David Meir Blei is a professor in the Statistics and Computer Science departments at Columbia University. Prior to fall 2014 he was an associate professor in the Department of Computer Science at Princeton University. His work is primarily in machine learning. == Research == His research interests include topic models and he was one of the original developers of latent Dirichlet allocation, along with Andrew Ng and Michael I. Jordan. As of June 18, 2020, his publications have been cited 109,821 times, giving him an h-index of 116. == Honors and awards == Blei received the ACM Infosys Foundation Award in 2013. (This award is given to a computer scientist under the age of 45. It has since been renamed the ACM Prize in Computing.) He was named Fellow of ACM "For contributions to the theory and practice of probabilistic topic modeling and Bayesian machine learning" in 2015.
Graph cuts in computer vision and artificial intelligence
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, numerous military applications (eg Automatic target recognition) and many other problems that can be formulated in terms of energy minimization (eg Climate Science and Environmental modelling). Graph cut techniques are now increasingly being used in combination with more general spatial Artificial intelligence techniques (eg to enforce structure in Large language model output to sharpen tumour boundaries and similarly for various Augmented reality, Self-driving car, Robotics, Google Maps applications etc). Many of these energy minimization problems can be approximated by solving a maximum flow problem in a graph (and thus, by the max-flow min-cut theorem, define a minimal cut of the graph). Under most formulations of such problems in computer vision, the minimum energy solution corresponds to the maximum a posteriori estimate of a solution. Although many computer vision algorithms involve cutting a graph (e.g. normalized cuts), the term "graph cuts" is applied specifically to those models which employ a max-flow/min-cut optimization (other graph cutting algorithms may be considered as graph partitioning algorithms). "Binary" problems (such as denoising a binary image) can be solved exactly using this approach; problems where pixels can be labeled with more than two different labels (such as stereo correspondence, or denoising of a grayscale image) cannot be solved exactly, but solutions produced are usually near the global optimum. == History == The foundational theory of graph cuts in computer vision was first developed by Margaret Greig, Bruce Porteous and Allan Seheult (GPS) of Durham University in a now legendary discussion contribution to Julian Besag's 1986 paper and a more detailed follow on paper in 1989. In the Bayesian statistical context of smoothing noisy images, using a Markov random field as the image prior distribution, they showed with a mathematically beautiful proof how the maximum a posteriori estimate of a binary image can be obtained exactly by maximizing the flow through an associated image network, or graph, involving the introduction of a source and sink and Log-likelihood ratios. The problem was shown to be efficiently solvable exactly, an unexpected result as the problem was believed to be computationally intractable (NP hard). GPS also addressed the computational cost of the max-flow algorithm on large graphs, a significant concern at the time. They proposed a partitioning algorithm (see Section 4 of GPS) involving the recursive amalgamation of non-overlapping blocks, or tiles, which gave a 12X increase in speed. This approach recursively solved and amalgamated independent sub-graphs until the whole graph was solved. While contemporaries like Geman and Geman had advocated Parallel computing in the context of Simulated annealing, the GPS blocking strategy offered a deterministic structure amenable to parallelisation and anticipated modern artificial intelligence design across multiple GPUs. However, until recently, this aspect of the paper was largely ignored and subsequent research focused on Serial computer global search trees, such as the Boykov-Kolmogorov algorithm. Although the general k {\displaystyle k} -colour problem is NP hard for k > 2 , {\displaystyle k>2,} the GPS approach has turned out to have very wide applicability in general computer vision problems. This was first demonstrated by Boykov, Veksler and Zabih who, in a seminal paper published more than 10 years after the original GPS paper, and in other important works, lit the blue touch paper for the general adoption of graph cut techniques in computer vision. They showed that, for general problems, the GPS approach can be applied iteratively to sequences of binary problems, using their now ubiquitous alpha-expansion algorithm, yielding near optimal solutions. Prior to these results, approximate local optimisation techniques such as simulated annealing (as proposed by the Geman brothers) or iterated conditional modes (a type of greedy algorithm suggested by Julian Besag) were used to solve such image smoothing problems. Building on these advancements, GPS graph cut optimization was subsequently adapted for interactive image segmentation, most notably through the "GrabCut" algorithm introduced by Carsten Rother, Vladimir Kolmogorov, and Andrew Blake of Microsoft Research, Cambridge. GrabCut