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Image Stitching
Image stitching or photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image. Commonly performed through the use of computer software, most approaches to image stitching require nearly exact overlaps between images and identical exposures to produce seamless results, although some stitching algorithms actually benefit from differently exposed images by doing high-dynamic-range imaging in regions of overlap. Some digital cameras can stitch their photos internally. Applications Image stitching is widely used in modern applications, such as the following: * Document mosaicing * Image stabilization feature in camcorders that use frame-rate image alignment *High-resolution image mosaics in digital maps and satellite imagery * Medical imaging *Multiple-image super-resolution imaging *Video stitching *Object insertion Process The image stitching process can be divided into thre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rochester NY
Rochester is a city in and the seat of government of Monroe County, New York, United States. It is the fourth-most populous city and 10th most-populated municipality in New York, with a population of 211,328 at the 2020 census. The city forms the core of the larger Rochester metropolitan area in Western New York, with a population of just over 1 million residents. Throughout its history, Rochester has acquired several nicknames based on local industries; it has been known as " the Flour City" and " the Flower City" for its dual role in flour production and floriculture, and as the "World's Image Center" for its association with film, optics, and photography. The city was one of the United States' first boomtowns, initially due to the fertile Genesee River valley which gave rise to numerous flour mills, and then as a manufacturing center, which spurred further rapid population growth. Rochester has also played a key part in US history as a hub for social and political movemen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speeded Up Robust Features
In computer vision, speeded up robust features (SURF) is a local feature detector and descriptor, with patented applications. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. To detect interest points, SURF uses an integer approximation of the determinant of Hessian blob detector, which can be computed with 3 integer operations using a precomputed integral image. Its feature descriptor is based on the sum of the Haar wavelet response around the point of interest. These can also be computed with the aid of the integral image. SURF descriptors have been used to locate and recognize objects, people or faces, to reconstruct 3D scenes, to track objects and to extract points of int ... [...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 t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hans Moravec
Hans Peter Moravec (born November 30, 1948, Kautzen, Austria) is a computer scientist and an adjunct faculty member at the Robotics Institute of Carnegie Mellon University in Pittsburgh, USA. He is known for his work on robotics, artificial intelligence, and writings on the impact of technology. Moravec also is a futurist with many of his publications and predictions focusing on transhumanism. Moravec developed techniques in computer vision for determining the region of interest (ROI) in a scene. Career Moravec attended Loyola College in Montreal for two years and transferred to Acadia University, where he received his BSc in mathematics in 1969. He received his MSc in computer science in 1971 from the University of Western Ontario. He then earned a PhD in computer science from Stanford University in 1980 for a TV-equipped robot which was remotely controlled by a large computer (the Stanford Cart). The robot was able to negotiate cluttered obstacle courses. Another achie ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Difference Of Gaussians
In imaging science, difference of Gaussians (DoG) is a feature enhancement algorithm that involves the subtraction of one Gaussian blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing width (standard deviations). Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images. Thus, the DoG is a spatial band-pass filter that attenuates frequencies in the original grayscale image that are far from the band center. [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Harris Corner
Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. Formalization A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point. An interest point is a point in an image which has a well-defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so-called corner detection methods detect inte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Detection (computer Vision)
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions. More broadly a ''feature'' is any piece of information that is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compositing
Compositing is the process or technique of combining visual elements from separate sources into single images, often to create the illusion that all those elements are parts of the same scene. Live action, Live-action shooting for compositing is variously called "chroma key", "blue screen", "green screen" and other names. Today, most compositing is achieved through digital image manipulation. Pre-digital compositing techniques, however, go back as far as the trick films of Georges Méliès in the late 19th century, and some are still in use. Basic procedure All compositing involves the replacement of selected parts of an image with other material, usually, but not always, from another image. In the digital method of compositing, software commands designate a narrowly defined color as the part of an image to be replaced. Then the software (e.g. Natron (software), Natron) replaces every pixel within the designated color range with a pixel from another image, aligned to appear ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aspect Ratio (image)
The aspect ratio of an image is the ratio of its width to its height. It is expressed as two numbers separated by a colon, in the format width:height. Common aspect ratios are 1.85:1 and 2.39:1 in cinematography, 4:3 and 16:9 in television, and 3:2 in still photography and 1:1: Used for square images, often seen on social media platforms like Instagram, 21:9: An ultrawide aspect ratio popular for gaming and desktop monitors. Some common examples The common film aspect ratios used in cinemas are 1.85:1 and 2.39:1.The 2.39:1 ratio is commonly labeled 2.40:1, e.g., in the American Society of Cinematographers' ''American Cinematographer Manual'' (Many widescreen films before the 1970 Society of Motion Picture and Television Engineers, SMPTE revision used 2.35:1). Two common videography, videographic aspect ratios are 4:3 (1.:1), the universal video format of the 20th century, and 16:9 (1.:1), universal for high-definition television and European digital television. Other cinematic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Exposure (photography)
In photography, exposure is the amount of light per unit area reaching a frame (photography), frame of photographic film or the surface of an electronic image sensor. It is determined by shutter speed, lens f-number, and scene luminance. Exposure is measured in unit of measurement, units of lux-seconds (symbol lxs), and can be computed from exposure value (EV) and scene luminance in a specified region. An "exposure" is a single shutter cycle. For example, a long-exposure photography, long exposure refers to a single, long shutter cycle to gather enough dim light, whereas a multiple exposure involves a series of shutter cycles, effectively layering a series of photographs in one image. The accumulated ''photometric exposure'' (''H''v) is the same so long as the total exposure time is the same. Definitions Radiant exposure Radiant exposure of a ''surface'', denoted ''H''e ("e" for "energetic", to avoid confusion with Photometry (optics), photometric quantities) and measured in , i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Motion Blur
Motion blur is the apparent streaking of moving objects in a photograph or a sequence of frames, such as a film or animation. It results when the image being recorded changes during the recording of a single exposure, due to rapid movement or long-exposure photography, long exposure. Usages / Effects of motion blur Photography When a camera creates an image, that image does not represent a single instant of time. Because of technological constraints or artistic requirements, the image may represent the scene over a period of time. Most often this exposure time is brief enough that the image captured by the camera appears to capture an instantaneous moment, but this is not always so, and a fast moving object or a longer exposure time may result in blurring artifacts which make this apparent. As objects in a scene move, an image of that scene must represent an Integral, integration of all positions of those objects, as well as the camera's viewpoint, over the period of exposur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
