Abstract: Many conventional video processing algorithms attempting to detect human presence in a video stream often generate false positives on non-human movements such as plants moving in the wind, rotating fan, etc. To reduce false positives, a technique exploiting temporal correlation of non-human movements can accurately detect human occupancy while reject non-human movements. Specifically, the technique involves performing temporal analysis on a time-series signal generated based on an accumulation of foreground maps and an accumulation of motion map and analyzing the running mean and the running variance of the time-series signal. By determining whether the time-series signal is correlated in time, the technique is able to distinguish human movements and non-human movements. Besides having superior accuracy, the technique lends itself to an efficient algorithm which can be implemented on low cost, low power digital signal processor or other suitable hardware.

Abstract: One factor in limiting the speed of conventional implementations of mixture models is that the algorithm involves many decisions where different operations are fetched and performed depending on the outcome of the decisions. These decisions cause flushing of the pipeline, and thus prevent the realization of a highly parallel pipeline in a processor. Without parallelism, the throughput of the pipeline in the processor, i.e., the ability to process many samples of the digital input at a time, is limited. To alleviate this issue, implementation of the mixture model is reformulated, among other things, by embedding decisions into the process flow as multiplicative factors. The resulting implementation alleviates the need to use if-else statements for the decisions and reduces the number of times the pipeline has to be flushed. The implementation enables a pipeline with a higher degree of parallelism and thereby increases throughput and speed of the implementation.

Abstract: Signal flows for data-processing applications may be implemented so as to enable each processing node in the flow when it contains a sufficient amount of input data at its input buffer. In various embodiments, such signal flows can be graphically defined in a GUI tool which, thereafter, auto-generates suitable code for implementing the signal flow.

Abstract: A system and method for removing noise from images are disclosed herein. An exemplary system includes an edge-detection-based adaptive filter that identifies edge pixels and non-edge pixels in an image and selects a filtering technique for at least one non-edge pixel based on a comparison of the at least one non-edge pixel to a neighboring pixel region, wherein such comparison indicates whether the at least one non-edge pixel is a result of low-light noise.

Abstract: Signal flows for data-processing applications may be implemented so as to enable each processing node in the flow when it contains a sufficient amount of input data at its input buffer. In various embodiments, such signal flows can be graphically defined in a GUI tool which, thereafter, auto-generates suitable code for implementing the signal flow.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: A method and apparatus utilizing a prediction guided decimated search motion estimation algorithm are provided. The prediction guided decimated search motion estimation algorithm generates a motion vector used to encode a macroblock in a frame from a video sequence. The algorithm includes generating full-pixel seed vectors, performing a full-pixel search around the generated seed vectors, which is followed by a fractional pixel search. The full-pixel seed vectors generated are a predicted motion vector and a hierarchical motion vector. A fractional pixel search may be conducted around a final motion vector generated by the full-pixel search and may include a half-pixel search and a quarter-pixel search. The prediction guided decimated search motion estimation algorithm can be implemented in both software and hardware. The algorithm is characterized by improved efficiency, scalability, and decreased complexity.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: In general, in one embodiment, low-light noise is removed from an image by separately filtering luma and chroma components of the image, by adaptively filtering the image based at least in part on a Gaussian distribution of the image, and/or by dividing the image into separate regions and filtering each region separately.

Abstract: A method and apparatus utilizing a prediction guided decimated search motion estimation algorithm are provided. The prediction guided decimated search motion estimation algorithm generates a motion vector used to encode a macroblock in a frame from a video sequence. The algorithm includes generating full-pixel seed vectors, performing a full-pixel search around the generated seed vectors, which is followed by a fractional pixel search. The full-pixel seed vectors generated are a predicted motion vector and a hierarchical motion vector. A fractional pixel search may be conducted around a final motion vector generated by the full-pixel search and may include a half-pixel search and a quarter-pixel search. The prediction guided decimated search motion estimation algorithm can be implemented in both software and hardware. The algorithm is characterized by improved efficiency, scalability, and decreased complexity.