extended earlier interactive graph cut methods by replacing monochrome image histograms with Gaussian mixture models to estimate colour distributions, and by employing an iterative GPS energy minimisation scheme. This approach significantly simplified user interaction, requiring only a rough bounding box around the target object rather than detailed user-drawn strokes, and it quickly became a standard tool in both academic research and commercial image editing software. The GPS paper connected and bridged profound ideas from Mathematical statistics (Bayes' theorem, Markov random field), Physics (Ising model), Optimisation (Energy function) and Computer science (Network flow problem) and led the move away from approximate local and slow optimisation approaches (eg simulated annealing) to more powerful exact, or near exact, faster global optimisation techniques. It is now recognised as seminal as it was well ahead of its time and, in particular, was published years before the computing power revolution of Moore's law and GPUs. Significantly, GPS was published in a mathematical statistics (rather than a computer vision) journal, and this led to it being overlooked by the computer vision community for many years. It is unofficially known as "The Velvet Underground" paper of computer vision (ie although very few computer vision people read the paper [bought the record], those that did, most importantly Boykov, Veksler and Zabih, started new and important research [formed a band]). This is confirmed by GPS' very large amplification ratio (2nd order citations/first order citations), estimated at well in excess of 100. Despite the foundational nature of the GPS work, formal recognition from the computer vision community has predominantly gone to the researchers who followed to extend and popularise the graph cut method. For example, Boykov, Veksler and Zabih deservedly received a Helmholtz Prize from the ICCV in 2011. This prize recognises ICCV papers from 10 or more years earlier that have had a significant impact on computer vision research. In 2011, Couprie et al. proposed a general image segmentation framework, called the "Power Watershed", that minimized a real-valued indicator function from [0,1] over a graph, constrained by user seeds (or unary terms) set to 0 or 1, in which the minimization of the indicator function over the graph is optimized with respect to an exponent p {\displaystyle p} . When p = 1 {\displaystyle p=1} , the Power Watershed is optimized by graph cuts, when p = 0 {\displaystyle p=0} the Power Watershed is optimized by shortest paths, p = 2 {\displaystyle p=2} is optimized by the random walker algorithm and p = ∞ {\displaystyle p=\infty } is optimized by the watershed algorithm. In this way, the Power Watershed may be viewed as a generalization of graph cuts that provides a straightforward connection with other energy optimization segmentation/clustering algorithms. == Binary segmentation of images == === Notation === Image: x ∈ { R , G , B } N {\displaystyle x\in \{R,G,B\}^{N}} Output: Segmentation (also called opacity) S ∈ R N {\displaystyle S\in R^{N}} (soft segmentation). For hard segmentation S ∈ { 0 for background , 1 for foreground/object to be detected } N {\displaystyle S\in \{0{\text{ for background}},1{\text{ for foreground/object to be detected}}\}^{N}} Energy function: E ( x , S , C , λ ) {\displaystyle E(x,S,C,\lambda )} where C is the color parameter and λ is the coherence parameter. E ( x , S , C , λ ) = E c o l o r + E c o h e r e n c e {\displaystyle E(x,S,C,\lambda )=E_{\rm {color}}+E_{\rm {coherence}}} Optimization: The segmentation can be estimated as a global minimum over S: arg min S E ( x , S , C , λ ) {\displaystyle {\arg \min }_{S}E(x,S,C,\lambda )} === Existing methods === Standard Graph cuts: optimize energy function over the segmentation (unknown S value). Iterated Graph cuts: First step optimizes over the color parameters using K-means. Second step performs the usual graph cuts algorithm. These 2 steps are repeated recursively until convergence Dynamic graph cuts:Allows to re-run the algorithm much faster after modifying the problem (e.g. after new seeds have been added by a user). === Energy function === Pr ( x ∣ S ) = K − E {\displaystyle \Pr(x\mid S)=K^{-E}} where the energy E {\displaystyle E} is composed of two different mod
Visopsys
Visopsys (Visual Operating System), is an operating system, written by Andy McLaughlin. Development of the operating system began in 1997. The operating system is licensed under the GNU GPL, with the headers and libraries under the less restrictive LGPL license. It runs on the 32-bit IA-32 architecture. It features a multitasking kernel, supports asynchronous I/O and the FAT line of file systems. It requires a Pentium processor. == History == The development of Visopsys began in 1997, being written by Andy McLaughlin. The first public release of the Operating System was on 2 March 2001, with version 0.1. In this release, Visopsys was a 32 bit operating system, supporting preemptive multitasking and virtual memory. == System overview == Visopsys uses a monolithic kernel, written in the C programming language, with elements of assembly language for certain interactions with the hardware. The operating system supports a graphical user interface, with a small C library.
