Abstract / truncated to 115 words (read the full abstract)

Content Based Image Retrieval (CBIR) has gained a lot of interest over the last two decades. The need to search and retrieve images from databases, based on information (“features”) extracted from the image itself, is becoming increasingly important. CBIR can be useful for handheld image recognition devices in which the image to be recognized is acquired with a camera, and thus there is no additional metadata associated to it. However, most CBIR systems require large computations, preventing their use in handheld devices. In this PhD work, we have developed low-complexity algorithms for content based image retrieval in handheld devices for camera acquired images. Two novel algorithms, ‘Color Density Circular Crop’ (CDCC) and ‘DCT-Phase Match’ (DCTPM), ... toggle 16 keywords

low-complexity – image recognition – content based image retrieval – alternative and augmentative communication – rst compensation – handheld image recognition device – color density circular crop – dct-phase match – two-stage image retrieval – fourier-mellin transform – hough transform – harris corner detector – perspective transform – dsp implementation – codec-engine – pictobar

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