Author
Catherine W. Burns
Bio: Catherine W. Burns is an academic researcher from Syracuse University. The author has an hindex of 1, co-authored 1 publications receiving 371 citations.
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TL;DR: It is found that efficiency for letter identification is independent of duration, overall contrast, and eccentricity, and only weakly dependent on size, suggesting that letters are identified by a similar computation across this wide range of viewing conditions.
398 citations
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TL;DR: The goal of this review is to provide a broad, balanced and succinct review that organizes and summarizes the diverse and scattered studies of crowding, and helps to explain it to the non-specialist.
867 citations
TL;DR: It is proposed that a VA span deficit is a likely alternative underlying cognitive deficit in dyslexia, and this hypothesis was assessed in two large samples of French and British dyslexic children whose performance was compared to that of chronological-age matched control children.
651 citations
TL;DR: The explosion of studies on crowding is reviewed—in grating discrimination, letter and face recognition, visual search, selective attention, and reading—and a universal principle, the Bouma law is found, which is equal for all objects, although the effect is weaker between dissimilar objects.
Abstract: It is now emerging that vision is usually limited by object spacing rather than size. The visual system recognizes an object by detecting and then combining its features. ‘Crowding’ occurs when objects are too close together and features from several objects are combined into a jumbled percept. Here, we review the explosion of studies on crowding—in grating discrimination, letter and face recognition, visual search, selective attention, and reading—and find a universal principle, the Bouma law. The critical spacing required to prevent crowding is equal for all objects, although the effect is weaker between dissimilar objects. Furthermore, critical spacing at the cortex is independent of object position, and critical spacing at the visual field is proportional to object distance from fixation. The region where object spacing exceeds critical spacing is the ‘uncrowded window’. Observers cannot recognize objects outside of this window and its size limits the speed of reading and search. Object recognition means calling a chair a chair, despite variations in style, viewpoint, rendering and surrounding clutter. Crowding is a breakdown of object recognition. Let us begin by sketching a popular two-step model of object recognition: feature detection and combination. Features are components of images that are detected independently 1–4 . They are typically simple and nonoverlapping. The first step in object recognition is feature detection 4 . Each neuron in the primary visual cortex responds when a feature matches its receptive field. Only the features that drive neurons hard enough are detected 5 . In the second step, the brain combines some of the detected features to recognize the object. This combining step (including ‘integration’, ‘binding’, ‘segmentation’, ‘pooling’, ‘grouping’, ‘contour integration’ and ‘selective attention’) is still mysterious 3,4,6–11 .
602 citations
01 Jul 1976
TL;DR: Electrical and computer engineering ece courses ece 257a multiuser communication systems 4 congestion control convex programming and dual controller fair end end rate allocation max min fair vs proportional, electrical systems engineering washington university.
Abstract: electrical and computer engineering ece courses ece 257a multiuser communication systems 4 congestion control convex programming and dual controller fair end end rate allocation max min fair vs proportional, electrical systems engineering washington university arye nehorai eugene and martha lohman professor of electrical engineering phd stanford university signal processing imaging biomedicine communications, ieee transactions on aerospace and electronic systems ieee transactions on aerospace and electronic systems focuses on the organization design development integration and operation of complex systems for space air, department of electrical engineering and computer science h kumar wickramsinghe department chair 2213 engineering hall 949 824 4821 http www eng uci edu dept eecs overview electrical engineering and computer science is, download electrical and electronics engineering ebooks syst mes temps discret commande num rique des proc d s pdf 499 ko terminology and symbols in control engineering pdf 326 ko the best of thomas, publications stream wise list iit kanpur papers published in journals in 2016 dutta s patchaikani p k behera l near optimal controller for nonlinear continuous time systems with unknown dynamics, resolve a doi name type or paste a doi name into the text box click go your browser will take you to a web page url associated with that doi name send questions or comments to doi, peer reviewed journal ijera com international journal of engineering research and applications ijera is an open access online peer reviewed international journal that publishes research, dod sbir 2016 2 sbir gov note the solicitations and topics listed on this site are copies from the various sbir agency solicitations and are not necessarily the latest and most up, an english japanese dictionary of electrical engineering c 2952 9 691 c band c c contact c c maccs centre for mathematical modelling and computer simulation, the of and to a in that is was he for it with as his on be most common text click on the icon to return to www berro com and to enjoy and benefit the of and to a in that is was he for it with as his on be at by i this had
590 citations
TL;DR: Simulations of the spatial coding model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task.
Abstract: Visual word identification requires readers to code the identity and order of the letters in a word and match this code against previously learned codes. Current models of this lexical matching process posit context-specific letter codes in which letter representations are tied to either specific serial positions or specific local contexts (e.g., letter clusters). The spatial coding model described here adopts a different approach to letter position coding and lexical matching based on context-independent letter representations. In this model, letter position is coded dynamically, with a scheme called spatial coding. Lexical matching is achieved via a method called superposition matching, in which input codes and learned codes are matched on the basis of the relative positions of their common letters. Simulations of the model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task.
409 citations