Full30
Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac
Vinyl cutter
A vinyl cutter is an entry-level machine for making signs. Computer-designed vector files with patterns and letters are directly cut on the roll of vinyl which is mounted and fed into the vinyl cutter through USB or serial cable. Vinyl cutters are mainly used to make signs, banners and advertisements. Advertisements seen on automobiles and vans are often made with vinyl cut letters. While these machines were designed for cutting vinyl, they can also cut through computer and specialty papers, as well as thicker items like thin sheets of magnet. In addition to sign business, vinyl cutters are commonly used for apparel decoration. To decorate apparel, a vector design needs to be cut in mirror image, weeded, and then heat applied using a commercial heat press or a hand iron for home use. Some businesses use their vinyl cutter to produce both signs and custom apparel. Many crafters also have vinyl cutters for home use. These require little maintenance, and the vinyl can be bought in bulk relatively cheaply. Vinyl cutters are also often used by stencil artists to create single use or reusable stencil art and lettering == How it works == A vinyl cutter is a type of computer-controlled machine tool. The computer controls the movement of a sharp blade over the surface of the material as it would the nozzles of an ink-jet printer. This blade is used to cut out shapes and letters from sheets of thin self-adhesive plastic (vinyl). The vinyl can then be stuck to a variety of surfaces depending on the adhesive and type of material. To cut out a design, a vector-based image must be created using vector drawing software. Some vinyl cutters are marketed to small in-home businesses and require download and use of a proprietary editing software. The design is then sent to the cutter where it cuts along the vector paths laid out in the design. The cutter is capable of moving the blade on an X and Y axis over the material, cutting it into the required shapes. The vinyl material comes in long rolls allowing projects with significant length like banners or billboards to be easily cut. A major limitation with vinyl cutters is that they can only cut shapes from solid colours of vinyl, paper, card or thin plastic sheets such as Mylar. The type and thickness of material will vary for each cutter and how much downforce the cutter is capable of. If the material has no backing, a backing sheet, material or cutting mat and a temporary adhesive are needed to allow the cutter to cut through the material. A design with multiple colours must have each colour cut separately and then layered on top of each other as it is applied to the substrate. This is a process that is often applied in stencil art. Also, since the shapes are cut out of solid colours, photographs and gradients cannot be reproduced with a stand-alone cutter. === Design creation === Designs are created using vector-based software like Adobe Illustrator, FlexiSign, EasyCutPro, or other software. Vector artwork is either drawn with lines, shapes and text or images are vectorized thus create vector shapes. Most cutters (also called plotters) require special software to load/edit the artwork and communicate with the cutter. Computer designed images are loaded onto the vinyl cutter via a wired connection or over a wireless protocol. Then the vinyl is loaded into the machine where it is automatically fed through and cut to follow the set design. The vinyl can be placed on an adhesive mat to stabilize the vinyl when cutting smaller designs. === Types of vinyl === Adhesive vinyl is the type of vinyl used for store windows, car decals, signage, and more. Adhesive vinyl is applied with a transfer medium often called "transfer tape" or "carrier sheet". Heat transfer vinyl is the type of vinyl used to apply a design to fabric including t-shirts, tea towels, canvas bags, and more. Heat Transfer vinyl can be applied using a heat press or an iron, though the constant pressure and heat from a heat press is recommended by experts. === Using other materials === In addition to vinyl some cutters are capable of cutting other materials such as paper, card, plastic sheets and even thin wood. The thickness and type of material that can be cut will depend on the model of the cutter and heavily depends on the downforce. Cricut is a popular home cutter used by arts and craft enthusiasts since it allows for a wide use of different materials and is similar in size to a household printer and has strong downforce for its size. === Backing and cutting mat === If you cut material that doesn't have an adhesive backing you will require a cutting mat that you need to attach your material to. Some cutting mats are sticky, others will require you to use a temporary adhesive and/or masking tape to keep