from the previous frame by exceeding predetermined thresholds, the current video frame is assumed to be a scene change. Once a scene change is detected, the remainder of the video frame is encoded as an intra-frame, intra-macroblocks, or intra slices, through implementation of one or more predetermined or adaptively adjusted quantization parameters to reduce computational complexity, decrease power consumption, and increase the resulting video image quality. As decoding is the inverse of encoding, these improvements are similarly recognized by a decoder as it decodes a resulting encoded video stream.

A digital evidential camera system is for detecting the alteration of image data obtained by photographing an object. The system comprises the camera and an alteration detection unit. The camera includes an image pickup unit for picking up an image of the object and an encryption processing unit for generating the alteration detection data using an encryption key built therein from the image data obtained by the image pickup unit. The alteration detection unit decrypts the alteration detection data using a decryption key corresponding to the encryption key, and detects whether the image data is altered based on the result of decryption.

A system and method for detecting changes by comparing images which cover the same physical area but are collected at different times, the system comprising: at least one input for inputting an image of a target area; the image of the target area having signatures representing outstanding features; at least one processor operating to divide the image of a target area into a plurality of target subimages; at least one memory comprising reference data comprising reference subimages taken at or near the target area at various times, the at least one processor operating to determine a sparse image representation from the reference data; the sparse image representation of the target data being a linear combination of reference data from corresponding reference subimages stored in the at least one memory; the at least one processor operating to compare the target image to the sparse image representation and to match signatures from the target image to the sparse image representation to register the images and perform change detection.

This invention relates to methods of feature extraction from MPEG-2 and MPEG-4 compressed video sequences. The spatio-temporal compression complexity of video sequences is evaluated for feature extraction by inspecting the compressed bitstream and the complexity is used as a descriptor of the spatio-temporal characteristics of the video sequence. The spatio-temporal compression complexity measure is used as a matching criterion and can also be used for absolute indexing. Feature extraction can be accomplished in conjunction with scene change detection techniques and the combination has reasonable accuracy and the advantage of high simplicity since it is based on entropy decoding of signals in compressed form and does not require computationally expensive inverse Discrete Cosine Transformation (DCT).

An active noise control system is provided to specify an input transducer, an error transducer, an output transducer and an active noise controller for generating an anti-phase canceling acoustic signal to attenuate an input noise and to output a reduced noise. The active noise control system also performs on-line secondary path modeling. For this purpose the active noise control system comprises, in addition to known systems, a secondary path change detection circuitry (6), a signal distinguishing circuitry (7) and an auxiliary noise control circuitry (8), all of them being used to model said secondary path. FIG. 3

Techniques are provided for discovering services available in accordance with an information network. For example, the invention provides methods and apparatus for dynamic service discovery from at least one chain of one or more service description documents employing one or more of automatic change detection of the chain, result aggregation and caching capability. The invention enables businesses to easily retrieve up-to-date web services linked and nested multi-level deep in the service description documents.

Techniques are disclosed for a computer vision engine to update both a background model and thresholds used to classify pixels as depicting scene foreground or background in response to detecting that a sudden illumination changes has occurred in a sequence of video frames. The threshold values may be used to specify how much pixel a given pixel may differ from corresponding values in the background model before being classified as depicting foreground. When a sudden illumination change is detected, the values for pixels affected by sudden illumination change may be used to update the value in the background image to reflect the value for that pixel following the sudden illumination change as well as update the threshold for classifying that pixel as depicting foreground/background in subsequent frames of video.

A method includes matching at least portions of first, second signals using local self-similarity descriptors of the signals. The matching includes computing a local self-similarity descriptor for each one of at least a portion of points in the first signal, forming a query ensemble of the descriptors for the first signal and seeking an ensemble of descriptors of the second signal which matches the query ensemble of descriptors. This matching can be used for image categorization, object classification, object recognition, image segmentation, image alignment, video categorization, action recognition, action classification, video segmentation, video alignment, signal alignment, multi-sensor signal alignment, multi-sensor signal matching, optical character recognition, image and video synthesis, correspondence estimation, signal registration and change detection. It may also be used to synthesize a new signal with elements similar to those of a guiding signal synthesized from portions of the reference signal. Apparatus is also included.

Systems, devices and methods provide for evaluation of heart failure symptoms. Sensor data associated with one or more symptoms of heart failure are acquired and trended. Statistical features, such as slope, are extracted from the data trend in a moving window and are used to develop a cumulative sum. The cumulative sum is compared to a threshold value or V-mask to detect a shift in cumulative sum indicating changes in heart failure symptoms. A shift beyond the threshold value may trigger an alert or implementation of therapy.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a remote sensing image building change detection method, which comprises the following operation steps: (1) reading an image and preprocessing the image; (2) making a sample data set; (3) constructing a network model based on an attention mechanism and a feature pyramid; (4) selecting a training sample to train the network model; (5) selecting a verification sample to verify the network model; and (6) classifying by using the trained model, and outputting a final change detection result. According to the method, a feature pyramid network is introduced, an attention mechanism is used in the multi-scale feature fusion process of all levels, and features are enhanced layer by layer tobe used for target detection of different scales; through application of deformable convolution and cavity convolution, the network obtains the feature expression capability of automatically adaptingto object deformation, the feature size is reserved while the receptive field is expanded, multi-scale information is obtained, the false alarm rate is effectively reduced, and the detection precisionis improved.

A technique is provided for comparative image analysis and/or change detection using computer assisted detection and/or diagnosis (CAD) algorithms. The technique includes registering two or more images, comparing the images with one another to generate a change map, and detecting anomalies in the images based on the change map.

Input means (31,32,33) receives signals representing stereo image. Image change detection means (36) detects changes with time in stereo image represented by the signals received from the input means. High attention center presumable area determination means (37) determines a peculiar local area of a displayed image area to be an observer's high attention center presumable area according to a result of detection by the image change detection means. Depth detection means (38,39) obtains information representing the depth of the image in the particular local area specified by the high attention center presumable area determination means. Depth control means (40) controls a quantity concerning the depth of stereo image represented by the signals received form the input means according to the depth information received form the depth detection means. Output means (41,42) outputs signals representing stereo image, with the quantity concerning the depth having been controlled by the depth control means, as stereo image signals to be displayed on stereo image display means.

Aspects of a method and system for change detection in localization and tracking of objects in a smart video camera are provided. A programmable surveillance video camera comprises processors for detecting objects in a video signal based on an object mask. The processors may generate a textual representation of the video signal by utilizing a description language to indicate characteristics of the detected objects, such as shape, texture, color, and/or motion, for example. The object mask may be based on a detection field value generated for each pixel in the video signal by comparing a first observation field and a second observation field associated with each of the pixels. The first observation field may be based on a difference between an input video signal value and an estimated background value while the second observation field may be based on a temporal difference between first observation fields.

A motion driven event alert system and method synchronize output of follow-up event alerts on a mobile electronic device to changes in the state of motion of the device. Generally, the device outputs an initial alert in response to receipt of a new event from a wireless network. If a user fails to take action on the device after the device outputs the initial alert, the device determines a state of motion of the device and monitors for a change in the state of motion. When a change is detected, the device outputs a follow-up alert. If the user fails to take action on the device after the device outputs the follow-up alert, the device repeats the steps of motion state determination, motion state change monitoring, motion state change detection and output of another follow-up alert.