Search Results for "recognition"

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865.

Highlight is a source code to formatted text converter. It generates HTML, XHTML, RTF, ODT, LaTeX, TeX, SVG, BBCode and terminal escape sequences with coloured syntax highlighting. Language definitions and colour themes are Lua scripts and support plugins

MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. This results in improved detection accuracy across various object sizes and categories compared to standard single-path architectures. The repository supports training, evaluation, and visualization for object detection tasks on popular datasets such as PASCAL VOC and MS COCO. ...

The software resents an executable specification language based on deterministic finite automata, this allows the user to perform tests of word recognition in the state machines that are defined by it. Moreover, it’s possible for the user to assign snippets of code to be executed every transition automaton, these snippets can be described in Lua or PHP programming language. To test that were conducted in the language, was developed a compiler that recognizes words and generates object code.

The thesis titled "On the Recognition of Emotion from Physiological Data" This thesis was submitted by Warren Creemers In Partial Fulfillment of the Requirements for the award of Doctor of Philosophy, (Computer Science) and is currently under examination. Warren Creemers completed this work at the Faculty of Communications, Health and Science - Edith Cowan University in February, 2013.