Maya codicesMaya codices (singular codex) are folding books written by the pre-Columbian Maya civilization in Maya hieroglyphic script on Mesoamerican bark paper. The folding books are the products of professional scribes working under the patronage of deities such as the Tonsured Maize God and the Howler Monkey Gods. Most of the codices were destroyed by conquistadors and Catholic priests in the 16th century. The codices have been named for the cities where they eventually settled.
Maya civilizationThe Maya civilization (ˈmaɪə) was a Mesoamerican civilization that existed from antiquity to the early modern period. It is known by its ancient temples and glyphs (script). The Maya script is the most sophisticated and highly developed writing system in the pre-Columbian Americas. The civilization is also noted for its art, architecture, mathematics, calendar, and astronomical system. The Maya civilization developed in the Maya Region, an area that today comprises southeastern Mexico, all of Guatemala and Belize, and the western portions of Honduras and El Salvador.
Maya scriptMaya script, also known as Maya glyphs, is historically the native writing system of the Maya civilization of Mesoamerica and is the only Mesoamerican writing system that has been substantially deciphered. The earliest inscriptions found which are identifiably Maya date to the 3rd century BCE in San Bartolo, Guatemala. Maya writing was in continuous use throughout Mesoamerica until the Spanish conquest of the Maya in the 16th and 17th centuries.
Maya religionThe traditional Maya or Mayan religion of the extant Maya peoples of Guatemala, Belize, western Honduras, and the Tabasco, Chiapas, Quintana Roo, Campeche and Yucatán states of Mexico is part of the wider frame of Mesoamerican religion. As is the case with many other contemporary Mesoamerican religions, it results from centuries of symbiosis with Roman Catholicism. When its pre-Hispanic antecedents are taken into account, however, traditional Maya religion has already existed for more than two and a half millennia as a recognizably distinct phenomenon.
Mesoamerican writing systemsMesoamerica, along with Mesopotamia and China, is one of three known places in the world where writing is thought to have developed independently. Mesoamerican scripts deciphered to date are a combination of logographic and syllabic systems. They are often called hieroglyphs due to the iconic shapes of many of the glyphs, a pattern superficially similar to Egyptian hieroglyphs. Fifteen distinct writing systems have been identified in pre-Columbian Mesoamerica, many from a single inscription.
Classic Maya languageClassic Maya (or properly Classical Ch'olti') is the oldest historically attested member of the Maya linguistic family. It is the main language documented in the pre-Columbian inscriptions of the classical period of the Maya civilization. It is also a direct descendant of Proto-Mayan (as are Wastek and Yucatec) and the common ancestor of the Cholan branch of Mayan languages. Contemporary descendants of classical Maya include Ch'ol and Ch'orti'. Speakers of these languages can understand many Classic Mayan words.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Image segmentationIn and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
Complexity classIn computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.
CodexThe codex (: codices 'koʊdɪsiːz) was the historical ancestor of the modern book. Instead of being composed of sheets of paper, it used sheets of vellum, papyrus, or other materials. The term codex is often used for ancient manuscript books, with handwritten contents. A codex, much like the modern book, is bound by stacking the pages and securing one set of edges by a variety of methods over the centuries, yet in a form analogous to modern bookbinding. Modern books are divided into paperback (or softback) and those bound with stiff boards, called hardbacks.
Activity recognitionActivity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.
Parameterized complexityIn computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output. The complexity of a problem is then measured as a function of those parameters. This allows the classification of NP-hard problems on a finer scale than in the classical setting, where the complexity of a problem is only measured as a function of the number of bits in the input.
Computational complexity theoryIn theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.
Object detectionObject detection is a computer technology related to computer vision and that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including and video surveillance. It is widely used in computer vision tasks such as , vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation.
Writing systemA writing system is a method of visually representing verbal communication, based on a script and a set of rules regulating its use. While both writing and speech are useful in conveying messages, writing differs in also being a reliable form of information storage and transfer. Writing systems require shared understanding between writers and readers of the meaning behind the sets of characters that make up a script.
Optical character recognitionOptical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).
Residual neural networkA Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.
Feature (computer vision)In computer vision and , 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.
EpsilonEpsilon (ˈɛpsᵻlɒn, UKalsoɛpˈsaɪlən; uppercase Ε, lowercase ε or lunate ε; έψιλον) is the fifth letter of the Greek alphabet, corresponding phonetically to a mid front unrounded vowel e̞ or ɛ̝. In the system of Greek numerals it also has the value five. It was derived from the Phoenician letter He . Letters that arose from epsilon include the Roman E, Ë and Ɛ, and Cyrillic Е, È, Ё, Є and Э.
Medical image computingMedical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.
