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Feature Tracking
Motion estimation is the process of determining ''motion vectors'' that describe the transformation from one 2D image to another; usually from adjacent frames in a video sequence. It is an ill-posed problem as the motion is in three dimensions but the images are a projection of the 3D scene onto a 2D plane. The motion vectors may relate to the whole image (global motion estimation) or specific parts, such as rectangular blocks, arbitrary shaped patches or even per pixel. The motion vectors may be represented by a translational model or many other models that can approximate the motion of a real video camera, such as rotation and translation in all three dimensions and zoom. Related terms More often than not, the term motion estimation and the term ''optical flow'' are used interchangeably. It is also related in concept to ''image registration'' and ''stereo correspondence''. In fact all of these terms refer to the process of finding corresponding points between two images or vid ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Video Processing
In electronics engineering, video processing is a particular case of signal processing, in particular image processing, which often employs video filters and where the input and output signals are video files or video streams. Video processing techniques are used in television sets, VCRs, DVDs, video codecs, video players, video scalers and other devices. For example—commonly only design and video processing is different in TV sets of different manufactures. Video processor Video processors are often combined with video scalers to create a video processor that improves the apparent definition of video signals. They perform the following tasks: * deinterlacing * aspect ratio control * digital zoom and pan * brightness/ contrast/hue/saturation/ sharpness/gamma adjustments * frame rate conversion and inverse-telecine * color point conversion (601 to 709 or 709 to 601) * color space conversion ( YPBPR/ YCBCR to RGB or RGB to YPBPR/YCBCR) * mosquito noise reduction * block noise ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scale-invariant Feature Transform
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local ''features'' in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalised Hough transf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vision Processing Unit
A vision processing unit (VPU) is (as of 2018) an emerging class of microprocessor; it is a specific type of AI accelerator, designed to accelerate machine vision tasks. Overview Vision processing units are distinct from video processing units (which are specialised for video encoding and decoding) in their suitability for running machine vision algorithms such as CNN (convolutional neural networks), SIFT (Scale-invariant feature transform) and similar. They may include direct interfaces to take data from cameras (bypassing any off chip buffers), and have a greater emphasis on on-chip dataflow between many parallel execution units with scratchpad memory, like a manycore DSP. But, like video processing units, they may have a focus on low precision fixed point arithmetic for image processing. Contrast with GPUs They are distinct from GPUs, which contain specialised hardware for rasterization and texture mapping (for 3D graphics), and whose memory architecture is optimi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graphics Processing Unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are efficient at manipulating computer graphics and image processing. Their parallel structure makes them more efficient than general-purpose central processing units (CPUs) for algorithms that process large blocks of data in parallel. In a personal computer, a GPU can be present on a video card or embedded on the motherboard. In some CPUs, they are embedded on the CPU die. In the 1970s, the term "GPU" originally stood for ''graphics processor unit'' and described a programmable processing unit independently working from the CPU and responsible for graphics manipulation and output. Later, in 1994, Sony used the term (now standing for ''graphics processing unit'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Moving Object Detection
Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected. Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border, etc. Definition Moving object detection is to recognize the physical movement of an object in a given place or region. J. S. Kulchandani and K. J. Dangarwala, "Moving object detection: Review of recent research trends," 2015 International Conference on Pervasive Computing (ICPC), Pune, 2015, pp. 1-5. doi: 10.1109/PERVASIVE.2015.7087138. By acting [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Daniel Cremers
Daniel Cremers (born 1971) is a German computer scientist, Professor of Informatics and Mathematics and Chair of Computer Vision & Artificial Intelligence at the Technische Universität München. His research foci are computer vision, mathematical image, partial differential equations, convex and combinatorial optimization, machine learning and statistical inference. Career Cremers received a bachelor's degree in mathematics (1994) and Physics (1994), and later a master's degree in Theoretical Physics (1997) from the University of Heidelberg. He obtained a PhD in Computer Science from the University of Mannheim in 2002. He was a postdoctoral researcher at UCLA. He was associate professor at the University of Bonn from 2005 until 2009. He received a Starting Grant (2009), and a Consolidator Grant (2015) by the European Research Council. On March 1, 2016, Cremers received the Gottfried Wilhelm Leibniz Prize The Gottfried Wilhelm Leibniz Prize (german: link=no, Förderpreis fü ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Simultaneous Localization And Mapping
Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken-and-egg problem, there are several algorithms known for solving it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality. SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance. Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
HEVC
High Efficiency Video Coding (HEVC), also known as H.265 and MPEG-H Part 2, is a video compression standard designed as part of the MPEG-H project as a successor to the widely used Advanced Video Coding (AVC, H.264, or MPEG-4 Part 10). In comparison to AVC, HEVC offers from 25% to 50% better data compression at the same level of video quality, or substantially improved video quality at the same bit rate. It supports resolutions up to 8192×4320, including 8K UHD, and unlike the primarily 8-bit AVC, HEVC's higher fidelity Main 10 profile has been incorporated into nearly all supporting hardware. While AVC uses the integer discrete cosine transform (DCT) with 4×4 and 8×8 block sizes, HEVC uses integer DCT and DST transforms with varied block sizes between 4×4 and 32×32. The High Efficiency Image Format (HEIF) is based on HEVC. , HEVC is used by 43% of video developers, and is the second most widely used video coding format after AVC. Concept In most ways, HEVC is an extensio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MPEG
The Moving Picture Experts Group (MPEG) is an alliance of working groups established jointly by International Organization for Standardization, ISO and International Electrotechnical Commission, IEC that sets standards for media coding, including compression coding of audio compression (data), audio, video compression, video, graphics, and Compression of Genomic Sequencing Data, genomic data; and transmission and Container format (digital), file formats for various applications.John Watkinson, ''The MPEG Handbook'', p. 1 Together with Joint Photographic Experts Group, JPEG, MPEG is organized under ISO/IEC JTC 1/ISO/IEC JTC 1/SC 29, SC 29 – ''Coding of audio, picture, multimedia and hypermedia information'' (ISO/IEC Joint Technical Committee 1, Subcommittee 29). MPEG formats are used in various multimedia systems. The most well known older MPEG media formats typically use MPEG-1, MPEG-2, and MPEG-4 AVC media coding and MPEG-2 systems MPEG transport stream, transport streams an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Video Compression
In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder. The process of reducing the size of a data file is often referred to as data compression. In the context of data transmission, it is called source coding; encoding done at the source of the data before it is stored or transmitted. Source coding should not be confused with channel coding, for error detection and correction or line coding, the means for mapping data onto a signal. C ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
