Abstract: Systems and methods are disclosed herein to detect pixels exhibiting anomalous behavior in captured image frames. In some examples, temporal anomalous behavior may be identified, such as flickering pixels exhibiting large magnitude changes in pixel values that vary rapidly from frame-to-frame. In some examples, spatial anomalous behavior may be identified, such as pixels exhibiting values that deviate from an expected linear response in comparison with other neighbor pixels.

Abstract: Systems and methods are disclosed herein to detect pixels exhibiting anomalous behavior in captured image frames. In some examples, temporal anomalous behavior may be identified, such as flickering pixels exhibiting large magnitude changes in pixel values that vary rapidly from frame-to-frame. In some examples, spatial anomalous behavior may be identified, such as pixels exhibiting values that deviate from an expected linear response in comparison with other neighbor pixels.

Abstract: Systems and methods are disclosed herein to detect pixels exhibiting anomalous behavior in captured image frames. In some examples, temporal anomalous behavior may be identified, such as flickering pixels exhibiting large magnitude changes in pixel values that vary rapidly from frame-to-frame. In some examples, spatial anomalous behavior may be identified, such as pixels exhibiting values that deviate from an expected linear response in comparison with other neighbor pixels.

Abstract: Techniques, methods, and systems for image processing may be provided. The image processing may be provided for upsampling and interpolating images. The upsampling and interpolating may include interpolating the image through at least an edge weight and a spatial weight. In various embodiments, the edge weight and/or the spatial weight may be calculated with a kernel. The kernel may be a kernel with a two dimensional (2D) distribution such as a Gaussian kernel, a Laplacian kernel, or another such statistically based kernel. The image processing may also include refining the upsampled and interpolated image through a refinement weight calculation and/or through back projection.

Abstract: Systems and methods are disclosed herein to detect pixels exhibiting anomalous behavior in captured image frames. In some examples, temporal anomalous behavior may be identified, such as flickering pixels exhibiting large magnitude changes in pixel values that vary rapidly from frame-to-frame. In some examples, spatial anomalous behavior may be identified, such as pixels exhibiting values that deviate from an expected linear response in comparison with other neighbor pixels.

Abstract: Various techniques are disclosed for a system and method to dynamically co-register images captured by two or more separate imaging modules. For example, in one embodiment, a method includes: capturing a thermal image of an object; capturing a visible light (VL) image of the object; determining a plurality of thermal image reference points from the thermal image; determining a plurality of VL image reference points from the VL image; determining a geometric transform based on the plurality of thermal image reference points and the plurality of VL image reference points; and applying the geometric transform to the thermal image or the VL image to align the thermal image and the VL image. In another embodiment, an imaging system comprises two or more separate imaging modules and one or more processors configured to perform the method above to dynamically co-register images.

Abstract: Techniques, methods, and systems for image processing may be provided. The image processing may be provided for upsampling and interpolating images. The upsampling and interpolating may include interpolating the image through at least an edge weight and a spatial weight. In various embodiments, the edge weight and/or the spatial weight may be calculated with a kernel. The kernel may be a kernel with a two dimensional (2D) distribution such as a Gaussian kernel, a Laplacian kernel, or another such statistically based kernel. The image processing may also include refining the upsampled and interpolated image through a refinement weight calculation and/or through back projection.

Abstract: Various techniques are disclosed for a system and method to dynamically co-register images captured by two or more separate imaging modules. For example, in one embodiment, a method includes: capturing a thermal image of an object; capturing a visible light (VL) image of the object; determining a plurality of thermal image reference points from the thermal image; determining a plurality of VL image reference points from the VL image; determining a geometric transform based on the plurality of thermal image reference points and the plurality of VL image reference points; and applying the geometric transform to the thermal image or the VL image to align the thermal image and the VL image. In another embodiment, an imaging system comprises two or more separate imaging modules and one or more processors configured to perform the method above to dynamically co-register images.

Abstract: A system for and method of locating desired files in distributed file sharing over a computer network is disclosed, whereby each file 6 is categorized according to the content of the file 6 and information about files 6 resides on at least one central index server 3. Client terminals 1 publish files 6 for other client terminals 1 in the computer network 2. The central index servers 3 store the list of files 6 in the system. Client terminals 1 query the central index servers 3 to determine the set of files 6 currently available in the system. A central index server 3 returns a list of files 6 and file locations which match the query results and are currently available. The client terminal 1 issuing the search can retrieve the file 6 from the search results by requesting it from one of the client terminals 1 specified in the search results.