1 |
Computational aspects of particle image size and velocity measurementsSanaye, Sepehr January 1994 (has links)
No description available.
|
2 |
Weightless neural networks for face recognitionKhaki, Kazimali M. January 2013 (has links)
The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition. Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition.
|
3 |
Klasifikace obrazů planktonu s proměnlivou velikosti pomocí konvoluční neuronové sítě / Classification of Varying-Size Plankton Images with Convolutional Neural NetworkBureš, Jaroslav January 2020 (has links)
Tato práce pojednává o technikách automatické analýzy obrazu založené na konvolučních neuronových sítích (CNN), zaměřených na klasifikaci planktonu. V oblasti studování planktonu panuje velká diverzita v jeho tvarech a velikostech. Kvůli tomuto bývá klasifikace pomocí CNN náročná, jelikož CNN typicky požadují definovanou velikost vstupu. Běžné metody využívají škálování obrazu do jednotné velikosti. Avšak kvůli tomuto jsou ztraceny drobné detaily potřebné ke správné klasifikaci. Cílem práce bylo navrhnout a implementovat CNN klasifikátor obrazových dat planktonu a prozkoumat metody, které jsou zaměřené na problematiku různorodých velikostí obrázků. Metody, jako jsou patch cropping, využití spatial pyramid pooling vrstvy, zahrnutí metadat a sestavení multi-stream modelu jsou vyhodnoceny na náročném datasetu obrázků fytoplanktonu. Takto bylo dosaženo zlepšení o 1.0 bodů pro InceptionV3 architekturu s výslednou úspěšností 96.2 %. Hlavním přínosem této práce je vylepšení CNN klasifikátorů planktonu díky úspěšné aplikaci těchto metod.
|
4 |
The Effect of Cue and Target Similarity on Visual Search Response Times: Manipulation of Basic Stimulus CharacteristicsFullenkamp, Steven Charles January 2013 (has links)
No description available.
|
Page generated in 0.049 seconds