the material in place when cutting. === Cutting === The vinyl cutter uses a small knife or blade to precisely cut the outline of figures into a sheet or piece of vinyl, but not the release liner. The process of cutting vinyl material without penetrating it completely is referred to as "kiss cutting". The knife moves side to side and turns, while the vinyl is moved beneath the knife. The results from the cut process is an image cut into the material. === Weeding === The material is then 'weeded' where the excess parts of the figures are removed from the release liner. It is possible to remove the positive parts, which would give a negative decal, or remove the negative parts, giving a positive decal. Removing the figure would be like removing the positive, giving a negative image of the figures. === Transfer tape === A sheet of transfer tape with an adhesive backing is laid on the weeded vinyl when necessary. Heat Transfer vinyl often does not require use of a separate transfer tape. A roller is applied to the tape, causing it to adhere to the vinyl. The transfer tape and the weeded vinyl is pulled off the release liner, and applied to a substrate, such as a sheet of aluminium. This results in an aluminium sign with vinyl figures. == Uses == In addition to the capabilities of the cutter itself, adhesive vinyl comes in a wide variety of colors and materials including gold and silver foil, vinyl that simulates frosted glass, holographic vinyl, reflective vinyl, thermal transfer material, and even clear vinyl embedded with gold leaf. (Often used in the lettering on fire trucks and rescue vehicles.) As the vinyl film is supplied by the manufacturer, it comes attached to a release liner. == Challenges when cutting on a vinyl cutter == Cutting on a vinyl cutter requires careful calibration to achieve clean and accurate results, especially when the goal is to cut through only the top layer of material while leaving the backing intact. One of the most common challenges is setting the correct cutting depth. If the blade is not lowered enough, the vinyl material may not separate properly; if it goes too deep, it can cut through the backing layer and potentially damage the cutting mat. The cutting depth on the vinyl cutter machines typically does not exceed 1 mm. Another frequent issue is the mismatch between the blade and the type of material being processed. Using an inappropriate blade can lead to uneven cuts, premature dulling of the edge, and torn or frayed material. The overall quality of the output also depends on factors such as the cutting speed, blade sharpening and cutting angle, and the material the knife is made of.
N-jet
An N-jet is the set of (partial) derivatives of a function f ( x ) {\displaystyle f(x)} up to order N. Specifically, in the area of computer vision, the N-jet is usually computed from a scale space representation L {\displaystyle L} of the input image f ( x , y ) {\displaystyle f(x,y)} , and the partial derivatives of L {\displaystyle L} are used as a basis for expressing various types of visual modules. For example, algorithms for tasks such as feature detection, feature classification, stereo matching, tracking and object recognition can be expressed in terms of N-jets computed at one or several scales in scale space.
Motion picture film scanner
A motion picture film scanner is a device used in digital filmmaking to scan original film for storage as high-resolution digital intermediate files. A film scanner scans original film stock: negative or positive print or reversal/IP. Units may scan gauges from 8 mm to 70 mm (8 mm, Super 8, 9.5 mm, 16 mm, Super 16, 35 mm, Super 35, 65 mm and 70 mm) with very high resolution scanning at 2K, 4K, 8K, or 16K resolutions. (2K is approximately 2048×1080 pixels and 4K is approximately 4096×2160 pixels). Some models of film scanner are intermittent pull-down film scanners which scan each frame individually, locked down in a pin-registered film gate, taking roughly a second per frame. Continuous-scan film scanners, where the film frames are scanned as the film is continuously moved past the imaging pick up device, are typically evolved from earlier telecine mechanisms, and can act as such at lower resolutions. The scanner scans the film frames into a file sequence (using high-end computer data storage devices), whose single file contains a digital scan of each still frame; the preferred image file format used as output are usually Cineon, DPX or TIFF, because they can store color information as raw data, preserving the optical characteristics of the film stock. These systems take a lot of storage area network (SAN) disk space. The files can be played back one after each other on high-end workstation non-linear editing system (NLE) or a virtual telecine systems. The playback is at the normal rate of 24 frames per second (or original projection frame rate of: 25, 30 or other speeds). Each year hard disks get larger and are able to hold more hours of movies on SAN systems. The challenge is to archive this massive amount of data on to data storage devices. The scanned footage is edited and composited on work stations then mastered back on film, see film-out and digital intermediate. Scanned film frames may also be used in digital film restoration. The film may also be projected directly on a digital projector in the theater. The data film files may be converted to SDTV (NTSC or PAL) video TV systems. Film recorders are the opposite of film scanners, copying content from a computer system onto film stock, for preservation or for display using film projectors. Telecines are similar to film scanners. == Imaging device == The front end of a motion picture film scanner is similar to a telecine. The imaging system may be either a charge-coupled device (CCD), a complementary metal–oxide–semiconductor (CMOS) or photomultipliers imaging pick up. A lamp is used as the light source in a CCD imaging front end. The CCDs convert the light to the video signals. In a cathode-ray tube (CRT) imaging system the CRT (also called a Flying spot tube) is used as the light source and part of the scanning system. Photomultipliers or avalanche photodiodes are used to convert the light to electrical video signals. A prism and/or dichroic mirrors or color filters are used to separate the light into the three: red, green and blue, imaging pick up devices. == Image processing == The three color signals (RGB) are electronically processed and color graded. A 3D look up table (3D LUT) is usually applied to the RGB values before it is coded into the DPX output files. The DPX files are usually made output through a network port cable or an optical fiber port: HIPPI, Fibre Channel or newer systems like gigabit Ethernet. A computer then stores the files on to hard drives of a storage area network for later processing and use. Modern motion picture film scanners many have an option for an infrared CCD channel for dirt mapping, that can be used to automatically or in post manually remove dirt-dust spots on the film. The IR camera channel can be used with IR dirt and scratch removal system or made output on a four IR channel for downstream dirt and dirt and scratch removal systems. Popular downstream dirt and dirt and scratch removal systems are PF Clean and Digital ICE. HDR or high dynamic range is a new system, using a compositing and tone-mapping of images to extend the dynamic range beyond the native capability of the capturing device. This may be done by using a triple exposure for the film and then compositing the three back together. Compositing can be done in a workstation in none real time or in the scanner in real time. == Models == Bold indicates a currently produced model Single frame intermittent pull-down: ARRI - Arriscan Cintel - diTTo Filmlight - Northlight 1 (up to 6K, 16mm to VistaVision), Northlight 2 (up to 8K, 16mm to VistaVision) Imagica scanner, single frame intermittent scanner. Kodak - Cineon, the first system designed for DI work, included a scanner, tapes drives, workstations and a film recorder. Lasergraphics Director 13.5K, 8mm to 70mm, IMAX & VistaVision) Continuous motion scanning: Arri - ARRISCAN XT (up to 6K, S35 down to 16mm) Cintel's C-Reality/DSX and ITK - Millennium/dataMill. Under ownership of Blackmagic Design, the Cintel Scanner was released, with the current 3rd generation capable of up to 4K scans at 30 fps. DFT - Spirit Classic (up to 2K), Spirit 4k/2k/HD (up to 4K), POLAR HQ (up to 8K, 8mm to S35), OXScan 14K (up to 14K, 16mm to 70mm), Scanity HDR (up to 4K, 16mm to S35) Filmfabriek - HDS+ (up to 4k), Pictor Pro (up to 2.7K), Pictor (up to 1080p). Filmfabriek scanners can only scan 17.5mm or smaller film formats. GE4 - Golden Eye Four - Filmscanner, 38 Mega Pixel camera. LED light source and continuous film transport using Capstan. From Digital Vision. Lasergraphics ScanStation (6.5K, 8mm to 70mm, IMAX & VistaVision) Lasergraphics Archivist (up to 5K) MWA Nova Vario series with patented laser-based, sprocket and claw free transport for 16/35mm for realtime (24/25fps) scanning with sensors for either 2K+ 2236 x 1752, or 2.5K+ HDR High Dynamic Range at 2560 x 2160, direct optical and magnetic sound on and 16 and 35mm. MWA Nova Choice 2K+ patented laser-based, sprocket and claw free transport for 8/Super8, 9.5mm, 16mm realtime (24/25fps) scanning w at 2K+, 2236 x 1752 with direct optical and magnetic sound on 16mm, magnetic from main and balance stripes on 8, Super8. Faster than real time scanning at lower resolution. P+S Technik - SteadyFrame Universal Format Film Scanner Walde - FilmStar 4K UHD 2K @ 25fps, 4K UHD @ 6fps. 35mm/16mm/8mm archive quality, continuous motion capstan driven.