7 resultados para Binary Image Representation

em University of Queensland eSpace - Australia


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In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

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Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

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In this paper, we present a new scheme for off-line recognition of multi-font numerals using the Takagi-Sugeno (TS) model. In this scheme, the binary image of a character is partitioned into a fixed number of sub-images called boxes. The features consist of normalized vector distances (gamma) from each box. Each feature extracted from different fonts gives rise to a fuzzy set. However, when we have a small number of fonts as in the case of multi-font numerals, the choice of a proper fuzzification function is crucial. Hence, we have devised a new fuzzification function involving parameters, which take account of the variations in the fuzzy sets. The new fuzzification function is employed in the TS model for the recognition of multi-font numerals.

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Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall

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Objectives. It has been proposed that disruption of the internal proprioceptive representation, via incongruent sensory input, may underpin pathological pain states, but experimental evidence relies on conflicting visual input, which is not clinically relevant. We aimed to determine the symptomatic effect of incongruent proprioceptive input, imparted by vibration of the wrist tendons, which evokes the illusion of perpetual wrist flexion and disrupts cortical proprioceptive representation. Methods. Twenty-nine healthy and naive volunteers reported symptoms during five conditions: control, active and passive wrist flexion, extensor carpi radialis tendon vibration to evoke illusion of perpetual wrist flexion, and ulnar styloid (sham) vibration. No advice was given about possible illusions. Results. Twenty-one subjects reported the illusion of perpetual wrist flexion during tendon vibration. There was no effect of condition or of whether or not subjects reported an illusion on discomfort/pain (P > 0.28). Peculiarity, swelling and foreignness were greater during tendon vibration than during the other conditions, and greater during tendon vibration in those who reported an illusion of wrist flexion than in those who did not (P < 0.05 for all). Symptoms were reported by at least two subjects in each condition and four subjects reported systemic symptoms (e.g. nausea). Conclusions. In healthy volunteers, incongruent proprioceptive input does not cause discomfort or pain but does evoke feelings of peculiarity, swelling and foreignness in the limb.