Search results for "Pattern recognition"
showing 10 items of 2301 documents
Tetrachromatic color vision in goldfish: evidence from color mixture experiments
1992
Additive color mixture experiments were performed in the goldfish using a behavioral training technique in which the fish had to discriminate between two test fields.
Honeybee (Apis mellifera) vision can discriminate between and recognise images of human faces.
2005
SUMMARY Recognising individuals using facial cues is an important ability. There is evidence that the mammalian brain may have specialised neural circuitry for face recognition tasks, although some recent work questions these findings. Thus, to understand if recognising human faces does require species-specific neural processing, it is important to know if non-human animals might be able to solve this difficult spatial task. Honeybees (Apis mellifera) were tested to evaluate whether an animal with no evolutionary history for discriminating between humanoid faces may be able to learn this task. Using differential conditioning, individual bees were trained to visit target face stimuli and to …
Phonological precision for word recognition in skilled readers
2019
According to the lexical quality hypothesis (Perfetti, 2007), differences in the orthographic, semantic, and phonological representations of words will affect individual reading performance. Whilst several studies have focused on orthographic precision and semantic coherence, few have considered phonological precision. The present study used a suite of individual difference measures to assess which components of lexical quality contributed to competition resolution in a masked priming experiment. The experiment measured form priming for word and pseudoword targets with dense and sparse neighbourhoods in 84 university students. Individual difference measures of language and cognitive skills …
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
2021
In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…
Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images
2020
The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…
Space variant vision and pipelined architecture for time to impact computation
2002
Image analysis is one of the most interesting ways for a mobile vehicle to understand its environment. One of the tasks of an autonomous vehicle is to get accurate information of what it has in front, to avoid collision or find a way to a target. This task requires real-time restrictions depending on the vehicle speed and external object movement. The use of normal cameras, with homogeneous (squared) pixel distribution, for real-time image processing, usually requires high performance computing and high image rates. A different approach makes use of a CMOS space-variant camera that yields a high frame rate with low data bandwidth. The camera also performs the log-polar transform, simplifyin…
Speeding-Up Differential Motion Detection Algorithms Using a Change-Driven Data Flow Processing Strategy
2007
A constraint of real-time implementation of differential motion detection algorithms is the large amount of data to be processed. Full image processing is usually the classical approach for these algorithms: spatial and temporal derivatives are calculated for all pixels in the image despite the fact that the majority of image pixels may not have changed from one frame to the next. By contrast, the data flow model works in a totally different way as instructions are only fired when the data needed for these instructions are available. Here we present a method to speed-up low level motion detection algorithms. This method is based on pixel change instead of full image processing and good spee…
Unsupervised deep feature extraction of hyperspectral images
2014
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms…
A support vector domain method for change detection in multitemporal images
2010
This paper formulates the problem of distinguishing changed from unchanged pixels in multitemporal remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere-shaped decision boundary with minimal volume that embraces changed pixels is approached in the context of the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. Unlike the standard SVDD, the proposed formulation of the SVDD uses both target and outlier samples for defi…