4,081 research outputs found

    Seeing the invisible: Evolution of wing interference patterns in Hymenoptera, and their application in taxonomy

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    The remarkably thin transparent wing membranes in tiny wasps may appear to have a simple structural design, but hide a largely unexplored complex of micro-morphological features that serve aerodynamics and may also function in visual signaling. I found that when such small transparent wings are viewed against a dark background they display vivid structural color patterns due to thin film interference, and named them Wing Interference Patterns (WIPs). Areas of different thickness across the wing membrane reflect specific interference colors and all together produce a specific color pattern, offering a new way to map the wing micro-morphology through direct observations. The color sequence is very characteristic and lacks pure red but may contain UV light. Hence, it fits the UV-blue-green trichromatic color vision of most small insects, strongly suggesting that the biological significance of WIPs lies in visual signaling. WIPs are optically stabilized by corrugations in the wing membrane and are essentially noniridescent over a large range of light incidences. These patterns show a high diversity in small Hymenoptera and are often species-specific, which makes this new morphological character useful in taxonomy. Several sympatric species of parasitic wasps were found to display sexually dimorphic WIPs, suggesting sexual selection as one of the driving forces for their evolution. The significance of wing membrane micro-morphology and the origin of the specific color sequence observed in WIPs are discussed, using Achrysocharoides and Omphale as model taxa. Several new findings are reported in addition to those in my five publications. A comprehensive study of the wing cuticle ultra-structure, based on analyses of wing membrane cross-sections by transmission electron microscopy, revealed asymmetrical organization of the dorsal and ventral cuticles. Presence of ultraviolet light reflections in WIPs is indirectly demonstrated through fluorescence microscopy, further strengthening the signaling function of WIPs

    Cross Pixel Optical Flow Similarity for Self-Supervised Learning

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    We propose a novel method for learning convolutional neural image representations without manual supervision. We use motion cues in the form of optical flow, to supervise representations of static images. The obvious approach of training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose a much simpler learning goal: embed pixels such that the similarity between their embeddings matches that between their optical flow vectors. At test time, the learned deep network can be used without access to video or flow information and transferred to tasks such as image classification, detection, and segmentation. Our method, which significantly simplifies previous attempts at using motion for self-supervision, achieves state-of-the-art results in self-supervision using motion cues, competitive results for self-supervision in general, and is overall state of the art in self-supervised pretraining for semantic image segmentation, as demonstrated on standard benchmarks

    OPTYCZNE ROZPOZNAWANIE ZNAKÓW Z UŻYCIEM SZTUCZNEJ INTELIGENCJI

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    The article represents results of the research of an Optical Character Recognition system. Proposed OCR system is able to convert a raster image into the text string, which represents the text shown on the input image. The main innovation is the fact that the system was created without following any strict rules. It was more an innovative research rather than simple programming using ready guidelines.Celem projektu opisywanego w artykule było przygotowanie działającego systemu do optycznego rozpoznawania znaków, tj. zdolnego przekształcić rastrowy obraz wejściowy w łańcuch znaków odpowiadający zapisanemu tekstowi na obrazie. Nowością jest m.in. fakt wykonania tego systemu bez podążania za z góry znaną architekturą aplikacji, a przygotowanie go w sposób bardziej doświadczalny, czyli wykorzystując podejście nowatorskie

    Recognition Design of License Plate and Car Type Using Tesseract Ocr and Emgucv

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    The goal of the research is to design and implement software that can recognize license plates and car types from images. The method used for the research is soft computing using library of EmguCV. There are four phases in creating the software, i.e., input image process, pre-processing, training processing and recognition. Firstly, user enters the car image. Then, the program reads and does pre-processing the image from bitmap form into vector. The next process is training process, which is learning phase in order the system to be able recognize an object (in this case license plate and car type), and in the end is the recognition process itself. The result is data about the car types and the license plates that have been entered. Using simulation, this software successfully recognized license plate by 80.223% accurate and car type 75% accurate
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