Abstract: Architecture and a method for maximally stable extremal regions (MSERs)-based detection of exudates in an ocular fundus is disclosed. The architecture includes a communication interface to receive pixels of an ocular fundus image. The architecture further includes processing circuitry that is coupled to the communication interface. The processing circuitry is configured to automatically provide labels for light image regions and dark image regions within the ocular fundus image for a given intensity threshold and find MSERs within the ocular fundus image based on the labels. The architecture also determines MSER regions based on the MSER criteria and then highlights the pixels of the ocular fundus image that are located within MSER regions to indicate the exudates in the ocular fundus. The architecture is further configured to determine MSER ellipses parameters based on MSER regions and MSER criteria and then highlight the locations of the exudates in the ocular fundus.

Abstract: Architecture and a method for maximally stable extremal regions (MSERs)-based detection of exudates in an ocular fundus is disclosed. The architecture includes a communication interface to receive pixels of an ocular fundus image. The architecture further includes processing circuitry that is coupled to the communication interface. The processing circuitry is configured to automatically provide labels for light image regions and dark image regions within the ocular fundus image for a given intensity threshold and find MSERs within the ocular fundus image based on the labels. The architecture also determines MSER regions based on the MSER criteria and then highlights the pixels of the ocular fundus image that are located within MSER regions to indicate the exudates in the ocular fundus. The architecture is further configured to determine MSER ellipses parameters based on MSER regions and MSER criteria and then highlight the locations of the exudates in the ocular fundus.

Abstract: An architecture for linear-time extraction of maximally stable extremal regions (MSERs) having an image memory, heap memory, a pointer array and processing hardware is disclosed. The processing hardware is configured to in real-time analyze image pixels in the image memory using a linear-time algorithm to identify a plurality of components of the image. The processing hardware is also configured to place the image pixels in the heap memory for each of the plurality of components of the image, generate a pointer that points to a location in the heap memory that is associated with a start of flooding for another component and store the pointer in the array of pointers. The processing hardware is also configured to access the plurality of components using the array of pointers and determine MSER ellipses based on the components and MSER criteria.

Abstract: Methods and systems for detecting and/or tracking one or more objects utilize depth data. An example method of detecting one or more objects in image data includes receiving depth image data corresponding to a depth image view point relative to the one or more objects. A series of binary threshold depth images are formed from the depth image data. Each of the binary threshold depth images is based on a respective depth. One or more depth extremal regions in which image pixels have the same value are identified for each of the binary depth threshold images. One or more depth maximally stable extremal regions are selected from the identified depth extremal regions based on change in area of the one or more respective depth extremal regions for different depths.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes communication interface and processing circuitry that is adapted in hardware to receive a data streams of an intensity image and a depth image in real-time and provide intensity labels for image regions within the intensity image that match a given intensity threshold and provide depth labels for image regions within the depth image that match a given depth threshold. The processing circuitry is also adapted in hardware to find intensity extremal regions within the intensity image based upon the intensity labels and to find depth extremal regions within the depth image based upon the depth labels. The processing circuitry determines strong extremal regions based upon significant overlap between the intensity extremal regions and depth extremal regions. The processing circuitry then determines X-MSER ellipses parameters based upon the strong extremal regions and X-MSER criteria.

Abstract: An architecture for linear-time extraction of maximally stable extremal regions (MSERs) having an image memory, heap memory, a pointer array and processing hardware is disclosed. The processing hardware is configured to in real-time analyze image pixels in the image memory using a linear-time algorithm to identify a plurality of components of the image. The processing hardware is also configured to place the image pixels in the heap memory for each of the plurality of components of the image, generate a pointer that points to a location in the heap memory that is associated with a start of flooding for another component and store the pointer in the array of pointers. The processing hardware is also configured to access the plurality of components using the array of pointers and determine MSER ellipses based on the components and MSER criteria.

Abstract: Hardware architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for image regions within the intensity image that match a given intensity threshold. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER area. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: Methods and systems for detecting and/or tracking one or more objects utilize depth data. An example method of detecting one or more objects in image data includes receiving depth image data corresponding to a depth image view point relative to the one or more objects. A series of binary threshold depth images are formed from the depth image data. Each of the binary threshold depth images is based on a respective depth. One or more depth extremal regions in which image pixels have the same value are identified for each of the binary depth threshold images. One or more depth maximally stable extremal regions are selected from the identified depth extremal regions based on change in area of the one or more respective depth extremal regions for different depths.

Abstract: Methods and systems process an MRI image to detect cancer. A method includes forming a series of binary threshold intensity images from an MRI image of a patient. Each of the binary threshold intensity images is based on a respective intensity. The binary threshold intensity images are processed to identify one or more bright extremal regions in which image pixels have the same value, and for which corresponding image pixels in the MRI image have a higher intensity than surrounding image pixels in the MRI image. One or more bright maximally stable extremal regions are selected from the identified bright extremal regions based on change in area of one or more respective bright extremal regions for different binary threshold images in the series. At least one of the selected one or more bright maximally stable extremal regions may be identified as potentially cancerous.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for light image regions and dark image regions within the intensity image that match a given intensity threshold during a single processing pass. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER areas. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes communication interface and processing circuitry that is adapted in hardware to receive a data streams of an intensity image and a depth image in real-time and provide intensity labels for image regions within the intensity image that match a given intensity threshold and provide depth labels for image regions within the depth image that match a given depth threshold. The processing circuitry is also adapted in hardware to find intensity extremal regions within the intensity image based upon the intensity labels and to find depth extremal regions within the depth image based upon the depth labels. The processing circuitry determines strong extremal regions based upon significant overlap between the intensity extremal regions and depth extremal regions. The processing circuitry then determines X-MSER ellipses parameters based upon the strong extremal regions and X-MSER criteria.

Abstract: Hardware architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for image regions within the intensity image that match a given intensity threshold. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER area. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for light image regions and dark image regions within the intensity image that match a given intensity threshold during a single processing pass. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER areas. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: An improved method of segmentation and feature-based description for verification/authentication of contour images (e.g. handwritten signatures) is described, directed to a system which employs a set of parameters extracted from a dynamic description of a contour image template to perform segmentation of incoming contour images being verified/authenticated by the system. The preferred embodiment would consist of a central processing unit incorporating a high-performance computer, and of many autonomous verification units (e.g. ATM machines) equipped with inexpensive processing devices. In the central processing unit, a contour image template is segmented into strokes according to the segmentation parameters which are computed using selective properties of the mathematical transformation for said contour image template. For illustration purpose, a discrete cosine transform (DCT) is being used.