Non-photorealistic rendering
Get flash to fully experience Pearltrees
Method of automatically producing ... - Google Patents
1. A method of generating a sketch image comprising: performing an adaptive luma chroma difference calculation to find and mark edges of objects in an input image as lines forming objects in the sketch image. 2. The method of claim 1, further comprising accepting the input image from a digital camera. 3.
1. A method of automatically processing an image comprising the steps of: locating, within the image, features having a high spatial variance by thresholding and skeletonising the image to produce an image comprising single pixel width definition of features;
Producing automatic “painting ... - Google Patents
1. A method for generating a digital finalized image having a hand-painted appearance on a digital canvas image using a computer system, the method comprising the steps of: (a) receiving a digital source image;
Method and system for generating an ... - Google Patents
Patents
The visual system provides us an enormous amount of information. For processing it in real time to be able to survive, humans (and other animals) have developed an attention system that allows them to filter out non important portions of the scene by just focusing on the most salient parts of what is being observed. I wrote a paper about using saliency as a new feature for object detection obtaining good results. It was accepted for publication in the Image and Vision Computing ( IMAVIS ) journal. You can download the manuscript from here .
Saliency – Sebastian Montabone
The iLab Neuromorphic Vision C++ Toolkit (iNVT, pronounced ``invent'') is a comprehensive set of C++ classes for the development of neuromorphic models of vision. Neuromorphic models are computational neuroscience algorithms whose architecture and function is closely inspired from biological brains. The iLab Neuromorphic Vision C++ Toolkit comprises not only base classes for images, neurons, and brain areas, but also fully-developed models such as our model of bottom-up visual attention and of Bayesian surprise . Features at a glance:
iLab Neuromorphic Vision C++ Toolkit (iNVT)
Kadir–Brady saliency detector
The Kadir–Brady saliency detector extracts features of objects in images that are distinct and representative. It was invented by Timor Kadir and Michael Brady [ 1 ] in 2001 and an affine invariant version was introduced by Kadir and Brady in 2004, [ 2 ] [ 3 ] and a robust version was designed by Shao et al. [ 4 ] in 2007. The detector uses the algorithms to more efficiently remove background noise and so more easily identify features which can be used in a 3D model. As the detector scans images it uses the three basics of global transformation, local perturbations and intra-class variations to define the areas of search, and identifies unique regions of those images rather than using the more traditional corner or blob searches.
MATLAB Saliency
Below is MATLAB code which computes a salience/saliency map for an image or image sequence/video (either Graph-Based Visual Saliency (GBVS) or the standard Itti, Koch, Niebur PAMI 1998 saliency map ). See the included readme file for details. I also have a newer, simpler version implementing only the Itti algorithm (see simpsal/readme.txt ).
Textures
Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) Objective The OpenCV library provides us a greatly interesting demonstration for a face detection. Furthermore, it provides us programs (or functions) that they used to train classifiers for their face detection system, called HaarTraining, so that we can create our own object classifiers using these functions. It is interesting. However, I could not follow how OpenCV developers performed the haartraining for their face detection system exactly because they did not provide us several information such as what images and parameters they used for training.
Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) - Naotoshi Seo
Color palettes are an important tool for color image analysis, since they are the initial point of different techniques such as quantization or indexing. This paper presents a new method for the automatic construction of a color palette, which adjusts dynamically its number of colors according to the visual content of the image. The method is based on appropriately segmenting the HSI color space, which is achieved by individually partitioning the histograms associated to each color component. As a result we obtain a hierarchical color palette, which represents the color image with a reduced number of colors. <p style="text-align:right;color:#A8A8A8"></p>
Automatic color palette
[Abstract] Color Image Segmentation using Optimal Separators of a Histogram
Browse Journals Browse Proceedings Color Image Segmentation using Optimal Separators of a Histogram J. Delon, A.
The Indexed color mode is used mainly in order to lower the number of colors and thus the need for memory space. When the RGB mode is used to describe pixel values, there are totally 16,777,216 colors for each color image; however, an ordinary color image usually does not need so many colors. Generally, 256 colors are enough for common color images; that is, it is usually more than adequate to select 256 representative colors according to the content of the image. However, some images can be so simple in color structure that not so many as 256 colors (e.g. 128 colors, 64 colors) are necessary. In this paper, we shall propose a new scheme that incorporates both CIQBM and Partial LBG.
Abstract
New: The BSDS500, an extended version of the BSDS300 that includes 200 fresh test images, is now available here . The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection . To this end, we have collected 12,000 hand-labeled segmentations of 1,000 Corel dataset images from 30 human subjects.
The Berkeley Segmentation Dataset and Benchmark
PeronaMalikFilter [ image , t , k , ] applies a Gaussian regularization of width to the image gradient in the conductance function. Details Details
PeronaMalikFilter
BibTeX @ARTICLE{Leung01representingand, author = {Thomas Leung and Jitendra Malik}, title = {Representing and Recognizing the Visual . . .}, journal = {INTERNATIONAL JOURNAL OF COMPUTER VISION}, year = {2001}, volume = {43}, number = {1}, pages = {29--44} } Bookmark
Representing and Recognizing the Visual . . .
Package Index > ImageContour > 0.1.2 Not Logged In ImageContour 0.1.2 Download ImageContour-0.1.2.tar.gz